Neural network (machine learning)

4816: 5033: 5021: 1130: 4408: 3132: 2254: 2059: 1285:. R. D. Joseph (1960) mentions an even earlier perceptron-like device by Farley and Clark: "Farley and Clark of MIT Lincoln Laboratory actually preceded Rosenblatt in the development of a perceptron-like device." However, "they dropped the subject." The perceptron raised public excitement for research in Artificial Neural Networks, causing the US government to drastically increase funding. This contributed to "the Golden Age of AI" fueled by the optimistic claims made by computer scientists regarding the ability of perceptrons to emulate human intelligence. 1154: 7769: 5009: 4997: 3016:(CAA). It is a system with only one input, situation s, and only one output, action (or behavior) a. It has neither external advice input nor external reinforcement input from the environment. The CAA computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about encountered situations. The system is driven by the interaction between cognition and emotion. Given the memory matrix, W =||w(a,s)||, the crossbar self-learning algorithm in each iteration performs the following computation: 5178:. In the marketing industry generative models are used to create personalized advertisements for consumers. Additionally, major film companies are partnering with technology companies to analyze the financial success of a film, such as the partnership between Warner Bros and technology company Cinelytic established in 2020. Furthermore, neural networks have found uses in video game creation, where Non Player Characters (NPCs) can make decisions based on all the characters currently in the game. 1321:(1965). They regarded it as a form of polynomial regression, or a generalization of Rosenblatt's perceptron. A 1971 paper described a deep network with eight layers trained by this method, which is based on layer by layer training through regression analysis. Superfluous hidden units are pruned using a separate validation set. Since the activation functions of the nodes are Kolmogorov-Gabor polynomials, these were also the first deep networks with multiplicative units or "gates." 4985: 40: 5045: 17224: 16570: 4646:. Dean Pomerleau uses a neural network to train a robotic vehicle to drive on multiple types of roads (single lane, multi-lane, dirt, etc.), and a large amount of his research is devoted to extrapolating multiple training scenarios from a single training experience, and preserving past training diversity so that the system does not become overtrained (if, for example, it is presented with a series of right turns—it should not learn to always turn right). 4815: 16550: 4494: 5368: 4490:. This concept emerges in a probabilistic (Bayesian) framework, where regularization can be performed by selecting a larger prior probability over simpler models; but also in statistical learning theory, where the goal is to minimize over two quantities: the 'empirical risk' and the 'structural risk', which roughly corresponds to the error over the training set and the predicted error in unseen data due to overfitting. 4357:

information capacity of a perceptron is intensively discussed in Sir David MacKay's book which summarizes work by Thomas Cover. The capacity of a network of standard neurons (not convolutional) can be derived by four rules that derive from understanding a neuron as an electrical element. The information capacity captures the functions modelable by the network given any data as input. The second notion, is the

17236: 4278:, with the objective to discriminate between legitimate activities and malicious ones. For example, machine learning has been used for classifying Android malware, for identifying domains belonging to threat actors and for detecting URLs posing a security risk. Research is underway on ANN systems designed for penetration testing, for detecting botnets, credit cards frauds and network intrusions. 1844: 3220:. Dynamic types allow one or more of these to evolve via learning. The latter is much more complicated but can shorten learning periods and produce better results. Some types allow/require learning to be "supervised" by the operator, while others operate independently. Some types operate purely in hardware, while others are purely software and run on general purpose computers. 16782: 3201:

reduces the chance of the network getting stuck in local minima. However, batch learning typically yields a faster, more stable descent to a local minimum, since each update is performed in the direction of the batch's average error. A common compromise is to use "mini-batches", small batches with samples in each batch selected stochastically from the entire data set.

4787:

scenarios where the training data may be imbalanced due to the scarcity of data for a specific race, gender or other attribute. This imbalance can result in the model having inadequate representation and understanding of underrepresented groups, leading to discriminatory outcomes that exasperate societal inequalities, especially in applications like

4293:, the dynamics of neural circuitry arise from interactions between individual neurons and how behavior can arise from abstract neural modules that represent complete subsystems. Studies considered long-and short-term plasticity of neural systems and their relation to learning and memory from the individual neuron to the system level. 4390:. Another example is when parameters are small, it is observed that ANNs often fits target functions from low to high frequencies. This behavior is referred to as the spectral bias, or frequency principle, of neural networks. This phenomenon is the opposite to the behavior of some well studied iterative numerical schemes such as 3278:(NAS) uses machine learning to automate ANN design. Various approaches to NAS have designed networks that compare well with hand-designed systems. The basic search algorithm is to propose a candidate model, evaluate it against a dataset, and use the results as feedback to teach the NAS network. Available systems include 5032: 5148:

application in personalized medicine and healthcare data analysis allows tailored therapies and efficient patient care management. Ongoing research is aimed at addressing remaining challenges such as data privacy and model interpretability, as well as expanding the scope of ANN applications in medicine.

1159:

Subsequent run of the network on an input image (left): The network correctly detects the starfish. However, the weakly weighted association between ringed texture and sea urchin also confers a weak signal to the latter from one of two intermediate nodes. In addition, a shell that was not included in

4698:

mechanisms, for visualizing and explaining learned neural networks. Furthermore, researchers involved in exploring learning algorithms for neural networks are gradually uncovering generic principles that allow a learning machine to be successful. For example, Bengio and LeCun (2007) wrote an article

4365:

and finds the maximum capacity under the best possible circumstances. This is, given input data in a specific form. As noted in, the VC Dimension for arbitrary inputs is half the information capacity of a Perceptron. The VC Dimension for arbitrary points is sometimes referred to as Memory Capacity.

4299:

Beyond their traditional applications, artificial neural networks are increasingly being utilized in interdisciplinary research, such as materials science. For instance, graph neural networks (GNNs) have demonstrated their capability in scaling deep learning for the discovery of new stable materials

5060:

Artificial neural networks (ANNs) have undergone significant advancements, particularly in their ability to model complex systems, handle large data sets, and adapt to various types of applications. Their evolution over the past few decades has been marked by a broad range of applications in fields

5078:

By modeling speech signals, ANNs are used for tasks like speaker identification and speech-to-text conversion. Deep neural network architectures have introduced significant improvements in large vocabulary continuous speech recognition, outperforming traditional techniques. These advancements have

4374:

Models may not consistently converge on a single solution, firstly because local minima may exist, depending on the cost function and the model. Secondly, the optimization method used might not guarantee to converge when it begins far from any local minimum. Thirdly, for sufficiently large data or

4356:

A model's "capacity" property corresponds to its ability to model any given function. It is related to the amount of information that can be stored in the network and to the notion of complexity. Two notions of capacity are known by the community. The information capacity and the VC Dimension. The

2146:

whose value can only be approximated. The outputs are actually numbers, so when the error is low, the difference between the output (almost certainly a cat) and the correct answer (cat) is small. Learning attempts to reduce the total of the differences across the observations. Most learning models

1931:

like a biological axon-synapse-dendrite connection. All the nodes connected by links take in some data and use it to perform specific operations and tasks on the data. Each link has a weight, determining the strength of one node's influence on another, allowing weights to choose the signal between

1911:

ANNs began as an attempt to exploit the architecture of the human brain to perform tasks that conventional algorithms had little success with. They soon reoriented towards improving empirical results, abandoning attempts to remain true to their biological precursors. ANNs have the ability to learn

4642:

solutions include randomly shuffling training examples, by using a numerical optimization algorithm that does not take too large steps when changing the network connections following an example, grouping examples in so-called mini-batches and/or introducing a recursive least squares algorithm for

4641:

A common criticism of neural networks, particularly in robotics, is that they require too many training samples for real-world operation. Any learning machine needs sufficient representative examples in order to capture the underlying structure that allows it to generalize to new cases. Potential

3022:

The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is behavioral environment where it behaves, and the other is genetic environment, where from it initially and only once receives initial emotions about to be

2182:

that increases or decreases as appropriate. The concept of momentum allows the balance between the gradient and the previous change to be weighted such that the weight adjustment depends to some degree on the previous change. A momentum close to 0 emphasizes the gradient, while a value close to 1

5087:

In natural language processing, ANNs are used for tasks such as text classification, sentiment analysis, and machine translation. They have enabled the development of models that can accurately translate between languages, understand the context and sentiment in textual data, and categorize text

4668:

columnist, commented that as a result, artificial neural networks have a "something-for-nothing quality, one that imparts a peculiar aura of laziness and a distinct lack of curiosity about just how good these computing systems are. No human hand (or mind) intervenes; solutions are found as if by

4472:

Applications whose goal is to create a system that generalizes well to unseen examples, face the possibility of over-training. This arises in convoluted or over-specified systems when the network capacity significantly exceeds the needed free parameters. Two approaches address over-training. The

3200:

and batch. In stochastic learning, each input creates a weight adjustment. In batch learning weights are adjusted based on a batch of inputs, accumulating errors over the batch. Stochastic learning introduces "noise" into the process, using the local gradient calculated from one data point; this

4786:

Neural networks are dependent on the quality of the data they are trained on, thus low quality data with imbalanced representativeness can lead to the model learning and perpetuating societal biases. These inherited biases become especially critical when the ANNs are integrated into real-world

2137:

Learning is the adaptation of the network to better handle a task by considering sample observations. Learning involves adjusting the weights (and optional thresholds) of the network to improve the accuracy of the result. This is done by minimizing the observed errors. Learning is complete when

4765:

Analyzing what has been learned by an ANN is much easier than analyzing what has been learned by a biological neural network. Furthermore, researchers involved in exploring learning algorithms for neural networks are gradually uncovering general principles that allow a learning machine to be

4680:

Neural networks, for instance, are in the dock not only because they have been hyped to high heaven, (what hasn't?) but also because you could create a successful net without understanding how it worked: the bunch of numbers that captures its behaviour would in all probability be "an opaque,

5147:

for early disease detection, and by predicting patient outcomes for personalized treatment planning. In drug discovery, ANNs speed up the identification of potential drug candidates and predict their efficacy and safety, significantly reducing development time and costs. Additionally, their

2169:

The learning rate defines the size of the corrective steps that the model takes to adjust for errors in each observation. A high learning rate shortens the training time, but with lower ultimate accuracy, while a lower learning rate takes longer, but with the potential for greater accuracy.

5069:

In the realm of image processing, ANNs are employed in tasks such as image classification, object recognition, and image segmentation. For instance, deep convolutional neural networks (CNNs) have been important in handwritten digit recognition, achieving state-of-the-art performance. This

4257:

ANNs have been used to accelerate reliability analysis of infrastructures subject to natural disasters and to predict foundation settlements. It can also be useful to mitigate flood by the use of ANNs for modelling rainfall-runoff. ANNs have also been used for building black-box models in

4300:

by efficiently predicting the total energy of crystals. This application underscores the adaptability and potential of ANNs in tackling complex problems beyond the realms of predictive modeling and artificial intelligence, opening new pathways for scientific discovery and innovation.

5169:

is a deep neural network trained on 650 million pairs of images and texts across the internet that can create artworks based on text entered by the user. In the field of music, transformers are used to create original music for commercials and documentaries through companies such as

4684:

In spite of his emphatic declaration that science is not technology, Dewdney seems here to pillory neural nets as bad science when most of those devising them are just trying to be good engineers. An unreadable table that a useful machine could read would still be well worth having.

5020: 4702:

Biological brains use both shallow and deep circuits as reported by brain anatomy, displaying a wide variety of invariance. Weng argued that the brain self-wires largely according to signal statistics and therefore, a serial cascade cannot catch all major statistical dependencies.

4799:

had to scrap a recruiting tool because the model favored men over women for jobs in software engineering due to the higher number of male workers in the field. The program would penalize any resume with the word "woman" or the name of any women's college. However, the use of

4528:, on the output layer of the neural network (or a softmax component in a component-based network) for categorical target variables, the outputs can be interpreted as posterior probabilities. This is useful in classification as it gives a certainty measure on classifications. 1948:. Each artificial neuron has inputs and produces a single output which can be sent to multiple other neurons. The inputs can be the feature values of a sample of external data, such as images or documents, or they can be the outputs of other neurons. The outputs of the final 4385:

The convergence behavior of certain types of ANN architectures are more understood than others. When the width of network approaches to infinity, the ANN is well described by its first order Taylor expansion throughout training, and so inherits the convergence behavior of

1537:

considered "reverberating circuit" as an explanation for short-term memory. The McCulloch and Pitts paper (1943) considered neural networks that contains cycles, and noted that the current activity of such networks can be affected by activity indefinitely far in the past.

1904: 4669:

magic; and no one, it seems, has learned anything". One response to Dewdney is that neural networks have been successfully used to handle many complex and diverse tasks, ranging from autonomously flying aircraft to detecting credit card fraud to mastering the game of

2138:

examining additional observations does not usefully reduce the error rate. Even after learning, the error rate typically does not reach 0. If after learning, the error rate is too high, the network typically must be redesigned. Practically this is done by defining a

3023:

encountered situations in the behavioral environment. Having received the genome vector (species vector) from the genetic environment, the CAA will learn a goal-seeking behavior, in the behavioral environment that contains both desirable and undesirable situations.

1197:(FNN) is a linear network, which consists of a single layer of output nodes with linear activation functions; the inputs are fed directly to the outputs via a series of weights. The sum of the products of the weights and the inputs is calculated at each node. The 3254:

Choice of model: This depends on the data representation and the application. Model parameters include the number, type, and connectedness of network layers, as well as the size of each and the connection type (full, pooling, etc. ). Overly complex models learn

5096:

In the domain of control systems, ANNs are used to model dynamic systems for tasks such as system identification, control design, and optimization. For instance, deep feedforward neural networks are important in system identification and control applications.

2659:

cost, according to some (usually unknown) rules. The rules and the long-term cost usually only can be estimated. At any juncture, the agent decides whether to explore new actions to uncover their costs or to exploit prior learning to proceed more quickly.

2650:

In applications such as playing video games, an actor takes a string of actions, receiving a generally unpredictable response from the environment after each one. The goal is to win the game, i.e., generate the most positive (lowest cost) responses. In

1239:(1943) considered a non-learning computational model for neural networks. This model paved the way for research to split into two approaches. One approach focused on biological processes while the other focused on the application of neural networks to 5130:

ANNs require high-quality data and careful tuning, and their "black-box" nature can pose challenges in interpretation. Nevertheless, ongoing advancements suggest that ANNs continue to play a role in finance, offering valuable insights and enhancing

1224:

operate via the execution of explicit instructions with access to memory by a number of processors. Some neural networks, on the other hand, originated from efforts to model information processing in biological systems through the framework of

5164:

are used for content creation across numerous industries. This is because deep learning models are able to learn the style of an artist or musician from huge datasets and generate completely new artworks and music compositions. For instance,

9720:

Bozinovski, S. (1982). "A self-learning system using secondary reinforcement". In R. Trappl (ed.) Cybernetics and Systems Research: Proceedings of the Sixth European Meeting on Cybernetics and Systems Research. North Holland. pp. 397–402.

1292:

with an adaptive hidden layer. Rosenblatt (1962) cited and adopted these ideas, also crediting work by H. D. Block and B. W. Knight. Unfortunately, these early efforts did not lead to a working learning algorithm for hidden units, i.e.,

1999:. Single layer and unlayered networks are also used. Between two layers, multiple connection patterns are possible. They can be 'fully connected', with every neuron in one layer connecting to every neuron in the next layer. They can be 1147:. The starfish match with a ringed texture and a star outline, whereas most sea urchins match with a striped texture and oval shape. However, the instance of a ring textured sea urchin creates a weakly weighted association between them. 3215:

ANNs have evolved into a broad family of techniques that have advanced the state of the art across multiple domains. The simplest types have one or more static components, including number of units, number of layers, unit weights and

4689:

Although it is true that analyzing what has been learned by an artificial neural network is difficult, it is much easier to do so than to analyze what has been learned by a biological neural network. Moreover, recent emphasis on the

1109:. This method is based on the idea of optimizing the network's parameters to minimize the difference, or empirical risk, between the predicted output and the actual target values in a given dataset. Gradient-based methods such as 2655:, the aim is to weight the network (devise a policy) to perform actions that minimize long-term (expected cumulative) cost. At each point in time the agent performs an action and the environment generates an observation and an 1591:(LSTM), which set accuracy records in multiple applications domains. This was not yet the modern version of LSTM, which required the forget gate, which was introduced in 1999. It became the default choice for RNN architecture. 1756:(2018) based on the Progressive GAN by Tero Karras et al. Here the GAN generator is grown from small to large scale in a pyramidal fashion. Image generation by GAN reached popular success, and provoked discussions concerning 2341:

uses a set of paired inputs and desired outputs. The learning task is to produce the desired output for each input. In this case, the cost function is related to eliminating incorrect deductions. A commonly used cost is the

7146:

Bohr, Henrik, Jakob Bohr, Søren Brunak, Rodney MJ Cotterill, Benny Lautrup, Leif Nørskov, Ole H. Olsen, and Steffen B. Petersen. "Protein secondary structure and homology by neural networks The α-helices in rhodopsin."

4505:(MSE) cost function can use formal statistical methods to determine the confidence of the trained model. The MSE on a validation set can be used as an estimate for variance. This value can then be used to calculate the 2217:

Backpropagation is a method used to adjust the connection weights to compensate for each error found during learning. The error amount is effectively divided among the connections. Technically, backprop calculates the

2045:, the number of hidden layers and batch size. The values of some hyperparameters can be dependent on those of other hyperparameters. For example, the size of some layers can depend on the overall number of layers. 12130: 4977: 9815:

Such FP, Madhavan V, Conti E, Lehman J, Stanley KO, Clune J (20 April 2018). "Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning".

4623: 3300:

must also be defined as part of the design (they are not learned), governing matters such as how many neurons are in each layer, learning rate, step, stride, depth, receptive field and padding (for CNNs), etc.

4752:

addresses the hardware difficulty directly, by constructing non-von-Neumann chips to directly implement neural networks in circuitry. Another type of chip optimized for neural network processing is called a

5403:- Illustrated, bilingual manuscript about artificial neural networks; Topics so far: Perceptrons, Backpropagation, Radial Basis Functions, Recurrent Neural Networks, Self Organizing Maps, Hopfield Networks. 1912:

and model non-linearities and complex relationships. This is achieved by neurons being connected in various patterns, allowing the output of some neurons to become the input of others. The network forms a

5358: 12492: 2609:

of the model given the data (note that in both of those examples, those quantities would be maximized rather than minimized). Tasks that fall within the paradigm of unsupervised learning are in general

6589:

The Early Mathematical Manuscripts of Leibniz: Translated from the Latin Texts Published by Carl Immanuel Gerhardt with Critical and Historical Notes (Leibniz published the chain rule in a 1676 memoir)

5038:

A two-layer feedforward artificial neural network with 8 inputs, 2x8 hidden nodes and 2 outputs. Given position state, direction and other environment values, it outputs thruster based control values.

11062: 4738:), has increased around a million-fold, making the standard backpropagation algorithm feasible for training networks that are several layers deeper than before. The use of accelerators such as 8009: 2967: 2789: 2727: 1650:

achieved for the first time superhuman performance in a visual pattern recognition contest, outperforming traditional methods by a factor of 3. It then won more contests. They also showed how

4730:

Some argue that the resurgence of neural networks in the twenty-first century is largely attributable to advances in hardware: from 1991 to 2015, computing power, especially as delivered by

1370:(1969), who emphasized that basic perceptrons were incapable of processing the exclusive-or circuit. This insight was irrelevant for the deep networks of Ivakhnenko (1965) and Amari (1967). 17274: 5123:

In investing, ANNs can process vast amounts of financial data, recognize complex patterns, and forecast stock market trends, aiding investors and risk managers in making informed decisions.

1458:

to apply CNN to phoneme recognition. It used convolutions, weight sharing, and backpropagation. In 1988, Wei Zhang applied a backpropagation-trained CNN to alphabet recognition. In 1989,

9537: 5070:

demonstrates the ability of ANNs to effectively process and interpret complex visual information, leading to advancements in fields ranging from automated surveillance to medical imaging.

1810:'s fast weight controller (1992) scales linearly and was later shown to be equivalent to the unnormalized linear Transformer. Transformers have increasingly become the model of choice for 1404:

in 1673 to networks of differentiable nodes. The terminology "back-propagating errors" was actually introduced in 1962 by Rosenblatt, but he did not know how to implement this, although

5356: 2895: 2842: 2003:, where a group of neurons in one layer connects to a single neuron in the next layer, thereby reducing the number of neurons in that layer. Neurons with only such connections form a 12018:

Homayoun S, Ahmadzadeh M, Hashemi S, Dehghantanha A, Khayami R (2018), Dehghantanha A, Conti M, Dargahi T (eds.), "BoTShark: A Deep Learning Approach for Botnet Traffic Detection",

12100: 10552:, Hubert T, Schrittwieser J, Antonoglou I, Lai M, Guez A, et al. (5 December 2017). "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm". 3019:

In situation s perform action a; Receive consequence situation s'; Compute emotion of being in consequence situation v(s'); Update crossbar memory w'(a,s) = w(a,s) + v(s').

1700:

created a network that learned to recognize higher-level concepts, such as cats, only from watching unlabeled images. Unsupervised pre-training and increased computing power from

13763:

Kortylewski A, Egger B, Schneider A, Gerig T, Morel-Forster A, Vetter T (June 2019). "Analyzing and Reducing the Damage of Dataset Bias to Face Recognition with Synthetic Data".

9253: 4848: 3308:

code snippet provides an overview of the training function, which uses the training dataset, number of hidden layer units, learning rate, and number of iterations as parameters:

1201:

between these calculated outputs and the given target values are minimized by creating an adjustment to the weights. This technique has been known for over two centuries as the

4654:

A central claim of ANNs is that they embody new and powerful general principles for processing information. These principles are ill-defined. It is often claimed that they are

2433: 1975:

to produce the output. The initial inputs are external data, such as images and documents. The ultimate outputs accomplish the task, such as recognizing an object in an image.

4723:

and storage. Furthermore, the designer often needs to transmit signals through many of these connections and their associated neurons – which require enormous

3234:

and can handle signals that have a mix of low and high frequency components aiding large-vocabulary speech recognition, text-to-speech synthesis, and photo-real talking heads;

2603: 13392: 2520: 9340: 9301: 5026:

A single-layer feedforward artificial neural network with 4 inputs, 6 hidden nodes and 2 outputs. Given position state and direction, it outputs wheel based control values.

4658:

from the network itself. This allows simple statistical association (the basic function of artificial neural networks) to be described as learning or recognition. In 1997,

10524: 3241:

in which multiple networks (of varying structure) compete with each other, on tasks such as winning a game or on deceiving the opponent about the authenticity of an input.

2572: 2542: 2455: 2390: 12881:

Lee J, Xiao L, Schoenholz SS, Bahri Y, Novak R, Sohl-Dickstein J, et al. (2020). "Wide neural networks of any depth evolve as linear models under gradient descent".

9645:

Damas, M., Salmeron, M., Diaz, A., Ortega, J., Prieto, A., Olivares, G. (2000). "Genetic algorithms and neuro-dynamic programming: application to water supply networks".

5126:

In credit scoring, ANNs offer data-driven, personalized assessments of creditworthiness, improving the accuracy of default predictions and automating the lending process.

4254:

ANNs have been used to diagnose several types of cancers and to distinguish highly invasive cancer cell lines from less invasive lines using only cell shape information.

16444: 12122: 5357: 17864: 12944: 11382: 10487: 10359: 9845: 17267: 5044: 906: 4875: 2358:). This can be thought of as learning with a "teacher", in the form of a function that provides continuous feedback on the quality of solutions obtained thus far. 1049:

in the brain. Each artificial neuron receives signals from connected neurons, then processes them and sends a signal to other connected neurons. The "signal" is a

944: 8138: 7863: 7846: 3000:

grid density for numerically approximating the solution of control problems. Tasks that fall within the paradigm of reinforcement learning are control problems,

11842:

Peres DJ, Iuppa C, Cavallaro L, Cancelliere A, Foti E (1 October 2015). "Significant wave height record extension by neural networks and reanalysis wind data".

11207:

Roman M. Balabin, Ekaterina I. Lomakina (2009). "Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies".

9981: 4711:

Large and effective neural networks require considerable computing resources. While the brain has hardware tailored to the task of processing signals through a

1068:

Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the first layer (the

4731: 3044:. It is competitive with sophisticated gradient descent approaches. One advantage of neuroevolution is that it may be less prone to get caught in "dead ends". 13741: 11766: 7444: 4093:

Because of their ability to reproduce and model nonlinear processes, artificial neural networks have found applications in many disciplines. These include:

17260: 8314:(2020). "Generative Adversarial Networks are Special Cases of Artificial Curiosity (1990) and also Closely Related to Predictability Minimization (1991)". 1987:. Neurons of one layer connect only to neurons of the immediately preceding and immediately following layers. The layer that receives external data is the 901: 1560:

proposed the "neural sequence chunker" or "neural history compressor" which introduced the important concepts of self-supervised pre-training (the "P" in

11050: 5548: 891: 10013: 5106: 15692: 10079:"A Novel Nonparametric Regression Ensemble for Rainfall Forecasting Using Particle Swarm Optimization Technique Coupled with Artificial Neural Network" 9604:. MODSIM 2001, International Congress on Modelling and Simulation. Canberra, Australia: Modelling and Simulation Society of Australia and New Zealand. 7655: 5929:

Rochester N, J.H. Holland, L.H. Habit, W.L. Duda (1956). "Tests on a cell assembly theory of the action of the brain, using a large digital computer".

11890: 11341: 8000: 10900:

2013 International Conference Oriental COCOSDA held jointly with 2013 Conference on Asian Spoken Language Research and Evaluation (O-COCOSDA/CASLRE)

5509: 2142:

that is evaluated periodically during learning. As long as its output continues to decline, learning continues. The cost is frequently defined as a

16286: 14732: 13071:

Zhi-Qin John Xu, Yaoyu Zhang, Tao Luo, Yanyang Xiao, Zheng Ma (2020). "Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks".

10243:

LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, et al. (1989). "Backpropagation Applied to Handwritten Zip Code Recognition".

10218: 10161: 10039: 6485:

Fukushima K (1980). "Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position".

5423: 3265:

for training on a particular data set. However, selecting and tuning an algorithm for training on unseen data requires significant experimentation.

1630:

Between 2009 and 2012, ANNs began winning prizes in image recognition contests, approaching human level performance on various tasks, initially in

1160:

the training gives a weak signal for the oval shape, also resulting in a weak signal for the sea urchin output. These weak signals may result in a

732: 13311: 10383:

Li X, Wu X (15 October 2014). "Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition".

9521: 4325:. However, the proof is not constructive regarding the number of neurons required, the network topology, the weights and the learning parameters. 2346:, which tries to minimize the average squared error between the network's output and the desired output. Tasks suited for supervised learning are 17783: 15280: 14216: 9102:

Li Y, Fu Y, Li H, Zhang SW (1 June 2009). "The Improved Training Algorithm of Back Propagation Neural Network with Self-adaptive Learning Rate".

5449: 1775:

In 2014, the state of the art was training “very deep neural network” with 20 to 30 layers. Stacking too many layers led to a steep reduction in

10758: 8657: 11539: 9794:

Salimans T, Ho J, Chen X, Sidor S, Sutskever I (7 September 2017). "Evolution Strategies as a Scalable Alternative to Reinforcement Learning".

5409: 4437: 3161: 2283: 2088: 939: 8418: 8268: 2041:

whose value is set before the learning process begins. The values of parameters are derived via learning. Examples of hyperparameters include

13799: 10003:

Ferreira C (2006). "Designing Neural Networks Using Gene Expression Programming". In A. Abraham, B. de Baets, M. Köppen, B. Nickolay (eds.).

9379: 2544:

that is equal to the mean of the data. The cost function can be much more complicated. Its form depends on the application: for example, in

1728:

et al., 2014) became state of the art in generative modeling during 2014-2018 period. The GAN principle was originally published in 1991 by

17773: 17144: 14209: 8034:

Ciresan D, Giusti A, Gambardella L, Schmidhuber J (2013). "Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks".

7893:

Cireşan DC, Meier U, Gambardella LM, Schmidhuber J (21 September 2010). "Deep, Big, Simple Neural Nets for Handwritten Digit Recognition".

4880: 1791: 1482:

et al., that classifies digits, was applied by several banks to recognize hand-written numbers on checks digitized in 32x32 pixel images.

896: 747: 12637: 12359:

Vicentini F, Biella A, Regnault N, Ciuti C (28 June 2019). "Variational Neural-Network Ansatz for Steady States in Open Quantum Systems".

8736:

Wolf T, Debut L, Sanh V, Chaumond J, Delangue C, Moi A, et al. (2020). "Transformers: State-of-the-Art Natural Language Processing".

9401:

Ojha VK, Abraham A, Snášel V (1 April 2017). "Metaheuristic design of feedforward neural networks: A review of two decades of research".

5418: 4537: 4296:

It is possible to create a profile of a user's interests from pictures, using artificial neural networks trained for object recognition.

1340:

to classify non-linearily separable pattern classes. Subsequent developments in hardware and hyperparameter tunings have made end-to-end

478: 14074:

Yu X, He S, Gao Y, Yang J, Sha L, Zhang Y, et al. (June 2010). "Dynamic difficulty adjustment of game AI for video game Dead-End".

13046: 12778: 12092: 8393:

Karras T, Aila T, Laine S, Lehtinen J (26 February 2018). "Progressive Growing of GANs for Improved Quality, Stability, and Variation".

4778:

models (combining neural networks and symbolic approaches) say that such a mixture can better capture the mechanisms of the human mind.

2398:

assumptions (the implicit properties of the model, its parameters and the observed variables). As a trivial example, consider the model

1135:

Simplified example of training a neural network in object detection: The network is trained by multiple images that are known to depict

13518: 12603: 6749: 1166:

In reality, textures and outlines would not be represented by single nodes, but rather by associated weight patterns of multiple nodes.

979: 782: 13372: 13328: 9891:

Jospin LV, Laga H, Boussaid F, Buntine W, Bennamoun M (2022). "Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users".

3230:

that have proven particularly successful in processing visual and other two-dimensional data; where long short-term memory avoids the

2174:

are primarily aimed at speeding up error minimization, while other improvements mainly try to increase reliability. In order to avoid

14338: 5088:

based on content. This has implications for automated customer service, content moderation, and language understanding technologies.

2354:(also known as function approximation). Supervised learning is also applicable to sequential data (e.g., for handwriting, speech and 1898: 13489: 13192: 9871:, Frontiers in artificial intelligence and applications: Knowledge-based intelligent engineering systems, vol. 102, IOS Press, 8907: 15216: 8490:

He K, Zhang X, Ren S, Sun J (2016). "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification".

4643: 3115: 1776: 1271: 14043:

Chow PS (6 July 2020). "Ghost in the (Hollywood) machine: Emergent applications of artificial intelligence in the film industry".

13389: 12969:

Xu ZJ, Zhang Y, Xiao Y (2019). "Training Behavior of Deep Neural Network in Frequency Domain". In Gedeon T, Wong K, Lee M (eds.).

10573:

Probst P, Boulesteix AL, Bischl B (26 February 2018). "Tunability: Importance of Hyperparameters of Machine Learning Algorithms".

5079:

enabled the development of more accurate and efficient voice-activated systems, enhancing user interfaces in technology products.

2969:, while a policy is defined as the conditional distribution over actions given the observations. Taken together, the two define a 1708:

allowed the use of larger networks, particularly in image and visual recognition problems, which became known as "deep learning".

1525:(1982).Another origin of RNN was neuroscience. The word "recurrent" is used to describe loop-like structures in anatomy. In 1901, 15802: 12422:"Simulation of alcohol action upon a detailed Purkinje neuron model and a simpler surrogate model that runs >400 times faster" 11665:"Accelerating Stochastic Assessment of Post-Earthquake Transportation Network Connectivity via Machine-Learning-Based Surrogates" 9324: 9285: 7030:"Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network" 6240: 5143:

ANNs are able to process and analyze vast medical datasets. They enhance diagnostic accuracy, especially by interpreting complex

1714:

and wavelet networks were introduced in 2013. These can be shown to offer best approximation properties and have been applied in

858: 12726: 12160: 10513: 6446:

Fukushima K (1979). "Neural network model for a mechanism of pattern recognition unaffected by shift in position—Neocognitron".

3059:

are a type of artificial neural network built by introducing random variations into the network, either by giving the network's

16616: 15685: 10994:

Sengupta N, Sahidullah, Md, Saha, Goutam (August 2016). "Lung sound classification using cepstral-based statistical features".

9759:

Bozinovski S, Bozinovska L (2001). "Self-learning agents: A connectionist theory of emotion based on crossbar value judgment".

9023: 8607: 6587: 3103: 2791:. Because the state transitions are not known, probability distributions are used instead: the instantaneous cost distribution 1866: 1677:

by a significant margin over shallow machine learning methods. Further incremental improvements included the VGG-16 network by

407: 13124:

Tao Luo, Zheng Ma, Zhi-Qin John Xu, Yaoyu Zhang (2019). "Theory of the Frequency Principle for General Deep Neural Networks".

9559:

Secomandi N (2000). "Comparing neuro-dynamic programming algorithms for the vehicle routing problem with stochastic demands".

7974: 7133:

Qian, Ning, and Terrence J. Sejnowski. "Predicting the secondary structure of globular proteins using neural network models."

1270:(1954) used computational machines to simulate a Hebbian network. Other neural network computational machines were created by 14700: 14673: 14646: 14592: 14560: 14525: 14498: 14471: 14444: 14410: 14383: 14356: 14258: 14231: 14183: 14156: 14091: 13785: 13404: 13293: 12996: 12827: 12720: 12067: 12035: 11964: 11105: 10915: 10858: 10801: 10744: 10707: 10477:"Unidirectional Long Short-Term Memory Recurrent Neural Network with Recurrent Output Layer for Low-Latency Speech Synthesis" 10098: 9975: 9876: 9726: 9705: 9531: 9373: 9334: 9295: 9119: 9057: 8783: 8590: 8239: 8102: 8051: 7478: 6718: 6427: 6173: 6125: 5886: 5849: 5664: 5607: 5579: 5542: 5298: 5263: 1179: 14308: 12935: 11364: 10476: 10366: 9837: 8953: 8185: 7484: 17692: 16475: 14725: 11912:

Ermini L, Catani F, Casagli N (1 March 2005). "Artificial Neural Networks applied to landslide susceptibility assessment".

5238: 5161: 916: 679: 214: 11119: 10929: 10815: 9742: 5460: 4984: 17849: 17839: 16576: 16127: 15864: 12811: 11446: 8632:

Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. (12 June 2017). "Attention Is All You Need".

4691: 3210: 1568:. In 1993, a neural history compressor system solved a "Very Deep Learning" task that required more than 1000 subsequent 934: 13668:"Failing at Face Value: The Effect of Biased Facial Recognition Technology on Racial Discrimination in Criminal Justice" 11610:

Nabian MA, Meidani H (28 August 2017). "Deep Learning for Accelerated Reliability Analysis of Infrastructure Networks".

11299: 9623: 8267:. Proceedings of the International Conference on Neural Information Processing Systems (NIPS 2014). pp. 2672–2680. 6700: 5431: 4850:

are omitted for clarity. There are p inputs to this network and q outputs. In this system, the value of the qth output,

1097:. They can learn from experience, and can derive conclusions from a complex and seemingly unrelated set of information. 17844: 17379: 14888: 13980: 12182:

Nagy A (28 June 2019). "Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems".

11150: 10872: 8127: 2900: 2732: 2670: 767: 742: 691: 11988: 7871: 7843: 6648:

The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors

2178:

inside the network such as alternating connection weights, and to improve the rate of convergence, refinements use an

1779:

accuracy, known as the "degradation" problem. In 2015, two techniques were developed to train very deep networks: the

1412:. The modern form of backpropagation was developed multiple times in early 1970s. The earliest published instance was 17834: 17591: 17401: 17310: 17252: 17124: 16388: 16015: 15822: 15678: 14545:

Cognitive science: integrative synchronization mechanisms in cognitive neuroarchitectures of the modern connectionism

9941: 9662: 9617: 9477: 9464:. IJCNN-91-Seattle International Joint Conference on Neural Networks. Seattle, Washington, US: IEEE. pp. 71–76. 8991: 8815: 8367: 7691: 7600: 6899: 6597: 5790: 5268: 4459: 3271:: If the model, cost function and learning algorithm are selected appropriately, the resulting ANN can become robust. 3183: 2305: 2110: 2034: 2028: 1884: 1474:

hardware. In 1991, a CNN was applied to medical image object segmentation and breast cancer detection in mammograms.

1118: 815: 810: 463: 10078: 5965:

Rosenblatt F (1958). "The Perceptron: A Probabilistic Model For Information Storage And Organization in the Brain".

4430: 3154: 2276: 2081: 1928: 17859: 16343: 15447: 13711: 12756:"Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition" 12245:

Yoshioka N, Hamazaki R (28 June 2019). "Constructing neural stationary states for open quantum many-body systems".

11754: 11696:"Use of artificial neural networks to predict 3-D elastic settlement of foundations on soils with inclined bedrock" 4197: 3012:

Self-learning in neural networks was introduced in 1982 along with a neural network capable of self-learning named

1800:

model was developed, and attention mechanisms were added. It led to the modern Transformer architecture in 2017 in

1336:. In computer experiments conducted by Amari's student Saito, a five layer MLP with two modifiable layers learned 473: 111: 17: 11447:"Artificial Neural Networks Applied to Outcome Prediction for Colorectal Cancer Patients in Separate Institutions" 10284: 1542: 17605: 17315: 15022: 14718: 13887:"Exploring the Advancements and Future Research Directions of Artificial Neural Networks: A Text Mining Approach" 9942:"A selective improvement technique for fastening Neuro-Dynamic Programming in Water Resources Network Management" 9493:

Hoskins J, Himmelblau, D.M. (1992). "Process control via artificial neural networks and reinforcement learning".

7999:

Ciresan D, Giusti A, Gambardella LM, Schmidhuber J (2012). Pereira F, Burges CJ, Bottou L, Weinberger KQ (eds.).

7441: 7325: 5293: 4996: 4322: 4125: 1715: 1546: 31: 17350: 13354: 9188:

Widrow B, et al. (2013). "The no-prop algorithm: A new learning algorithm for multilayer neural networks".

7029: 6982: 6936:"Parallel distributed processing model with local space-invariant interconnections and its optical architecture" 6935: 3286:

provides functions to help with building a deep network from scratch. We can then implement a deep network with

17712: 17436: 16530: 16470: 16068: 15080: 12851: 11672: 5157: 3238: 2847: 2794: 2237: 1721: 1697: 1281:

described the perceptron, one of the first implemented artificial neural networks, funded by the United States

972: 868: 632: 453: 11032: 10972: 8699: 5633: 5532: 3261:: Numerous trade-offs exist between learning algorithms. Almost any algorithm will work well with the correct 17628: 17369: 17240: 16965: 16063: 15752: 15618: 15601: 15500: 15338: 14984: 13309:

NASA – Dryden Flight Research Center – News Room: News Releases: NASA NEURAL NETWORK PROJECT PASSES MILESTONE

10004: 9050:

Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies

8738:

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

7377:"Learning complex, extended sequences using the principle of history compression (based on TR FKI-148, 1991)" 4739: 4474: 4282: 3305: 2013:. Alternatively, networks that allow connections between neurons in the same or previous layers are known as 1711: 1310: 1053:, and the output of each neuron is computed by some non-linear function of the sum of its inputs, called the 843: 545: 321: 13020:

Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, et al. (2019).

12659:

Balcázar J (July 1997). "Computational Power of Neural Networks: A Kolmogorov Complexity Characterization".

8946:

Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence-Volume Volume Two

17748: 17717: 17623: 17452: 17411: 17300: 17119: 16940: 16505: 15902: 15859: 15812: 15807: 15209: 12298:

Hartmann MJ, Carleo G (28 June 2019). "Neural-Network Approach to Dissipative Quantum Many-Body Dynamics".

11878: 11325: 7160:

Rost, Burkhard, and Chris Sander. "Prediction of protein secondary structure at better than 70% accuracy."

6888:

IEEE Transactions on Acoustics, Speech, and Signal Processing, Volume 37, No. 3, pp. 328. – 339 March 1989.

6350:

Sonoda S, Murata N (2017). "Neural network with unbounded activation functions is universal approximator".

4712: 4695: 4486: 3227: 1611: 1486: 1436: 1374: 800: 737: 647: 625: 468: 458: 9949: 6884: 17406: 17305: 17159: 16556: 15852: 15778: 14830: 14334: 9649:. 2000 Congress on Evolutionary Computation. Vol. 1. La Jolla, California, US: IEEE. pp. 7–14. 9009:"Comparative analysis of Recurrent and Finite Impulse Response Neural Networks in Time Series Prediction" 6822: 5505: 5258: 5218: 5008: 4521: 4208: 4137: 3197: 3111: 3095: 2989: 2227: 1811: 1594:

During 1985–1995, inspired by statistical mechanics, several architectures and methods were developed by

1352: 1341: 1329: 1106: 951: 863: 848: 309: 131: 16825: 11828: 11801: 10187: 10130: 10055: 8075:

Ciresan D, Meier U, Schmidhuber J (2012). "Multi-column deep neural networks for image classification".

6869:. Meeting of the Institute of Electrical, Information and Communication Engineers (IEICE). Tokyo, Japan. 6323:

Fukushima K (1969). "Visual feature extraction by a multilayered network of analog threshold elements".

6112: 4824: 1521:

rule as a model of associative memory, adding in the component of learning. This was popularized as the

17646: 17550: 17384: 16686: 16609: 16180: 16115: 15716: 15634: 15624: 15490: 15399: 15075: 14800: 13308: 12712: 12022:, Advances in Information Security, vol. 70, Springer International Publishing, pp. 137–153, 11815:

Govindaraju RS (1 April 2000). "Artificial Neural Networks in Hydrology. II: Hydrologic Applications".

10410:

Proceedings of the Annual Conference of the International Speech Communication Association, Interspeech

7679:

Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations

7514: 6823:"Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position" 5373: 5253: 3279: 3275: 3231: 3067: 2401: 2008: 1580: 1194: 1113:

are usually used to estimate the parameters of the network. During the training phase, ANNs learn from

911: 838: 588: 483: 271: 204: 164: 14276:

Egmont-Petersen M, de Ridder D, Handels H (2002). "Image processing with neural networks – a review".

7617: 5446: 5050:

Parallel pipeline structure of CMAC neural network. This learning algorithm can converge in one step.

1748:

to predict the reactions of the environment to these patterns. Excellent image quality is achieved by

55:

and an arrow represents a connection from the output of one artificial neuron to the input of another.

17778: 17758: 17677: 17325: 17154: 17129: 16998: 16581: 16439: 16078: 15909: 15732: 15550: 15485: 15442: 14858: 14004:"From artificial neural networks to deep learning for music generation: history, concepts and trends" 13272:

Dean Pomerleau, "Knowledge-based Training of Artificial Neural Networks for Autonomous Robot Driving"

12806:(2019). "Reproducibility and Experimental Design for Machine Learning on Audio and Multimedia Data". 10724: 10549: 8296:(1991). "A possibility for implementing curiosity and boredom in model-building neural controllers". 6098:

Contributions to Perceptron Theory, Cornell Aeronautical Laboratory Report No. VG-11 96--G-7, Buffalo

5392: 5208: 5132: 4788: 4420: 4147: 3144: 3056: 2619: 2577: 2394: 2266: 2148: 2124: 2071: 1802: 1451: 1401: 1144: 1021: 965: 571: 339: 209: 30:

This article is about the computational models used for artificial intelligence. For other uses, see

14176:

Neuro-Fuzzy-Systeme: von den Grundlagen künstlicher Neuronaler Netze zur Kopplung mit Fuzzy-Systemen

12673: 11947:

Nix R, Zhang J (May 2017). "Classification of Android apps and malware using deep neural networks".

10640: 10627:

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

9688: 9573: 9157: 8259: 7768: 7670: 7428:

Page 150 ff demonstrates credit assignment across the equivalent of 1,200 layers in an unfolded RNN.

5979: 5406: 4105: 2460: 1732:

who called it "artificial curiosity": two neural networks contest with each other in the form of a

1526: 17854: 17697: 17576: 17441: 17093: 17068: 16993: 16900: 16805: 16701: 16480: 15737: 15505: 15275: 14994: 14936: 14863: 14795: 14290: 14120: 11788:

Govindaraju RS (1 April 2000). "Artificial Neural Networks in Hydrology. I: Preliminary Concepts".

11511: 8414: 8232:

Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains

7096: 4745: 4735: 4716: 4514: 4424: 4416: 4337: 4290: 3148: 3140: 3041: 2270: 2262: 2232: 2075: 2067: 2014: 1862: 1741: 1337: 1282: 593: 513: 436: 354: 184: 146: 141: 101: 96: 13764: 9363: 8831:

Abbod MF (2007). "Application of Artificial Intelligence to the Management of Urological Cancer".

6050:

Olazaran M (1996). "A Sociological Study of the Official History of the Perceptrons Controversy".

5061:

such as image processing, speech recognition, natural language processing, finance, and medicine.

2555: 2525: 2438: 2373: 1907:

Neuron and myelinated axon, with signal flow from inputs at dendrites to outputs at axon terminals

1556:

In the 1980s, backpropagation did not work well for deep RNNs. To overcome this problem, in 1991,

43:

An artificial neural network is an interconnected group of nodes, inspired by a simplification of

17869: 17525: 17394: 16930: 16641: 16636: 16525: 16510: 16163: 16158: 16058: 15926: 15707: 15515: 15495: 15202: 14999: 14954: 14893: 14825: 14785: 12184: 11369: 10678: 10360:"Long Short-Term Memory recurrent neural network architectures for large scale acoustic modeling" 9678:

Deng G, Ferris, M.C. (2008). "Neuro-dynamic programming for fractionated radiotherapy planning".

8163:

Simonyan K, Andrew Z (2014). "Very Deep Convolution Networks for Large Scale Image Recognition".

5474: 5283: 4749: 4724: 4170: 4119: 2664: 2627: 1784: 1635: 1584: 1467: 1240: 1229:. Unlike the von Neumann model, connectionist computing does not separate memory and processing. 1202: 1094: 540: 389: 289: 116: 12852:"Stop tinkering, start measuring! Predictable experimental design of Neural Network experiments" 6190: 2392:

and the network's output. The cost function is dependent on the task (the model domain) and any

1288:

The first perceptrons did not have adaptive hidden units. However, Joseph (1960) also discussed

17682: 16925: 16915: 16706: 16661: 16485: 16245: 15964: 15959: 15581: 15560: 15254: 15085: 14926: 14285: 12668: 12629: 9683: 9568: 9152: 7583:

Gers F, Schmidhuber J, Cummins F (1999). "Learning to forget: Continual prediction with LSTM".

7091: 5974: 5328: 5318: 5313: 5308: 5288: 5243: 5213: 5198: 5112: 4754: 4441: 4394:. Deeper neural networks have been observed to be more biased towards low frequency functions. 4097: 3165: 3107: 2652: 2639: 2327: 2287: 2179: 2152: 2092: 2004: 1643: 1588: 1565: 1377:(CNNs) with convolutional layers and downsampling layers and weight replication began with the 1289: 720: 696: 598: 359: 334: 294: 106: 17613: 14205: 9740:

Modeling mechanisms of cognition-emotion interaction in artificial neural networks, since 1981

7683: 2226:

associated with a given state with respect to the weights. The weight updates can be done via

1923:

An artificial neural network consists of simulated neurons. Each neuron is connected to other

17768: 17687: 17228: 16988: 16983: 16790: 16681: 16602: 16515: 16500: 16465: 16153: 16053: 15921: 15520: 15464: 14921: 14898: 14815: 13283: 13021: 12943:. 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montreal, Canada. 12755: 12052:

Proceedings of the Twenty-Seventh Hawaii International Conference on System Sciences HICSS-94

8206:

Ng A, Dean J (2012). "Building High-level Features Using Large Scale Unsupervised Learning".

7649: 5441: 5233: 4775: 4314: 4109: 2606: 2367: 2323: 2200: 1955:

To find the output of the neuron we take the weighted sum of all the inputs, weighted by the

1924: 1827: 1815: 1705: 1569: 1506: 1325: 1210: 1129: 1082:). A network is typically called a deep neural network if it has at least two hidden layers. 674: 496: 448: 304: 219: 91: 16383: 13511: 13455: 12580: 10431: 10304: 8653: 8311: 8293: 7970: 7510: 7350: 7273: 6738: 5750: 3066:

transfer functions , or by giving them stochastic weights. This makes them useful tools for

1807: 1729: 1647: 1557: 17335: 17330: 17209: 16960: 16800: 16771: 16666: 16535: 16490: 15936: 15881: 15727: 15722: 15586: 15437: 15057: 15017: 14959: 14432: 13556: 13332: 13090: 12900: 12527: 12378: 12317: 12264: 12203: 11921: 11851: 11707: 11629: 11417: 11267: 11217: 10953:"A cloud based architecture capable of perceiving and predicting multiple vessel behaviour" 10674: 10600:

Zoph B, Le QV (4 November 2016). "Neural Architecture Search with Reinforcement Learning".

10447: 9940:

de Rigo, D., Castelletti, A., Rizzoli, A. E., Soncini-Sessa, R., Weber, E. (January 2005).

9420: 8879: 8258:

Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. (2014).

7802: 7226: 7041: 6994: 6947: 6787: 6395:

Ramachandran P, Barret Z, Quoc VL (16 October 2017). "Searching for Activation Functions".

5388: 5303: 4853: 4720: 4219: 3091: 3087: 1674: 1619: 1550: 603: 553: 10731:. Springer Texts in Business and Economics. Springer Berlin Heidelberg. pp. 209–256. 7853:

with Juergen Schmidhuber on the eight competitions won by his Deep Learning team 2009–2012

5902:

Farley B, W.A. Clark (1954). "Simulation of Self-Organizing Systems by Digital Computer".

1420:

developed it independently in 1971, but had difficulty publishing it until 1982. In 1986,

1017: 8: 17809: 17491: 17103: 17003: 16880: 16850: 16835: 16110: 16088: 15837: 15832: 15790: 15742: 15545: 15530: 15458: 15452: 15383: 15270: 15037: 14931: 14873: 14770: 14224:

Yes, we have no neutrons: an eye-opening tour through the twists and turns of bad science

13481: 13285:

Yes, we have no neutrons: an eye-opening tour through the twists and turns of bad science

13146: 11143:"Sensor for food analysis applying impedance spectroscopy and artificial neural networks" 10149: 9939: 8915: 8718: 4766:

successful. For example, local vs. non-local learning and shallow vs. deep architecture.

4664: 4655: 4510: 4506: 4286: 4267: 4234: 4162: 4143: 4101: 3099: 3090:

framework, a distribution over the set of allowed models is chosen to minimize the cost.

2977: 2355: 2351: 2347: 2338: 2319: 1972: 1854: 1631: 1356: 1086: 1055: 706: 642: 613: 518: 344: 277: 263: 249: 224: 174: 126: 86: 14436: 13560: 13094: 12973:. Lecture Notes in Computer Science. Vol. 11953. Springer, Cham. pp. 264–274. 12904: 12556: 12531: 12515: 12382: 12321: 12268: 12207: 11925: 11855: 11711: 11633: 11421: 11271: 11221: 10451: 9424: 8883: 8441: 7806: 7453:

Diploma thesis. Institut f. Informatik, Technische Univ. Munich. Advisor: J. Schmidhuber

7418: 7274:"The Importance of Cajal's and Lorente de Nó's Neuroscience to the Birth of Cybernetics" 7230: 7045: 6998: 6951: 6861: 6791: 4340:, using a finite number of neurons and standard linear connections. Further, the use of 27:

Computational model used in machine learning, based on connected, hierarchical functions

17586: 17581: 17509: 17416: 17340: 17179: 17073: 17013: 16736: 16716: 16495: 16073: 15540: 15353: 15312: 15183: 15178: 15133: 15090: 15052: 15009: 14868: 14625: 14566: 14097: 13791: 13728: 13640: 13613: 13577: 13544: 13437: 13227: 13184: 13158: 13125: 13106: 13080: 13036: 13019: 13002: 12974: 12916: 12890: 12833: 12448: 12421: 12402: 12368: 12341: 12307: 12280: 12254: 12227: 12193: 12073: 11970: 11645: 11619: 11587: 11562: 11477: 11291: 11189: 11111: 10921: 10864: 10807: 10750: 10663: 10630: 10601: 10582: 10553: 10384: 10340: 10260: 10210: 10153: 9922: 9896: 9817: 9795: 9776: 9436: 9410: 9224: 9170: 9143:

Huang GB, Zhu QY, Siew CK (2006). "Extreme learning machine: theory and applications".

9125: 9082: 8937:

Ciresan D, Ueli Meier, Jonathan Masci, Luca M. Gambardella, Jurgen Schmidhuber (2011).

8749: 8680: 8633: 8568: 8537: 8516: 8491: 8471: 8394: 8349: 8323: 8207: 8164: 8108: 8080: 7936: 7902: 7826: 7757: 7671:"Chapter 6: Information Processing in Dynamical Systems: Foundations of Harmony Theory" 7565: 7399: 7215:"Neural networks and physical systems with emergent collective computational abilities" 7176:"Learning Patterns and Pattern Sequences by Self-Organizing Nets of Threshold Elements" 7109: 6681: 6568: 6542: 6510: 6467: 6396: 6377: 6359: 6301: 6075: 6067: 6000: 5754: 5203: 4502: 4229: 4166: 3063: 2981: 2645: 2549: 2343: 1941: 1510: 1494: 1432: 1421: 1382: 1348: 1333: 1198: 684: 608: 394: 189: 17282: 14299: 11465: 10782:"A self-adaptive similarity-based fitness approximation for evolutionary optimization" 9582: 7633: 7249: 7214: 7076: 2195:, frequently the choice is determined by the function's desirable properties (such as 17374: 17098: 17033: 17008: 16860: 16820: 16561: 16549: 16353: 16005: 15876: 15869: 15591: 15421: 15394: 15378: 15363: 15358: 15348: 15302: 15292: 15095: 15067: 14790: 14760: 14696: 14679: 14669: 14652: 14642: 14620: 14603: 14588: 14570: 14556: 14531: 14521: 14504: 14494: 14477: 14467: 14450: 14440: 14416: 14406: 14389: 14379: 14362: 14352: 14264: 14254: 14237: 14227: 14189: 14179: 14162: 14152: 14139: 14087: 14056: 14025: 13952: 13908: 13858: 13837:"Advances in Artificial Neural Networks – Methodological Development and Application" 13795: 13781: 13766:

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

13733: 13689: 13645: 13582: 13400: 13289: 13255: 13247: 13188: 13176: 12992: 12837: 12823: 12716: 12703: 12699: 12599: 12561: 12543: 12472: 12453: 12406: 12394: 12345: 12333: 12284: 12231: 12219: 12152: 12063: 12031: 11960: 11735: 11592: 11531: 11469: 11374: 11333: 11283: 11233: 11193: 11101: 11011: 11007: 10911: 10868: 10854: 10797: 10754: 10740: 10703: 10405: 10332: 10324: 10094: 9971: 9957: 9926: 9914: 9872: 9722: 9701: 9658: 9613: 9527: 9506: 9473: 9456: 9369: 9330: 9291: 9245: 9205: 9115: 9063: 9053: 8987: 8848: 8811: 8789: 8779: 8753: 8745: 8586: 8353: 8341: 8235: 8098: 8057: 8047: 7928: 7920: 7818: 7793:, Neal R (26 May 1995). "The wake-sleep algorithm for unsupervised neural networks". 7749: 7744: 7687: 7637: 7596: 7557: 7474: 7354: 7307: 7299: 7254: 7195: 7057: 7010: 6963: 6842: 6838: 6803: 6714: 6710: 6685: 6593: 6560: 6514: 6502: 6471: 6459: 6423: 6206: 6169: 6121: 5992: 5882: 5830: 5786: 5779: 5660: 5603: 5575: 5538: 5400: 5323: 5248: 4659: 4525: 4341: 4275: 4248: 4214: 4155: 4151: 3250:

Using artificial neural networks requires an understanding of their characteristics.

3071: 3060: 2128: 1806:. It requires computation time that is quadratic in the size of the context window. 1615: 1607: 1471: 1314: 1305:

Fundamental research was conducted on ANNs in the 1960s and 1970s. The first working

1221: 1206: 1033: 777: 620: 533: 329: 299: 244: 239: 194: 136: 52: 14101: 13441: 13110: 13006: 12920: 12077: 11649: 11481: 11406:"Application of Neural Networks in Diagnosing Cancer Disease Using Demographic Data" 11115: 11086:"CNN based common approach to handwritten character recognition of multiple scripts" 10925: 10811: 10586: 10264: 10214: 10044:

New Aspects in Neurocomputing: 11th European Symposium on Artificial Neural Networks

9440: 9129: 9008: 8684: 7467:"Gradient flow in recurrent nets: the difficulty of learning long-term dependencies" 7403: 7113: 6572: 6381: 6079: 6004: 5625: 5419:

Artificial Neural Networks Tutorial in three languages (Univ. Politécnica de Madrid)

1443:, a popular downsampling procedure for CNNs. CNNs have become an essential tool for 1153: 17485: 17320: 17283: 17139: 17023: 16950: 16865: 16746: 16696: 16306: 16296: 16103: 15897: 15847: 15842: 15785: 15773: 15404: 15239: 15105: 14883: 14810: 14629: 14615: 14548: 14295: 14135: 14079: 14048: 14015: 13942: 13898: 13848: 13773: 13723: 13712:"Gender Bias in Hiring: An Analysis of the Impact of Amazon's Recruiting Algorithm" 13679: 13635: 13625: 13572: 13564: 13429: 13237: 13168: 13098: 12984: 12908: 12815: 12803: 12770: 12678: 12595: 12551: 12535: 12484: 12443: 12433: 12390: 12386: 12329: 12325: 12272: 12215: 12211: 12055: 12050:

Ghosh, Reilly (January 1994). "Credit card fraud detection with a neural-network".

12023: 11974: 11952: 11933: 11929: 11859: 11824: 11797: 11725: 11715: 11637: 11582: 11574: 11523: 11461: 11425: 11275: 11225: 11181: 11093: 11003: 10964: 10903: 10846: 10789: 10732: 10695: 10455: 10344: 10316: 10252: 10202: 10157: 10145: 10086: 10047: 9961: 9953: 9906: 9780: 9768: 9693: 9650: 9605: 9595: 9578: 9502: 9465: 9432: 9428: 9223:

Ollivier Y, Charpiat G (2015). "Training recurrent networks without backtracking".

9197: 9162: 9107: 8939:"Flexible, High Performance Convolutional Neural Networks for Image Classification" 8887: 8840: 8741: 8672: 8578: 8333: 8112: 8090: 8039: 7966: 7956:"Flexible, High Performance Convolutional Neural Networks for Image Classification" 7955: 7940: 7912: 7810: 7761: 7739: 7731: 7629: 7588: 7569: 7549: 7520: 7391: 7376: 7289: 7281: 7244: 7234: 7187: 7101: 7049: 7002: 6955: 6834: 6795: 6706: 6673: 6661: 6643: 6625: 6552: 6494: 6451: 6369: 6332: 6281: 6232: 6202: 6144: 6059: 6021:

Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences

5984: 5938: 5911: 5822: 5702: 5228: 4796: 4513:. A confidence analysis made this way is statistically valid as long as the output 4345: 4129: 3258: 2623: 2545: 2132: 1769: 1745: 1737: 1682: 1522: 1413: 1359:. The rectifier has become the most popular activation function for deep learning. 1278: 1259: 1232: 1090: 993: 805: 558: 508: 418: 402: 372: 234: 229: 179: 169: 67: 13947: 13930: 12514:

Merchant A, Batzner S, Schoenholz SS, Aykol M, Cheon G, Cubuk ED (December 2023).

12488: 11512:"Measuring systematic changes in invasive cancer cell shape using Zernike moments" 11295: 11185: 9174: 8936: 7830: 6221: 1952:

of the neural net accomplish the task, such as recognizing an object in an image.

1209:. It was used as a means of finding a good rough linear fit to a set of points by 17565: 17519: 17199: 17194: 17189: 17184: 17078: 17018: 16955: 16870: 16830: 16815: 16766: 16756: 16711: 16419: 16363: 16185: 15827: 15747: 15649: 15576: 15416: 15343: 15297: 15264: 15249: 15153: 15100: 14989: 14946: 14878: 14835: 14820: 14780: 14582: 14578: 14315: 14076:

The 3rd International Conference on Information Sciences and Interaction Sciences

13931:"Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration" 13630: 13396: 13315: 13242: 13215: 12988: 12751: 11863: 11036: 10699: 10300: 10288: 10051: 9746: 9597: 9201: 9166: 9104:

2009 International Conference on Computational Intelligence and Natural Computing

8938: 8337: 8043: 7850: 7782: 7715: 7711: 7537: 7506: 7466: 7448: 6900:"Shift-invariant pattern recognition neural network and its optical architecture" 6613: 6556: 6417: 6265: 6163: 6019: 5876: 5774: 5453: 5435: 5413: 5144: 4792: 4694:

of AI has contributed towards the development of methods, notably those based on

4478: 4329: 4133: 4115: 2985: 2615: 2212: 2199:) or because it arises from the model (e.g. in a probabilistic model the model's 2196: 1780: 1761: 1670: 1662: 1603: 1595: 1576: 1444: 1405: 1393: 1267: 1255: 1110: 833: 637: 503: 443: 14083: 13568: 12027: 11720: 11695: 10736: 10090: 9697: 5679:

Mansfield Merriman, "A List of Writings Relating to the Method of Least Squares"

4699:

regarding local vs non-local learning, as well as shallow vs deep architecture.

4477:

and similar techniques to check for the presence of over-training and to select

3114:

are other learning algorithms. Convergent recursion is a learning algorithm for

2236:, "no-prop" networks, training without backtracking, "weightless" networks, and 17804: 17799: 17389: 17164: 17134: 17063: 17038: 17028: 16875: 16855: 16845: 16731: 16651: 16393: 16358: 16348: 16173: 15931: 15757: 15644: 15629: 15596: 15555: 15389: 15307: 15244: 15225: 15143: 15120: 15047: 14805: 14741: 14201: 14020: 14003: 12912: 12539: 12276: 11956: 11209: 11097: 11090:

2015 13th International Conference on Document Analysis and Recognition (ICDAR)

11085: 10968: 10907: 10895: 10781: 10460: 10435: 10320: 10085:. Lecture Notes in Computer Science. Vol. 5553. Springer. pp. 49–58. 9739: 8844: 8700:"Transformers are RNNs: Fast autoregressive Transformers with linear attention" 8558: 7553: 7175: 6373: 6063: 5116: 4801: 4362: 4177: 3297: 3262: 3037: 3032: 2997: 1917: 1913: 1736:, where one network's gain is the other network's loss. The first network is a 1725: 1678: 1666: 1534: 1518: 1409: 1367: 1247: 1161: 853: 384: 121: 14052: 13355:"Scaling Learning Algorithms towards {AI} - LISA - Publications - Aigaion 2.0" 13172: 13147:"Deep Frequency Principle Towards Understanding Why Deeper Learning is Faster" 13102: 12438: 12059: 11485: 11030:

3d-r2n2: A unified approach for single and multi-view 3d object reconstruction

10793: 10256: 10206: 9602:

Proceedings of MODSIM 2001, International Congress on Modelling and Simulation

9598:"Neuro-dynamic programming for the efficient management of reservoir networks" 9596:

de Rigo, D., Rizzoli, A. E., Soncini-Sessa, R., Weber, E., Zenesi, P. (2001).

9469: 9067: 8892: 8867: 8676: 8094: 7735: 7395: 7285: 6286: 6269: 6236: 6035:

Rosenblatt F (1957). "The Perceptron—a perceiving and recognizing automaton".

5810: 5428: 4821:

A single-layer feedforward artificial neural network. Arrows originating from

2996:

because of ANNs ability to mitigate losses of accuracy even when reducing the

2370:, input data is given along with the cost function, some function of the data 17828: 17497: 17447: 17088: 17083: 17048: 16945: 16935: 16895: 16741: 16676: 16671: 16338: 16318: 16235: 15914: 15535: 15525: 15368: 15333: 15259: 15148: 15110: 14969: 14481: 14060: 14029: 13971:"DALL-E 2's Failures Are the Most Interesting Thing About It - IEEE Spectrum" 13956: 13912: 13862: 13777: 13737: 13693: 13614:"Addressing bias in big data and AI for health care: A call for open science" 13545:"A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation" 13390:

Natural and Artificial Intelligence: Introduction to Computational Brain-Mind

13251: 13180: 13070: 12774: 12547: 11739: 11378: 11337: 11326:"Artificial Intelligence Glossary: Neural Networks and Other Terms Explained" 11252: 10850: 10328: 9918: 9910: 8793: 7924: 7719: 7677: 7641: 7303: 7199: 6846: 6807: 6336: 6120:(3rd ed.). United States of America: Pearson Education. pp. 16–28. 5942: 5915: 5834: 5707: 5690: 4391: 4271: 4224: 2611: 2223: 2164: 2139: 2042: 1984: 1764:(2015) eclipsed GANs in generative modeling since then, with systems such as 1733: 1363: 1306: 1294: 1226: 1117:

training data by iteratively updating their parameters to minimize a defined

772: 701: 583: 314: 199: 14683: 14656: 14535: 14508: 14454: 14420: 14393: 14366: 14268: 14241: 14193: 14166: 13970: 13684: 13667: 12819: 12093:"Latest Neural Nets Solve World's Hardest Equations Faster Than Ever Before" 11142: 10838: 9682:. Springer Optimization and Its Applications. Vol. 12. pp. 47–70. 9654: 8658:"Learning to control fast-weight memories: an alternative to recurrent nets" 7814: 7191: 7074: 5376:

was created from a revision of this article dated 27 November 2011

3074:. Stochastic neural networks trained using a Bayesian approach are known as 1622:. These were designed for unsupervised learning of deep generative models. 1362:

Nevertheless, research stagnated in the United States following the work of

17707: 17618: 17531: 17174: 17169: 17053: 16885: 16810: 16761: 16751: 16726: 16721: 16691: 16625: 16424: 16255: 15670: 15639: 15410: 15042: 14974: 14903: 13649: 13586: 13259: 12565: 12457: 12398: 12337: 12223: 11996: 11596: 11535: 11287: 11237: 11015: 9772: 9209: 8852: 8345: 8061: 7932: 7790: 7311: 7239: 7014: 6983:"Image processing of human corneal endothelium based on a learning network" 6967: 6775: 6564: 5996: 5223: 4387: 4379: 4358: 3283: 2970: 2318:

Machine learning is commonly separated into three main learning paradigms,

1470:

on mail. Training required 3 days. In 1990, Wei Zhang implemented a CNN on

1378: 1236: 1114: 1078: 14552: 11563:"Changes in cell shape are correlated with metastatic potential in murine" 11473: 10662:

Claesen M, De Moor B (2015). "Hyperparameter Search in Machine Learning".

10336: 10129:

Ting Qin, Zonghai Chen, Haitao Zhang, Sifu Li, Wei Xiang, Ming Li (2004).

9111: 7822: 7753: 7592: 7561: 7524: 7258: 7061: 6506: 6463: 3507:# 2. in each iteration, feed all layers with the latest weights and biases 17560: 17043: 16890: 16520: 16291: 16200: 16195: 15817: 15795: 15510: 15469: 15373: 15173: 15168: 15158: 15128: 15027: 14979: 14913: 13853: 13836: 13123: 12933: 12473:"Semantic Image-Based Profiling of Users' Interests with Neural Networks" 10185: 10104: 9453: 8582: 8439: 7916: 7786: 7707: 7006: 6959: 6879: 6734: 5469: 5193: 4333: 4318: 4242: 4193: 2175: 1686: 1651: 1599: 1514: 1455: 1440: 1417: 1050: 578: 72: 39: 14462:

Kruse R, Borgelt C, Klawonn F, Moewes C, Steinbrecher M, Held P (2013).

13903: 13886: 12937:

Neural Tangent Kernel: Convergence and Generalization in Neural Networks

11279: 9966: 9609: 8536:

Srivastava RK, Greff K, Schmidhuber J (2 May 2015). "Highway Networks".

8036:

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013

7864:"How bio-inspired deep learning keeps winning competitions | KurzweilAI" 7294: 6904:

Proceedings of Annual Conference of the Japan Society of Applied Physics

5928: 1983:

The neurons are typically organized into multiple layers, especially in

1408:

had a continuous precursor of backpropagation in 1960 in the context of

17204: 16905: 16414: 16373: 16368: 16281: 16190: 16098: 16010: 15990: 14964: 14850: 11730: 11641: 11578: 11527: 10406:"TTS synthesis with bidirectional LSTM based Recurrent Neural Networks" 10281: 10277: 10128: 9644: 8440:

Sohl-Dickstein J, Weiss E, Maheswaranathan N, Ganguli S (1 June 2015).

8038:. Lecture Notes in Computer Science. Vol. 7908. pp. 411–418. 6677: 6533:

Schmidhuber J (2015). "Deep Learning in Neural Networks: An Overview".

6498: 6455: 5826: 5602:(Corrected 2nd print. ed.). New York Berlin Heidelberg: Springer. 5278: 4259: 4187: 3287: 3268: 1530: 1479: 1459: 1397: 1263: 1251: 1190: 1140: 727: 423: 349: 12682: 12123:"AI has cracked a key mathematical puzzle for understanding our world" 11229: 8565:

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

6071: 4804:

can help reduce dataset bias and increase representation in datasets.

1787:(ResNet) in Dec 2015. ResNet behaves like an open-gated Highway Net. 1485:

From 1988 onward, the use of neural networks transformed the field of

17149: 16840: 16409: 16378: 16276: 16120: 16083: 16020: 15974: 15969: 15954: 15328: 15032: 14845: 14755: 12017: 11916:. Geomorphological hazard and human impact in mountain environments. 11206: 7585:

9th International Conference on Artificial Neural Networks: ICANN '99

7105: 7053: 6799: 5988: 5781:

The History of Statistics: The Measurement of Uncertainty before 1900

5273: 5107:

Applications of artificial intelligence § Trading and investment

4972:{\displaystyle \scriptstyle y_{q}=K*(\sum _{i}(x_{i}*w_{iq})-b_{q}).} 4670: 4493: 4263: 3070:

problems, since the random fluctuations help the network escape from

2171: 2143: 2038: 1693: 1300: 1061: 886: 667: 13433: 13029:

Proceedings of the 36th International Conference on Machine Learning

12859: 12153:"Caltech Open-Sources AI for Solving Partial Differential Equations" 11664: 11430: 11405: 11253:"Mastering the game of Go with deep neural networks and tree search" 8868:"An artificial neural network approach to rainfall-runoff modelling" 8721:(2021). "Linear Transformers Are Secretly Fast Weight Programmers". 8449:

Proceedings of the 32nd International Conference on Machine Learning

8135:

NIPS 2012: Neural Information Processing Systems, Lake Tahoe, Nevada

7271: 6191:"Heuristic self-organization in problems of engineering cybernetics" 2984:

programming) has been applied to problems such as those involved in

17570: 17555: 17058: 16311: 16143: 15654: 15138: 13232: 13163: 13130: 13085: 13041: 12979: 12895: 12373: 12312: 12259: 12198: 11624: 11029: 10952: 10694:(Springer US ed.). Boston, MA: Springer US. pp. 928–987. 10668: 10635: 10606: 10558: 10548: 9901: 9822: 9800: 9415: 9229: 9087: 8638: 8573: 8542: 8521: 8496: 8399: 8328: 8033: 7998: 7954:

Ciresan DC, Meier U, Masci J, Gambardella L, Schmidhuber J (2011).

6629: 6401: 6364: 5759: 5627: 5175: 4618:{\displaystyle y_{i}={\frac {e^{x_{i}}}{\sum _{j=1}^{c}e^{x_{j}}}}} 4183: 3217: 2993: 2219: 1757: 1753: 1313:, a method to train arbitrarily deep neural networks, published by 1136: 1059:. The strength of the signal at each connection is determined by a 14710: 14518:

Signal and image processing with neural networks: a C++ sourcebook

13373:

Distributed hierarchical processing in the primate cerebral cortex

12808:

Proceedings of the 27th ACM International Conference on Multimedia

10993: 10389: 9462:

IJCNN-91-Seattle International Joint Conference on Neural Networks

8476: 8468:

Very Deep Convolutional Networks for Large-Scale Image Recognition

8212: 8169: 8085: 7907: 7892: 6547: 1489:, in particular when the first cascading networks were trained on 1262:. It was used in many early neural networks, such as Rosenblatt's 16434: 16271: 16225: 16148: 16048: 16043: 15995: 15194: 15163: 11879:"Review on Applications of Neural Network in Coastal Engineering" 8128:"ImageNet Classification with Deep Convolutional Neural Networks" 7505: 6920:, "Backpropagation Applied to Handwritten Zip Code Recognition", 5478: 5188: 4204: 2656: 1971:. This weighted sum is then passed through a (usually nonlinear) 1819: 1797: 1658: 1561: 1318: 1085:

Artificial neural networks are used for various tasks, including

1046: 662: 14491:

Introduction to neural networks: design, theory and applications

13612:

Norori N, Hu Q, Aellen FM, Faraci FD, Tzovara A (October 2021).

11039:." European conference on computer vision. Springer, Cham, 2016. 10006:

Applied Soft Computing Technologies: The Challenge of Complexity

8442:"Deep Unsupervised Learning using Nonequilibrium Thermodynamics" 5955:

Haykin (2008) Neural Networks and Learning Machines, 3rd edition

5537:. Karolinska Institute, Stockholm, Sweden: Elsevier. p. 1. 4378:

Another issue worthy to mention is that training may cross some

17763: 16594: 16449: 16429: 16301: 16093: 15286: 14275: 13935:

Journal of Information Technology Case and Application Research

13214:

Parisi GI, Kemker R, Part JL, Kanan C, Wermter S (1 May 2019).

10786:

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

9946:

Proceedings of the 16th IFAC World Congress – IFAC-PapersOnLine

9081:

Wei J (26 April 2019). "Forget the Learning Rate, Decay Loss".

8077:

2012 IEEE Conference on Computer Vision and Pattern Recognition

7272:

Espinosa-Sanchez JM, Gomez-Marin A, de Castro F (5 July 2023).

5166: 4128:

and control (including vehicle control, trajectory prediction,

2192: 1945: 1765: 1749: 1038: 413: 44: 13929:

Fui-Hoon Nah F, Zheng R, Cai J, Siau K, Chen L (3 July 2023).

13762: 12513: 11949:

2017 International Joint Conference on Neural Networks (IJCNN)

11841: 10620: 10040:"An improved PSO-based ANN with simulated annealing technique" 8986:] (in German) (1st ed.). Addison-Wesley. p. 73. 8697: 8608:"Microsoft researchers win ImageNet computer vision challenge" 8257: 6664:(1976). "Taylor expansion of the accumulated rounding error". 5811:"A logical calculus of the ideas immanent in nervous activity" 2605:, whereas in statistical modeling, it could be related to the 1121:. This method allows the network to generalize to unseen data. 17702: 17672: 17667: 17662: 17503: 16250: 16230: 16220: 16215: 16210: 16205: 16168: 16000: 15659: 13420:

Edwards C (25 June 2015). "Growing pains for deep learning".

13151:

Proceedings of the AAAI Conference on Artificial Intelligence

9952:. Vol. 16. Prague, Czech Republic: IFAC. pp. 7–12. 9454:

Dominic, S., Das, R., Whitley, D., Anderson, C. (July 1991).

9246:"A Practical Guide to Training Restricted Boltzmann Machines" 8631: 7540:, Schmidhuber J (1 November 1997). "Long Short-Term Memory". 7075:

LeCun Y, Léon Bottou, Yoshua Bengio, Patrick Haffner (1998).

5626:

Ian Goodfellow and Yoshua Bengio and Aaron Courville (2016).

4289:. In brain research ANNs have studied short-term behavior of 3291: 1823: 1475: 1463: 1214: 1025: 657: 652: 379: 48: 13216:"Continual lifelong learning with neural networks: A review" 12358: 11752: 10623:"Auto-keras: An efficient neural architecture search system" 7953: 6739:"Applications of advances in nonlinear sensitivity analysis" 6650:(Masters) (in Finnish). University of Helsinki. p. 6–7. 5850:"Representation of Events in Nerve Nets and Finite Automata" 5753:(2022). "Annotated History of Modern AI and Deep Learning". 5055: 1967:

term to this sum. This weighted sum is sometimes called the

17753: 17479: 16240: 14341:(DOD), ARPA Order No. 4976 under Contract F33615-87-C-1499. 13975: 12766: 12470: 9047: 6110: 5171: 3001: 2976:

ANNs serve as the learning component in such applications.

16781: 13884: 8535: 1903: 1143:, which are correlated with "nodes" that represent visual 1076:), possibly passing through multiple intermediate layers ( 14464:

Computational intelligence: a methodological introduction

14461: 14206:"Approximation by Superpositions of a Sigmoidal function" 11755:"Artificial Neural Network for Modelling Rainfall-Runoff" 11509: 10622: 10242: 10083:

6th International Symposium on Neural Networks, ISNN 2009

9890: 8125: 7963:

International Joint Conference on Artificial Intelligence

7077:"Gradient-based learning applied to document recognition" 1701: 13928: 13716:

Advances in Economics, Management and Political Sciences

11876: 10186:

Ting Qin, Haitao Zhang, Zonghai Chen, Wei Xiang (2005).

9838:"Artificial intelligence can 'evolve' to solve problems" 9647:

Proceedings of 2000 Congress on Evolutionary Computation

5856:. No. 34. Princeton University Press. pp. 3–41 4742:

and GPUs can reduce training times from months to days.

4681:

unreadable table...valueless as a scientific resource".

1189:

Today's deep neural networks are based on early work in

945:

List of datasets in computer vision and image processing

12883:

Journal of Statistical Mechanics: Theory and Experiment

11083: 9814: 9492: 8392: 8074: 6773: 4382:

which may lead the convergence to the wrong direction.

3040:

can create neural network topologies and weights using

1792:

Transformer (deep learning architecture) § History

1587:

connections to solve it. He and Schmidhuber introduced

14429:

Information theory, inference, and learning algorithms

13482:"Google Built Its Very Own Chips to Power Its AI Bots" 12934:

Arthur Jacot, Franck Gabriel, Clement Hongler (2018).

12880: 12705:

Information Theory, Inference, and Learning Algorithms

10572: 10514:"Photo-Real Talking Head with Deep Bidirectional LSTM" 7582: 6419:

Perceptrons: An Introduction to Computational Geometry

6394: 5726:(3rd ed.). Upper Saddle River, NJ: Prentice Hall. 4884: 4828: 2904: 2851: 2798: 2736: 2674: 2581: 2559: 2529: 2464: 2442: 2405: 2377: 14118: 13213: 10843:

Dynamic Data Assimilation - Beating the Uncertainties

10780:

Tian J, Tan Y, Sun C, Zeng J, Jin Y (December 2016).

10299: 10081:. In Wang, H., Shen, Y., Huang, T., Zeng, Z. (eds.). 8735: 8716: 8698:

Katharopoulos A, Vyas A, Pappas N, Fleuret F (2020).

8415:"Prepare, Don't Panic: Synthetic Media and Deepfakes" 7616:

Ackley DH, Hinton GE, Sejnowski TJ (1 January 1985).

7615: 7536: 7420:

Habilitation thesis: System modeling and optimization

6776:"Learning representations by back-propagating errors" 6774:

Rumelhart DE, Hinton GE, Williams RJ (October 1986).

4883: 4856: 4827: 4540: 4207:

models for specific financial long-run forecasts and

4158:, object recognition, and sequential decision making) 2903: 2850: 2797: 2735: 2673: 2580: 2558: 2528: 2463: 2441: 2404: 2376: 1991:. The layer that produces the ultimate result is the 13611: 11365:"Facebook Boosts A.I. to Block Terrorist Propaganda" 9758: 9457:"Genetic reinforcement learning for neural networks" 8386: 8002:

Advances in Neural Information Processing Systems 25

7706: 6885:

Phoneme Recognition Using Time-Delay Neural Networks

6863:

Phoneme Recognition Using Time-Delay Neural Networks

6325:

IEEE Transactions on Systems Science and Cybernetics

5654: 5401:

A Brief Introduction to Neural Networks (D. Kriesel)

4285:

in physics and simulate the properties of many-body

13542: 11911: 11883:

Artificial Intelligent Systems and Machine Learning

11612:

Computer-Aided Civil and Infrastructure Engineering

11084:Maitra DS, Bhattacharya U, Parui SK (August 2015). 9793: 9519: 9403:

Engineering Applications of Artificial Intelligence

7781: 6616:(1960). "Gradient theory of optimal flight paths". 6304:(1967). "A theory of adaptive pattern classifier". 4715:of neurons, simulating even a simplified neuron on 1744:over output patterns. The second network learns by 1031:An ANN consists of connected units or nodes called 11753:Tayebiyan A, Mohammad TA, Ghazali AH, Mashohor S. 11510:Alizadeh E, Lyons SM, Castle JM, Prasad A (2016). 10839:"Data Processing Using Artificial Neural Networks" 9455: 9048:Kelleher JD, Mac Namee B, D'Arcy A (2020). "7-8". 8778:] (in German) (1st ed.). Addison-Wesley. 8360: 6229:IEEE Transactions on Systems, Man, and Cybernetics 5778: 4971: 4869: 4842: 4617: 2961: 2889: 2836: 2783: 2721: 2597: 2566: 2536: 2514: 2449: 2427: 2384: 2330:. Each corresponds to a particular learning task. 2147:can be viewed as a straightforward application of 1301:Deep learning breakthroughs in the 1960s and 1970s 17865:Mathematical and quantitative methods (economics) 17286:, self-driving vehicles and enabling technologies 14431:. Cambridge University Press. 25 September 2003. 13885:Kariri E, Louati H, Louati A, Masmoudi F (2023). 11669:Transportation Research Board 97th Annual Meeting 10357: 9400: 8765: 8763: 8465: 8368:"GAN 2.0: NVIDIA's Hyperrealistic Face Generator" 8027: 6698: 6139: 6137: 5477:from the original on 7 November 2021 – via 4990:A two-layer feedforward artificial neural network 2973:(MC). The aim is to discover the lowest-cost MC. 2962:{\displaystyle \textstyle P(s_{t+1}|s_{t},a_{t})} 2784:{\displaystyle \textstyle {a_{1},...,a_{m}}\in A} 2722:{\displaystyle \textstyle {s_{1},...,s_{n}}\in S} 1217:(1795) for the prediction of planetary movement. 17826: 14601: 14403:Introduction to the theory of neural computation 14400: 12578: 12244: 11693: 10896:"Blind source separation: A review and analysis" 10893: 10271: 10037: 9677: 9222: 8511:He K, Zhang X, Ren S, Sun J (10 December 2015). 7410: 6702:Talking Nets: An Oral History of Neural Networks 5901: 4517:stays the same and the network is not modified. 4429:but its sources remain unclear because it lacks 4397: 3153:but its sources remain unclear because it lacks 2275:but its sources remain unclear because it lacks 2080:but its sources remain unclear because it lacks 1654:CNNs on GPU improved performance significantly. 1427: 1385:in 1979, though not trained by backpropagation. 17784:IEEE Intelligent Transportation Systems Society 14309:"The Cascade-Correlation Learning Architecture" 12516:"Scaling deep learning for materials discovery" 11042: 10511: 10403: 8691: 8119: 7442:Untersuchungen zu dynamischen neuronalen Netzen 7368: 7219:Proceedings of the National Academy of Sciences 6820: 2191:While it is possible to define a cost function 1944:which are conceptually derived from biological 1579:'s diploma thesis identified and analyzed the 14306: 12471:Wieczorek S, Filipiak D, Filipowska A (2018). 12297: 11410:International Journal of Computer Applications 11172:French J (2016). "The time traveller's CAPM". 11048: 10836: 10779: 10661: 10655: 10424: 10076: 9052:(2nd ed.). Cambridge, MA: The MIT Press. 8810:(3rd ed.). Addison-Wesley Pub. Co. 1992. 8760: 8646: 8567:. Las Vegas, NV, USA: IEEE. pp. 770–778. 8162: 8008:. Curran Associates, Inc. pp. 2843–2851. 7654:: CS1 maint: DOI inactive as of August 2024 ( 6161: 6134: 5808: 5407:Review of Neural Networks in Materials Science 5156:ANNs such as generative adversarial networks ( 5082: 4332:-valued weights (as opposed to full precision 1105:Neural networks are typically trained through 940:List of datasets for machine-learning research 17268: 16610: 15686: 15210: 14726: 14248: 13365: 13329:"Roger Bridgman's defence of neural networks" 11759:Pertanika Journal of Science & Technology 11694:Díaz E, Brotons V, Tomás R (September 2018). 11662: 11609: 11200: 10307:(1 November 1997). "Long Short-Term Memory". 10188:"Continuous CMAC-QRLS and its systolic array" 8556: 8510: 8489: 8300:. MIT Press/Bradford Books. pp. 222–227. 8286: 7618:"A learning algorithm for boltzmann machines" 7471:A Field Guide to Dynamical Recurrent Networks 7465:Hochreiter S, et al. (15 January 2001). 7206: 6316: 6091: 6089: 4375:parameters, some methods become impractical. 4344:values for weights results in a machine with 3858:# 3. update weights and biases with gradients 1513:in 1972 proposed to modify the weights of an 1065:, which adjusts during the learning process. 973: 17774:VisLab Intercontinental Autonomous Challenge 17145:Intraoperative neurophysiological monitoring 15700: 14211:Mathematics of Control, Signals, and Systems 12483:(Emerging Topics in Semantic Technologies). 12090: 11877:Dwarakish GS, Rakshith S, Natesan U (2013). 10046:. Vol. 63. Elsevier. pp. 527–533. 9895:. Vol. 17, no. 2. pp. 29–48. 9142: 8971: 8710: 8560:Deep Residual Learning for Image Recognition 8529: 8513:Deep Residual Learning for Image Recognition 8304: 8126:Krizhevsky A, Sutskever I, Hinton G (2012). 6873: 6579: 6528: 6526: 6524: 6415: 6349: 6264: 5745: 5743: 5741: 5739: 5737: 5735: 5733: 5597: 4760: 4676:Technology writer Roger Bridgman commented: 3047: 3004:and other sequential decision making tasks. 1865:. There might be a discussion about this on 1642:by Dan Ciresan, Ueli Meier, Jonathan Masci, 1220:Historically, digital computers such as the 14376:Neural networks: a comprehensive foundation 14214:. Springer International. pp. 303–314. 14073: 12968: 12049: 11814: 11787: 11051:"Introduction to Neural Net Machine Vision" 10722: 10690:Esch R (1990). "Functional Approximation". 10621:Haifeng Jin, Qingquan Song, Xia Hu (2019). 10430: 10131:"A learning algorithm of CMAC based on RLS" 9101: 8859: 8652: 8625: 8550: 8310: 8292: 7436: 7434: 7416: 7374: 7349: 7343: 6727: 6606: 6532: 6441: 6439: 6352:Applied and Computational Harmonic Analysis 6258: 6011: 5749: 5673: 4281:ANNs have been proposed as a tool to solve 2522:. Minimizing this cost produces a value of 1439:(CNN) architecture of 1979 also introduced 1344:the currently dominant training technique. 17275: 17261: 16617: 16603: 15693: 15679: 15217: 15203: 14733: 14719: 13318:. Nasa.gov. Retrieved on 20 November 2013. 12419: 9016:Indian Journal of Computer and Engineering 9006: 8824: 8253: 8251: 7464: 6853: 6660: 6642: 6219: 6188: 6143: 6086: 6034: 5964: 5691:"Gauss and the Invention of Least Squares" 1020:inspired by the structure and function of 980: 966: 14663: 14619: 14339:Defense Advanced Research Projects Agency 14289: 14019: 13946: 13902: 13852: 13727: 13683: 13639: 13629: 13576: 13479: 13382: 13241: 13231: 13162: 13129: 13084: 13040: 13022:"On the Spectral Bias of Neural Networks" 12978: 12894: 12763:IEEE Transactions on Electronic Computers 12672: 12555: 12447: 12437: 12372: 12311: 12258: 12197: 11729: 11719: 11623: 11586: 11429: 11362: 10667: 10634: 10605: 10557: 10542: 10459: 10388: 10376: 9965: 9900: 9866: 9821: 9799: 9687: 9572: 9558: 9414: 9237: 9228: 9156: 9086: 8930: 8891: 8729: 8637: 8572: 8541: 8520: 8495: 8475: 8466:Simonyan K, Zisserman A (10 April 2015), 8398: 8327: 8211: 8168: 8084: 7906: 7743: 7668: 7293: 7248: 7238: 7095: 6910: 6546: 6521: 6484: 6445: 6400: 6363: 6322: 6294: 6285: 6114:Artificial Intelligence A Modern Approach 5978: 5758: 5730: 5721: 5715: 5706: 5600:The nature of statistical learning theory 5458: 5056:Recent advancements and future directions 4460:Learn how and when to remove this message 4303: 3372:# 1. random initialize weights and biases 3223:Some of the main breakthroughs include: 3184:Learn how and when to remove this message 2890:{\displaystyle \textstyle P(x_{t}|s_{t})} 2837:{\displaystyle \textstyle P(c_{t}|s_{t})} 2663:Formally the environment is modeled as a 2633: 2306:Learn how and when to remove this message 2111:Learn how and when to remove this message 1899:Mathematics of artificial neural networks 1885:Learn how and when to remove this message 1193:over 200 years ago. The simplest kind of 51:. Here, each circular node represents an 14639:Neural networks for statistical modeling 14604:"Analog computation via neural networks" 14488: 14045:NECSUS_European Journal of Media Studies 13607: 13605: 13512:"Scaling Learning Algorithms towards AI" 12694: 12692: 12658: 11993:The systems and networking group at UCSD 11946: 10894:Pal M, Roy R, Basu J, Bepari MS (2013). 10505: 10468: 10351: 10124: 10122: 10002: 9893:IEEE Computational Intelligence Magazine 9329:. Champaign: Wolfram Media. p. 12. 8800: 8229: 7676:. In Rumelhart DE, McLelland JL (eds.). 7576: 7458: 7431: 7212: 7154: 7140: 7127: 6821:Fukushima K, Miyake S (1 January 1982). 6654: 6636: 6478: 6436: 6343: 6213: 6155: 6049: 6043: 5682: 5572:Pattern Recognition and Machine Learning 5530: 5503: 5384:, and does not reflect subsequent edits. 5367: 4492: 3116:cerebellar model articulation controller 2361: 1963:from the inputs to the neuron. We add a 1902: 1529:observed "recurrent semicircles" in the 38: 14584:Pattern Recognition and Neural Networks 14515: 14221: 14200: 14173: 14149:Neural networks for pattern recognition 13924: 13922: 13880: 13878: 13876: 13874: 13872: 13830: 13828: 13826: 13824: 13822: 13820: 13705: 13703: 13661: 13659: 13419: 13281: 13073:Communications in Computational Physics 12661:IEEE Transactions on Information Theory 12581:"Turing computability with neural nets" 11444: 11403: 11140: 10512:Fan B, Wang L, Soong FK, Xie L (2015). 10404:Fan Y, Qian Y, Xie F, Soong FK (2014). 9749:." Procedia Computer Science p. 255-263 9361: 9322: 9290:. Champaign: Wolfram Media. p. 9. 9283: 8504: 8248: 8225: 8223: 8183: 8177: 7068: 6699:Anderson JA, Rosenfeld E, eds. (2000). 6585: 5815:The Bulletin of Mathematical Biophysics 5809:McCulloch WS, Pitts W (December 1943). 5773: 5767: 5688: 4497:Confidence analysis of a neural network 4328:A specific recurrent architecture with 14: 17827: 14690: 14577: 14542: 14373: 14346: 14146: 14121:"Neural Networks in Materials Science" 13665: 13415: 13413: 13144: 12802: 12698: 11323: 11250: 11171: 10950: 10599: 10474: 9243: 9187: 8865: 8483: 8205: 8199: 7980:from the original on 29 September 2014 7700: 6927: 6891: 6859: 6733: 6612: 6222:"Polynomial theory of complex systems" 6165:Cybernetics and Forecasting Techniques 6111:Russel, Stuart, Norvig, Peter (2010). 6095: 6017: 5931:IRE Transactions on Information Theory 5904:IRE Transactions on Information Theory 5847: 5569: 4501:Supervised neural networks that use a 4481:to minimize the generalization error. 4361:. VC Dimension uses the principles of 4308: 4186:(including directing manipulators and 2333: 17256: 16598: 15674: 15198: 14714: 14636: 14401:Hertz J, Palmer RG, Krogh AS (1991). 14001: 13834: 13709: 13602: 13371:D. J. Felleman and D. C. Van Essen, " 12750: 12689: 12630:"Analog computer trumps Turing model" 12627: 11829:10.1061/(ASCE)1084-0699(2000)5:2(124) 11802:10.1061/(ASCE)1084-0699(2000)5:2(115) 11560: 11344:from the original on 1 September 2023 11305:from the original on 23 November 2018 10224:from the original on 18 November 2018 10179: 10119: 10019:from the original on 19 December 2013 10012:. Springer-Verlag. pp. 517–536. 8830: 8274:from the original on 22 November 2019 7971:10.5591/978-1-57735-516-8/ijcai11-210 7530: 7173: 7027: 6980: 6933: 6897: 6300: 6274:The Annals of Mathematical Statistics 5804: 5802: 5299:Philosophy of artificial intelligence 5264:Large width limits of neural networks 5073: 3196:Two modes of learning are available: 2243: 1935: 1541:Two early influential works were the 1500: 1266:and the Hopfield network. Farley and 1180:History of artificial neural networks 1041:in the brain. These are connected by 17693:Dedicated short-range communications 17235: 16531:Generative adversarial network (GAN) 14666:Advanced methods in neural computing 14042: 13919: 13869: 13817: 13744:from the original on 9 December 2023 13700: 13656: 13492:from the original on 13 January 2018 13052:from the original on 22 October 2022 12732:from the original on 19 October 2016 12181: 12163:from the original on 25 January 2021 11153:from the original on 21 October 2021 11122:from the original on 16 October 2023 10689: 10530:from the original on 1 November 2017 10382: 10358:Sak H, Senior A, Beaufays F (2014). 10293: 9869:Stochastic Models of Neural Networks 9848:from the original on 9 December 2021 9495:Computers & Chemical Engineering 9007:Miljanovic M (February–March 2012). 8977: 8769: 8605: 8557:He K, Zhang X, Ren S, Sun J (2016). 8421:from the original on 2 December 2020 8220: 8144:from the original on 10 January 2017 7587:. Vol. 1999. pp. 850–855. 6409: 5874: 5621: 5619: 5593: 5591: 5239:Efficiently updatable neural network 4769: 4531:The softmax activation function is: 4401: 4336:-valued weights) has the power of a 3125: 2247: 2206: 2052: 1837: 1424:et al. popularized backpropagation. 14740: 14337:, Contract Number EET-8716324, and 14249:Duda RO, Hart PE, Stork DG (2001). 13524:from the original on 12 August 2022 13410: 13195:from the original on 5 October 2021 11893:from the original on 15 August 2017 10643:from the original on 21 August 2019 9561:Computers & Operations Research 9080: 8156: 7992: 7947: 7886: 7775: 6692: 6270:"A Stochastic Approximation Method" 6246:from the original on 29 August 2017 6168:. American Elsevier Publishing Co. 6104: 5151: 5064: 3211:Types of artificial neural networks 2614:problems; the applications include 2350:(also known as classification) and 1995:. In between them are zero or more 1549:(1990), which applied RNN to study 1396:is an efficient application of the 1274:, Holland, Habit and Duda (1956). 935:Glossary of artificial intelligence 24: 17380:Automated emergency braking system 15224: 14889:Evolutionary developmental biology 14493:. California Scientific Software. 14349:An introduction to neural networks 10932:from the original on 20 March 2023 10875:from the original on 20 March 2023 10761:from the original on 20 March 2023 10729:A Concise Guide to Market Research 10167:from the original on 14 April 2021 10150:10.1023/B:NEPL.0000016847.18175.60 9984:from the original on 26 April 2012 9626:from the original on 7 August 2013 9526:. Athena Scientific. p. 512. 9520:Bertsekas D, Tsitsiklis J (1996). 8068: 8015:from the original on 9 August 2017 6755:from the original on 14 April 2016 6039:. Cornell Aeronautical Laboratory. 5799: 5636:from the original on 16 April 2016 5512:from the original on 18 March 2024 5429:Next Generation of Neural Networks 5354: 5091: 4843:{\displaystyle \scriptstyle x_{2}} 4484:The second is to use some form of 4118:(including filtering, clustering, 1783:was published in May 2015 and the 1388: 25: 17881: 17402:Advanced driver-assistance system 17311:Intelligent transportation system 17125:Development of the nervous system 14008:Neural Computing and Applications 13983:from the original on 15 July 2022 12950:from the original on 22 June 2022 12784:from the original on 5 March 2016 12054:. Vol. 3. pp. 621–630. 11817:Journal of Hydrologic Engineering 11790:Journal of Hydrologic Engineering 11675:from the original on 9 March 2018 10996:Computers in Biology and Medicine 10975:from the original on 26 July 2020 9540:from the original on 29 June 2017 8959:from the original on 5 April 2022 8908:"The Machine Learning Dictionary" 7722:(1995). "The Helmholtz machine". 7469:. In Kolen JF, Kremer SC (eds.). 6592:. Open court publishing Company. 5616: 5588: 5551:from the original on 28 July 2022 5335: 5269:List of machine learning concepts 3245: 3026: 2428:{\displaystyle \textstyle f(x)=a} 2238:non-connectionist neural networks 2203:can be used as an inverse cost). 2029:Hyperparameter (machine learning) 2022: 1718:and classification applications. 1468:recognizing handwritten ZIP codes 1454:(TDNN) was introduced in 1987 by 17624:Safe speed automotive common law 17234: 17223: 17222: 16780: 16624: 16569: 16568: 16548: 14602:Siegelmann H, Sontag ED (1994). 14140:10.2355/isijinternational.39.966 14067: 14036: 13995: 13963: 13805:from the original on 19 May 2024 13756: 13593: 13536: 13504: 13473: 13448: 13347: 13321: 13302: 13275: 13266: 13207: 13138: 13117: 13064: 13013: 12962: 12927: 12874: 12844: 12796: 12744: 12652: 12640:from the original on 11 May 2023 12621: 12609:from the original on 19 May 2024 12572: 12507: 12495:from the original on 19 May 2024 12464: 12413: 12352: 12291: 12238: 12175: 12145: 12133:from the original on 19 May 2024 12115: 12103:from the original on 19 May 2024 12091:Ananthaswamy A (19 April 2021). 12084: 12043: 12011: 11981: 11940: 11905: 11870: 11835: 11808: 11781: 11769:from the original on 17 May 2023 11746: 11687: 11656: 11603: 11554: 11542:from the original on 19 May 2024 11503: 11438: 11397: 11385:from the original on 19 May 2024 11356: 11317: 11244: 11165: 11134: 11077: 11065:from the original on 16 May 2013 11022: 11008:10.1016/j.compbiomed.2016.05.013 10987: 10944: 10887: 10830: 10818:from the original on 19 May 2024 10773: 10716: 10683: 10614: 10593: 10566: 10397: 10280:(2016). Slides on Deep Learning 10236: 10070: 10038:Da, Y., Xiurun, G. (July 2005). 10031: 9996: 9958:10.3182/20050703-6-CZ-1902.02172 9933: 9884: 9860: 9830: 9808: 9787: 9752: 9732: 9714: 9671: 9638: 9589: 9552: 9513: 9486: 9447: 9394: 9382:from the original on 19 May 2024 9368:. Wolfram Media Inc. p. 9. 9365:Introduction to Machine Learning 9355: 9343:from the original on 19 May 2024 9326:Introduction to machine learning 9316: 9304:from the original on 19 May 2024 9287:Introduction to machine learning 9277: 9268: 9216: 9181: 9136: 9095: 9074: 9041: 9029:from the original on 19 May 2024 9000: 8900: 8186:"Going deeper with convolutions" 7767: 7487:from the original on 19 May 2024 6746:System modeling and optimization 5724:Linear Algebra With Applications 5534:Comprehensive Biomedical Physics 5366: 5043: 5031: 5019: 5007: 4995: 4983: 4814: 4406: 4198:knowledge discovery in databases 4176:Sensor data analysis (including 4161:Sequence recognition (including 3130: 3007: 2897:and the transition distribution 2252: 2186: 2158: 2057: 1842: 1625: 1493:(matrices) produced by multiple 1373:Deep learning architectures for 1152: 1128: 17316:Context-aware pervasive systems 15023:Ordinary differential equations 14111: 12579:Siegelmann H, Sontag E (1991). 10837:Alaloul WS, Qureshi AH (2019). 10692:Handbook of Applied Mathematics 10493:from the original on 9 May 2021 9256:from the original on 9 May 2021 9106:. Vol. 1. pp. 73–76. 8725:. Springer. pp. 9355–9366. 8599: 8459: 8433: 8407: 8261:Generative Adversarial Networks 7856: 7837: 7662: 7609: 7499: 7318: 7265: 7167: 7021: 6974: 6814: 6767: 6388: 6182: 6162:Ivakhnenko AG, Lapa VG (1967). 6028: 5958: 5949: 5922: 5895: 5868: 5841: 5655:Ferrie, C., Kaiser, S. (2019). 5447:Neural Networks and Information 5294:Parallel distributed processing 4781: 4323:universal approximation theorem 4321:approximator, as proven by the 4126:Nonlinear system identification 4088: 3239:generative adversarial networks 2844:, the observation distribution 2598:{\displaystyle \textstyle f(x)} 1978: 1716:nonlinear system identification 32:Neural network (disambiguation) 17713:Radio-frequency identification 17437:Automated Lane Keeping Systems 16481:Recurrent neural network (RNN) 16471:Differentiable neural computer 15081:Partial differential equations 14587:. Cambridge University Press. 14119:Bhadeshia H. K. D. H. (1999). 13729:10.54254/2754-1169/23/20230367 13672:Scientific and Social Research 13543:Tahmasebi, Hezarkhani (2012). 12391:10.1103/PhysRevLett.122.250503 12330:10.1103/PhysRevLett.122.250502 12216:10.1103/PhysRevLett.122.250501 11934:10.1016/j.geomorph.2004.09.025 11251:Silver D, et al. (2016). 11028:Choy, Christopher B., et al. " 10486:. ICASSP. pp. 4470–4474. 9433:10.1016/j.engappai.2017.01.013 8770:Zell A (2003). "chapter 5.2". 8746:10.18653/v1/2020.emnlp-demos.6 7180:IEEE Transactions on Computers 6748:. Springer. pp. 762–770. 6711:10.7551/mitpress/6626.003.0016 5648: 5563: 5524: 5497: 5459:Sanderson G (5 October 2017). 5442:Performance of Neural Networks 4962: 4946: 4917: 4904: 4509:of network output, assuming a 4369: 4283:partial differential equations 3057:Sherrington–Kirkpatrick models 2955: 2928: 2908: 2883: 2869: 2855: 2830: 2816: 2802: 2591: 2585: 2515:{\displaystyle \textstyle C=E} 2508: 2499: 2495: 2489: 2477: 2474: 2415: 2409: 1722:Generative adversarial network 355:Relevance vector machine (RVM) 13: 1: 17629:Automated lane keeping system 17370:Lane departure warning system 16966:Social cognitive neuroscience 16526:Variational autoencoder (VAE) 16486:Long short-term memory (LSTM) 15753:Computational learning theory 15619:Automation and Remote Control 15602:Programmable logic controller 15501:Distributed parameter systems 15339:Closed-loop transfer function 14307:Fahlman S, Lebiere C (1991). 14300:10.1016/S0031-3203(01)00178-9 13948:10.1080/15228053.2023.2233814 13710:Chang X (13 September 2023). 13145:Xu ZJ, Zhou H (18 May 2021). 12971:Neural Information Processing 12489:10.3233/978-1-61499-894-5-179 11663:Nabian MA, Meidani H (2018). 11466:10.1016/S0140-6736(96)11196-X 11186:10.1080/10293523.2016.1255469 11049:Turek, Fred D. (March 2007). 10077:Wu, J., Chen, E. (May 2009). 9583:10.1016/S0305-0548(99)00146-X 8984:Simulation of Neural Networks 8872:Hydrological Sciences Journal 8776:Simulation of Neural Networks 7634:10.1016/S0364-0213(85)80012-4 5854:Annals of Mathematics Studies 5598:Vapnik VN, Vapnik VN (1998). 5491: 4636: 4398:Generalization and statistics 4274:. ANNs have been employed in 4173:and printed text recognition) 3237:Competitive networks such as 3228:Convolutional neural networks 1796:During the 2010s period, the 1428:Convolutional neural networks 1375:convolutional neural networks 1311:Group method of data handling 1184: 844:Computational learning theory 408:Expectation–maximization (EM) 17749:American Center for Mobility 17718:Automotive navigation system 17506:, formerly Google Car (2012) 17453:Automotive navigation system 17412:Intelligent speed adaptation 17301:History of self-driving cars 16941:Molecular cellular cognition 16506:Convolutional neural network 14621:10.1016/0304-3975(94)90178-3 14608:Theoretical Computer Science 14208:. In van Schuppen JH (ed.). 13772:. IEEE. pp. 2261–2268. 13666:Carina W (27 October 2022). 13631:10.1016/j.patter.2021.100347 13243:10.1016/j.neunet.2019.01.012 12989:10.1007/978-3-030-36708-4_22 12600:10.1016/0893-9659(91)90080-F 11864:10.1016/j.ocemod.2015.08.002 11460:(9076). The Lancet: 469–72. 11363:Schechner S (15 June 2017). 10700:10.1007/978-1-4684-1423-3_17 10052:10.1016/j.neucom.2004.07.002 9507:10.1016/0098-1354(92)80045-B 9202:10.1016/j.neunet.2012.09.020 9167:10.1016/j.neucom.2005.12.126 8338:10.1016/j.neunet.2020.04.008 8044:10.1007/978-3-642-40763-5_51 7162:Journal of molecular biology 7135:Journal of molecular biology 6839:10.1016/0031-3203(82)90024-3 6557:10.1016/j.neunet.2014.09.003 6207:10.1016/0005-1098(70)90092-0 5878:The Organization of Behavior 5570:Bishop CM (17 August 2006). 5506:"Explained: Neural networks" 5504:Hardesty L (14 April 2017). 5002:An artificial neural network 4719:may consume vast amounts of 4631: 4209:artificial financial markets 2567:{\displaystyle \textstyle x} 2537:{\displaystyle \textstyle a} 2450:{\displaystyle \textstyle a} 2385:{\displaystyle \textstyle x} 2183:emphasizes the last change. 1612:restricted Boltzmann machine 1572:in an RNN unfolded in time. 1487:protein structure prediction 1478:-5 (1998), a 7-level CNN by 1462:et al. created a CNN called 1437:convolutional neural network 801:Coefficient of determination 648:Convolutional neural network 360:Support vector machine (SVM) 7: 17407:Driver drowsiness detection 17306:Impact of self-driving cars 17160:Neurodevelopmental disorder 17135:Neural network (biological) 17130:Neural network (artificial) 16501:Multilayer perceptron (MLP) 15660:Supervisory control (SCADA) 14831:Particle swarm optimization 14335:National Science Foundation 14084:10.1109/icicis.2010.5534761 13569:10.1016/j.cageo.2012.02.004 13549:Computers & Geosciences 13282:Dewdney AK (1 April 1997). 12628:Bains S (3 November 1998). 12477:Studies on the Semantic Web 12028:10.1007/978-3-319-73951-9_7 11721:10.1016/j.sandf.2018.08.001 11174:Investment Analysts Journal 10737:10.1007/978-3-662-56707-4_7 10091:10.1007/978-3-642-01513-7_6 9698:10.1007/978-0-387-73299-2_3 9250:Tech. Rep. UTML TR 2010-003 8980:Simulation Neuronaler Netze 8772:Simulation neuronaler Netze 8706:. PMLR. pp. 5156–5165. 8606:Linn A (10 December 2015). 7164:232, no. 2 (1993): 584-599. 7137:202, no. 4 (1988): 865-884. 6416:Minsky M, Papert S (1969). 6189:Ivakhnenko A (March 1970). 6149:Principles of Neurodynamics 5424:Another introduction to ANN 5259:In situ adaptive tabulation 5219:Connectionist expert system 5181: 5138: 5083:Natural language processing 4706: 4522:softmax activation function 4351: 4138:natural resource management 3112:particle swarm optimization 3096:gene expression programming 2990:natural resource management 2548:it could be related to the 2457:is a constant and the cost 2228:stochastic gradient descent 2048: 1812:natural language processing 1342:stochastic gradient descent 1330:stochastic gradient descent 1107:empirical risk minimization 1100: 952:Outline of machine learning 849:Empirical risk minimization 10: 17886: 17850:Computational neuroscience 17840:Artificial neural networks 17647:Self-driving car liability 17551:Automated guideway transit 17385:Collision avoidance system 16687:Computational neuroscience 16577:Artificial neural networks 16491:Gated recurrent unit (GRU) 15717:Differentiable programming 15635:Industrial control systems 15625:Distributed control system 15491:Coefficient diagram method 15400:State space representation 15076:Reaction-diffusion systems 14801:Self-organized criticality 14078:. IEEE. pp. 583–587. 14021:10.1007/s00521-020-05399-0 12713:Cambridge University Press 12540:10.1038/s41586-023-06735-9 12277:10.1103/PhysRevB.99.214306 11989:"Detecting Malicious URLs" 11957:10.1109/IJCNN.2017.7966078 11324:Pasick A (27 March 2023). 11098:10.1109/ICDAR.2015.7333916 10969:10.1016/j.asoc.2015.07.002 10908:10.1109/ICSDA.2013.6709849 10723:Sarstedt M, Moo E (2019). 10651:– via autokeras.com. 10461:10.4249/scholarpedia.32832 10321:10.1162/neco.1997.9.8.1735 8845:10.1016/j.juro.2007.05.122 7844:2012 Kurzweil AI Interview 7636:(inactive 7 August 2024). 7554:10.1162/neco.1997.9.8.1735 7355:"Neural Sequence Chunkers" 7174:Amari SI (November 1972). 6860:Waibel A (December 1987). 6374:10.1016/j.acha.2015.12.005 6064:10.1177/030631296026003005 5657:Neural Networks for Babies 5254:Hyperdimensional computing 5133:risk management strategies 5104: 5100: 4807: 4524:, a generalization of the 4146:(including radar systems, 3276:Neural architecture search 3232:vanishing gradient problem 3208: 3053:Stochastic neural networks 3030: 2980:coupled with ANNs (giving 2643: 2637: 2230:or other methods, such as 2210: 2162: 2122: 2026: 1896: 1789: 1581:vanishing gradient problem 1254:based on the mechanism of 1195:feedforward neural network 1177: 1173: 1093:, and solving problems in 1037:, which loosely model the 1022:biological neural networks 589:Feedforward neural network 340:Artificial neural networks 29: 17845:Classification algorithms 17792: 17779:Eureka Prometheus Project 17759:European Land-Robot Trial 17737: 17726: 17678:Artificial neural network 17653: 17641: 17634:Regulation (EU) 2019/2144 17604: 17541: 17472: 17465: 17425: 17358: 17349: 17326:Smart, connected products 17291: 17218: 17155:Neurodegenerative disease 17112: 16999:Evolutionary neuroscience 16974: 16914: 16789: 16778: 16650: 16632: 16544: 16458: 16402: 16331: 16264: 16136: 16036: 16029: 15983: 15947: 15910:Artificial neural network 15890: 15766: 15733:Automatic differentiation 15706: 15610: 15569: 15551:Perceptual control theory 15486:Artificial neural network 15478: 15443:Digital signal processing 15430: 15321: 15232: 15119: 15066: 15008: 14945: 14912: 14859:Artificial neural network 14844: 14769: 14748: 14695:. Grey House Publishing. 14668:. Van Nostrand Reinhold. 14641:. Van Nostrand Reinhold. 14002:Briot JP (January 2021). 13480:Cade Metz (18 May 2016). 13422:Communications of the ACM 13173:10.1609/aaai.v35i12.17261 13103:10.4208/cicp.OA-2020-0085 12439:10.1186/s12868-015-0162-6 12420:Forrest MD (April 2015). 12060:10.1109/HICSS.1994.323314 12020:Cyber Threat Intelligence 11141:Gessler J (August 2021). 10951:Zissis D (October 2015). 10794:10.1109/SSCI.2016.7850209 10257:10.1162/neco.1989.1.4.541 10207:10.1007/s11063-004-2694-0 10195:Neural Processing Letters 10138:Neural Processing Letters 9523:Neuro-dynamic programming 9470:10.1109/IJCNN.1991.155315 8893:10.1080/02626669809492102 8677:10.1162/neco.1992.4.1.131 8095:10.1109/cvpr.2012.6248110 7745:21.11116/0000-0002-D6D3-E 7736:10.1162/neco.1995.7.5.889 7473:. John Wiley & Sons. 7396:10.1162/neco.1992.4.2.234 7286:10.1177/10738584231179932 6666:BIT Numerical Mathematics 6448:Trans. IECE (In Japanese) 6237:10.1109/TSMC.1971.4308320 6052:Social Studies of Science 5209:Catastrophic interference 4761:Practical counterexamples 4649: 4150:, signal classification, 3048:Stochastic neural network 2620:statistical distributions 2233:extreme learning machines 2125:Mathematical optimization 1833: 1803:Attention Is All You Need 1452:time delay neural network 1416:'s master thesis (1970). 1402:Gottfried Wilhelm Leibniz 1332:was published in 1967 by 1072:) to the last layer (the 1002:artificial neural network 572:Artificial neural network 17835:Computational statistics 17698:Real-time Control System 17577:Freightliner Inspiration 17442:Vehicular ad hoc network 17120:Brain–computer interface 17069:Neuromorphic engineering 16994:Educational neuroscience 16901:Nutritional neuroscience 16806:Clinical neurophysiology 16702:Integrative neuroscience 15738:Neuromorphic engineering 15701:Differentiable computing 15506:Fractional-order control 15276:Model predictive control 14937:Evolutionary game theory 14864:Evolutionary computation 14796:Collective consciousness 13778:10.1109/cvprw.2019.00279 12913:10.1088/1742-5468/abc62b 12775:10.1109/PGEC.1965.264137 10851:10.5772/intechopen.91935 10042:. In T. Villmann (ed.). 9950:16th IFAC World Congress 9944:. In Pavel Zítek (ed.). 9911:10.1109/mci.2022.3155327 9680:Optimization in Medicine 6337:10.1109/TSSC.1969.300225 5943:10.1109/TIT.1956.1056810 5916:10.1109/TIT.1954.1057468 5486: 4795:. For example, in 2018, 4791:, hiring processes, and 4746:Neuromorphic engineering 4717:von Neumann architecture 4515:probability distribution 4415:This section includes a 4338:universal Turing machine 4239:Social network filtering 3309: 3204: 3139:This section includes a 3121: 3118:(CMAC) neural networks. 3104:expectation–maximization 3081: 3076:Bayesian neural networks 3042:evolutionary computation 2261:This section includes a 2222:(the derivative) of the 2066:This section includes a 1742:probability distribution 1355:(rectified linear unit) 1338:internal representations 1324:The first deep learning 1283:Office of Naval Research 881:Journals and conferences 828:Mathematical foundations 738:Temporal difference (TD) 594:Recurrent neural network 514:Conditional random field 437:Dimensionality reduction 185:Dimensionality reduction 147:Quantum machine learning 142:Neuromorphic engineering 102:Self-supervised learning 97:Semi-supervised learning 17860:Mathematical psychology 17526:Yandex self-driving car 17428:the driving environment 17395:Adaptive cruise control 17361:the driving environment 16931:Behavioral neuroscience 16511:Residual neural network 15927:Artificial Intelligence 15516:H-infinity loop-shaping 15496:Control reconfiguration 14955:Social network analysis 14894:Artificial intelligence 14826:Ant colony optimization 14786:Collective intelligence 14693:Artificial intelligence 14053:10.25969/MEDIAREP/14307 13685:10.26689/ssr.v4i10.4402 13460:www.incompleteideas.net 12820:10.1145/3343031.3350545 12361:Physical Review Letters 12300:Physical Review Letters 12185:Physical Review Letters 11370:The Wall Street Journal 9761:Cybernetics and Systems 9738:Bozinovski, S. (2014) " 9655:10.1109/CEC.2000.870269 8808:Artificial intelligence 7815:10.1126/science.7761831 7326:"reverberating circuit" 7192:10.1109/T-C.1972.223477 7084:Proceedings of the IEEE 6924:, 1, pp. 541–551, 1989. 6450:. J62-A (10): 658–665. 6287:10.1214/aoms/1177729586 5531:Yang Z, Yang Z (2014). 5434:24 January 2011 at the 5284:Neural network software 5113:stock market prediction 5014:An ANN dependency graph 4750:physical neural network 4444:more precise citations. 4120:blind source separation 3168:more precise citations. 3014:crossbar adaptive array 2665:Markov decision process 2290:more precise citations. 2095:more precise citations. 1830:use this architecture. 1785:residual neural network 1638:. In 2011, a CNN named 1636:handwriting recognition 1583:and proposed recurrent 1241:artificial intelligence 1203:method of least squares 1095:artificial intelligence 290:Apprenticeship learning 17683:Computer stereo vision 17631:(unece regulation 157) 17359:Human driver monitors 16926:Affective neuroscience 16707:Molecular neuroscience 16662:Behavioral epigenetics 15582:Closed-loop controller 15255:Energy-shaping control 15086:Dissipative structures 14927:Rational choice theory 14251:Pattern classification 12814:. pp. 2709–2710. 11951:. pp. 1871–1878. 11092:. pp. 1021–1025. 10957:Applied Soft Computing 10902:. IEEE. pp. 1–5. 9773:10.1080/01969720118145 8833:The Journal of Urology 8079:. pp. 3642–3649. 7849:31 August 2018 at the 7682:. MIT Press. pp. 7516:Long Short Term Memory 7417:Schmidhuber J (1993). 7375:Schmidhuber J (1992). 7240:10.1073/pnas.79.8.2554 6231:. SMC-1 (4): 364–378. 5785:. Cambridge: Harvard. 5708:10.1214/aos/1176345451 5574:. New York: Springer. 5362: 5342:Listen to this article 5329:Tensor product network 5319:Spiking neural network 5314:Support vector machine 5309:Quantum neural network 5289:Optical neural network 5244:Evolutionary algorithm 5214:Cognitive architecture 5199:Bio-inspired computing 4973: 4871: 4844: 4755:Tensor Processing Unit 4687: 4619: 4594: 4498: 4304:Theoretical properties 4266:, ocean modelling and 4106:time series prediction 4098:Function approximation 3108:non-parametric methods 2963: 2891: 2838: 2785: 2723: 2653:reinforcement learning 2640:Reinforcement learning 2634:Reinforcement learning 2599: 2568: 2538: 2516: 2451: 2429: 2386: 2328:reinforcement learning 2180:adaptive learning rate 2170:Optimizations such as 2153:statistical estimation 2005:directed acyclic graph 1908: 1644:Luca Maria Gambardella 1606:, etc., including the 1589:long short-term memory 1566:knowledge distillation 1505:One origin of RNN was 1290:multilayer perceptrons 1277:In 1958, psychologist 1164:result for sea urchin. 839:Bias–variance tradeoff 721:Reinforcement learning 697:Spiking neural network 107:Reinforcement learning 56: 17769:DARPA Grand Challenge 17592:Mobility as a service 17542:Buses and commercial 16989:Cultural neuroscience 16984:Consumer neuroscience 16826:Neurogastroenterology 16682:Cellular neuroscience 16466:Neural Turing machine 16054:Human image synthesis 15521:Hankel singular value 15465:System identification 15413:analysis & design 14899:Evolutionary robotics 14816:Agent-based modelling 14664:Wasserman PD (1993). 14553:10.1201/9781351043526 14253:(2 ed.). Wiley. 13599:Sun and Bookman, 1990 13288:. Wiley. p. 82. 12127:MIT Technology Review 11700:Soils and Foundations 11055:Vision Systems Design 10725:"Regression Analysis" 10521:Proceedings of ICASSP 10475:Zen H, Sak H (2015). 10287:23 April 2016 at the 9745:23 March 2019 at the 9112:10.1109/CINC.2009.111 7362:TR FKI-148, TU Munich 6220:Ivakhnenko A (1971). 5361: 5234:Digital morphogenesis 5105:Further information: 4974: 4872: 4870:{\displaystyle y_{q}} 4845: 4678: 4620: 4574: 4496: 4315:multilayer perceptron 4110:fitness approximation 2964: 2892: 2839: 2786: 2724: 2607:posterior probability 2600: 2569: 2539: 2517: 2452: 2430: 2387: 2368:unsupervised learning 2362:Unsupervised learning 2324:unsupervised learning 2201:posterior probability 1940:ANNs are composed of 1906: 1897:Further information: 1816:large language models 1712:Radial basis function 1706:distributed computing 1507:statistical mechanics 1326:multilayer perceptron 1258:that became known as 675:Neural radiance field 497:Structured prediction 220:Structured prediction 92:Unsupervised learning 42: 17336:Ambient intelligence 17331:Ubiquitous computing 16961:Sensory neuroscience 16801:Behavioral neurology 16772:Systems neuroscience 16557:Computer programming 16536:Graph neural network 16111:Text-to-video models 16089:Text-to-image models 15937:Large language model 15922:Scientific computing 15728:Statistical manifold 15723:Information geometry 15611:Control applications 15587:Lead-lag compensator 15438:Discrete-time signal 15058:Coupled map lattices 15018:Time series analysis 14960:Small-world networks 13854:10.3390/algor2030973 13379:, 1, pp. 1–47, 1991. 13359:www.iro.umontreal.ca 13314:2 April 2010 at the 12856:The Tensorflow Meter 11035:26 July 2020 at the 9867:Turchetti C (2004), 9567:(11–12): 1201–1225. 8583:10.1109/CVPR.2016.90 8230:Billings SA (2013). 7917:10.1162/neco_a_00052 7669:Smolensky P (1986). 7213:Hopfield JJ (1982). 7151:241, (1988): 223-228 7007:10.1364/AO.30.004211 6960:10.1364/AO.29.004790 6151:. Spartan, New York. 5967:Psychological Review 5722:Bretscher O (1995). 5393:More spoken articles 5304:Predictive analytics 4881: 4854: 4825: 4538: 4287:open quantum systems 4220:General game playing 3284:scikit-learn library 3092:Evolutionary methods 2901: 2848: 2795: 2733: 2671: 2618:, the estimation of 2578: 2556: 2526: 2461: 2439: 2402: 2374: 2010:feedforward networks 1855:confusing or unclear 1675:ImageNet competition 1673:won the large-scale 1620:wake-sleep algorithm 1551:cognitive psychology 1250:proposed a learning 864:Statistical learning 762:Learning with humans 554:Local outlier factor 17810:Anthony Levandowski 17492:General Motors EN-V 17104:Social neuroscience 17004:Global neurosurgery 16881:Neurorehabilitation 16851:Neuro-ophthalmology 16836:Neurointensive care 16667:Behavioral genetics 15903:In-context learning 15743:Pattern recognition 15577:Embedded controller 15546:Minor loop feedback 15479:Advanced techniques 15271:Intelligent control 15038:Population dynamics 14932:Bounded rationality 14874:Genetic programming 14489:Lawrence J (1994). 14437:2003itil.book.....M 14278:Pattern Recognition 14226:. New York: Wiley. 14222:Dewdney AK (1997). 14151:. Clarendon Press. 13904:10.3390/app13053186 13561:2012CG.....42...18T 13456:"The Bitter Lesson" 13395:19 May 2024 at the 13157:(12): 10541–10550. 13095:2020CCoPh..28.1746X 12905:2020JSMTE2020l4002L 12532:2023Natur.624...80M 12383:2019PhRvL.122y0503V 12322:2019PhRvL.122y0502H 12269:2019PhRvB..99u4306Y 12208:2019PhRvL.122y0501N 11926:2005Geomo..66..327E 11856:2015OcMod..94..128P 11712:2018SoFou..58.1414D 11634:2017arXiv170808551N 11516:Integrative Biology 11491:on 23 November 2018 11422:2010IJCA....1z..81G 11280:10.1038/nature16961 11272:2016Natur.529..484S 11222:2009JChPh.131g4104B 10679:2015arXiv150202127C 10575:J. Mach. Learn. Res 10452:2015SchpJ..1032832S 10107:on 31 December 2014 9844:. 10 January 2018. 9610:10.5281/zenodo.7481 9425:2017arXiv170505584O 8912:www.cse.unsw.edu.au 8884:1998HydSJ..43...47D 7807:1995Sci...268.1158H 7801:(5214): 1158–1161. 7593:10.1049/cp:19991218 7231:1982PNAS...79.2554H 7046:1994MedPh..21..517Z 6999:1991ApOpt..30.4211Z 6952:1990ApOpt..29.4790Z 6827:Pattern Recognition 6792:1986Natur.323..533R 6586:Leibniz GW (1920). 5881:. New York: Wiley. 5689:Stigler SM (1981). 5508:. MIT News Office. 5452:9 July 2009 at the 5438:- Google Tech Talks 5412:7 June 2015 at the 4877:, is calculated as 4665:Scientific American 4511:normal distribution 4507:confidence interval 4309:Computational power 4268:coastal engineering 4235:Machine translation 4148:face identification 4144:Pattern recognition 4102:regression analysis 3100:simulated annealing 2978:Dynamic programming 2356:gesture recognition 2348:pattern recognition 2339:Supervised learning 2334:Supervised learning 2320:supervised learning 1973:activation function 1863:clarify the section 1632:pattern recognition 1495:sequence alignments 1357:activation function 1246:In the late 1940s, 1199:mean squared errors 1087:predictive modeling 1056:activation function 707:Electrochemical RAM 614:reservoir computing 345:Logistic regression 264:Supervised learning 250:Multimodal learning 225:Feature engineering 170:Generative modeling 132:Rule-based learning 127:Curriculum learning 87:Supervised learning 62:Part of a series on 17587:Self-driving truck 17582:Driverless tractor 17417:Blind spot monitor 17341:Internet of things 17180:Neuroimmune system 17074:Neurophenomenology 17014:Neural engineering 16737:Neuroendocrinology 16717:Neural engineering 16496:Echo state network 16384:Jürgen Schmidhuber 16079:Facial recognition 16074:Speech recognition 15984:Software libraries 15541:Lyapunov stability 15354:Frequency response 15313:Stochastic control 15184:Computation theory 15179:Information theory 15134:Operationalization 15010:Nonlinear dynamics 14922:Prisoner's dilemma 14869:Genetic algorithms 14516:Masters T (1994). 14405:. Addison-Wesley. 14374:Haykin SS (1999). 14174:Borgelt C (2003). 14147:Bishop CM (1995). 14128:ISIJ International 11642:10.1111/mice.12359 11579:10.1242/bio.013409 11528:10.1039/C6IB00100A 11445:Bottaci L (1997). 11404:Ganesan N (2010). 11330:The New York Times 10309:Neural Computation 10245:Neural Computation 9362:Bernard E (2021). 9323:Bernard E (2021). 9284:Bernard E (2021). 9244:Hinton GE (2010). 8866:Dawson CW (1998). 8740:. pp. 38–45. 8717:Schlag I, Irie K, 8665:Neural Computation 8455:. PMLR: 2256–2265. 8374:. 14 December 2018 8184:Szegedy C (2015). 7895:Neural Computation 7868:www.kurzweilai.net 7724:Neural Computation 7542:Neural Computation 7513:(21 August 1995), 7511:Jürgen Schmidhuber 7447:2015-03-06 at the 7384:Neural Computation 7278:The Neuroscientist 6922:Neural Computation 6678:10.1007/bf01931367 6499:10.1007/bf00344251 6456:10.1007/bf00344251 6268:, Monro S (1951). 6096:Joseph RD (1960). 5827:10.1007/BF02478259 5465:a Neural Network?" 5363: 5204:Blue Brain Project 5111:ANNs are used for 5074:Speech recognition 4969: 4968: 4916: 4867: 4840: 4839: 4789:facial recognition 4615: 4503:mean squared error 4499: 4417:list of references 4319:universal function 4291:individual neurons 4230:Data visualization 4122:, and compression) 3984:"loss: " 3774:sigmoid_derivative 3259:Learning algorithm 3141:list of references 3061:artificial neurons 3055:originating from 2959: 2958: 2887: 2886: 2834: 2833: 2781: 2780: 2719: 2718: 2667:(MDP) with states 2646:Stochastic control 2595: 2594: 2564: 2563: 2550:mutual information 2534: 2533: 2512: 2511: 2447: 2446: 2425: 2424: 2382: 2381: 2344:mean-squared error 2263:list of references 2244:Learning paradigms 2068:list of references 2016:recurrent networks 1942:artificial neurons 1936:Artificial neurons 1909: 1808:Jürgen Schmidhuber 1730:Jürgen Schmidhuber 1648:Jürgen Schmidhuber 1558:Jürgen Schmidhuber 1501:Recurrent networks 1433:Kunihiko Fukushima 1422:David E. Rumelhart 1383:Kunihiko Fukushima 1349:Kunihiko Fukushima 1309:algorithm was the 1045:, which model the 1034:artificial neurons 275: • 190:Density estimation 57: 17822: 17821: 17818: 17817: 17688:Image recognition 17600: 17599: 17461: 17460: 17375:Automatic parking 17284:Self-driving cars 17250: 17249: 17099:Paleoneurobiology 17034:Neuroepistemology 17009:Neuroanthropology 16975:Interdisciplinary 16861:Neuropharmacology 16821:Neuroepidemiology 16592: 16591: 16354:Stephen Grossberg 16327: 16326: 15668: 15667: 15592:Numerical control 15422:Transfer function 15395:Signal-flow graph 15379:Positive feedback 15364:Negative feedback 15359:Laplace transform 15349:Fourier transform 15322:System properties 15303:Real-time control 15293:Nonlinear control 15192: 15191: 15096:Cellular automata 15068:Pattern formation 15000:Adaptive networks 14791:Collective action 14761:Self-organization 14702:978-1-68217-867-6 14691:Wilson H (2018). 14675:978-0-442-00461-3 14648:978-0-442-01310-3 14594:978-0-521-71770-0 14562:978-1-351-04352-6 14543:Maurer H (2021). 14527:978-0-471-04963-0 14500:978-1-883157-00-5 14473:978-1-4471-5012-1 14446:978-0-521-64298-9 14412:978-0-201-51560-2 14385:978-0-13-273350-2 14378:. Prentice Hall. 14358:978-1-85728-673-1 14347:Gurney K (1997). 14284:(10): 2279–2301. 14260:978-0-471-05669-0 14233:978-0-471-10806-1 14185:978-3-528-25265-6 14158:978-0-19-853849-3 14093:978-1-4244-7384-7 13787:978-1-7281-2506-0 13405:978-0-9858757-2-5 13295:978-0-471-10806-1 12998:978-3-030-36707-7 12829:978-1-4503-6889-6 12722:978-0-521-64298-9 12683:10.1109/18.605580 12247:Physical Review B 12069:978-0-8186-5090-1 12037:978-3-319-73951-9 11966:978-1-5090-6182-2 11522:(11): 1183–1193. 11266:(7587): 484–489. 11230:10.1063/1.3206326 11107:978-1-4799-1805-8 10917:978-1-4799-2378-6 10860:978-1-83968-083-0 10803:978-1-5090-4240-1 10746:978-3-662-56706-7 10709:978-1-4684-1423-3 10372:on 24 April 2018. 10100:978-3-642-01215-0 9977:978-3-902661-75-3 9878:978-1-58603-388-0 9727:978-0-444-86488-8 9707:978-0-387-73298-5 9533:978-1-886529-10-6 9375:978-1-57955-048-6 9336:978-1-57955-048-6 9297:978-1-57955-048-6 9121:978-0-7695-3645-3 9059:978-0-262-36110-1 8918:on 26 August 2018 8785:978-3-89319-554-1 8592:978-1-4673-8851-1 8241:978-1-119-94359-4 8104:978-1-4673-1228-8 8053:978-3-642-38708-1 7901:(12): 3207–3220. 7874:on 31 August 2018 7622:Cognitive Science 7480:978-0-7803-5369-5 7440:S. Hochreiter., " 7186:(11): 1197–1206. 7090:(11): 2278–2324. 6786:(6088): 533–536. 6720:978-0-262-26715-1 6705:. The MIT Press. 6429:978-0-262-63022-1 6306:IEEE Transactions 6175:978-0-444-00020-0 6127:978-0-13-604259-4 6018:Werbos P (1975). 5888:978-1-135-63190-1 5848:Kleene S (1956). 5666:978-1-4926-7120-6 5609:978-0-387-94559-0 5581:978-0-387-31073-2 5544:978-0-444-53633-4 5359: 5324:Stochastic parrot 5249:Genetic algorithm 4907: 4770:Hybrid approaches 4660:Alexander Dewdney 4613: 4526:logistic function 4470: 4469: 4462: 4249:Medical diagnosis 4215:Quantum chemistry 4203:Finance (such as 4156:3D reconstruction 4152:novelty detection 3972:"Epoch" 3194: 3193: 3186: 2316: 2315: 2308: 2129:Estimation theory 2121: 2120: 2113: 2007:and are known as 1895: 1894: 1887: 1657:In October 2012, 1616:Helmholtz machine 1608:Boltzmann machine 1531:cerebellar cortex 1472:optical computing 1315:Alexey Ivakhnenko 1256:neural plasticity 1222:von Neumann model 1207:linear regression 990: 989: 795:Model diagnostics 778:Human-in-the-loop 621:Boltzmann machine 534:Anomaly detection 330:Linear regression 245:Ontology learning 240:Grammar induction 215:Semantic analysis 210:Association rules 195:Anomaly detection 137:Neuro-symbolic AI 53:artificial neuron 16:(Redirected from 17877: 17735: 17734: 17486:Spirit of Berlin 17470: 17469: 17426:System monitors 17356: 17355: 17321:Mobile computing 17277: 17270: 17263: 17254: 17253: 17238: 17237: 17226: 17225: 17140:Detection theory 17024:Neurocriminology 16951:Neurolinguistics 16866:Neuroprosthetics 16784: 16747:Neuroinformatics 16697:Imaging genetics 16619: 16612: 16605: 16596: 16595: 16582:Machine learning 16572: 16571: 16552: 16307:Action selection 16297:Self-driving car 16104:Stable Diffusion 16069:Speech synthesis 16034: 16033: 15898:Machine learning 15774:Gradient descent 15695: 15688: 15681: 15672: 15671: 15531:Krener's theorem 15240:Adaptive control 15219: 15212: 15205: 15196: 15195: 15106:Self-replication 14995:Dynamic networks 14884:Machine learning 14811:Phase transition 14735: 14728: 14721: 14712: 14711: 14706: 14687: 14660: 14637:Smith M (1993). 14633: 14623: 14598: 14574: 14539: 14512: 14485: 14458: 14424: 14397: 14370: 14330: 14328: 14326: 14320: 14314:. Archived from 14313: 14303: 14293: 14272: 14245: 14215: 14197: 14170: 14143: 14125: 14106: 14105: 14071: 14065: 14064: 14040: 14034: 14033: 14023: 13999: 13993: 13992: 13990: 13988: 13967: 13961: 13960: 13950: 13926: 13917: 13916: 13906: 13891:Applied Sciences 13882: 13867: 13866: 13856: 13835:Huang Y (2009). 13832: 13815: 13814: 13812: 13810: 13804: 13771: 13760: 13754: 13753: 13751: 13749: 13731: 13707: 13698: 13697: 13687: 13663: 13654: 13653: 13643: 13633: 13609: 13600: 13597: 13591: 13590: 13580: 13540: 13534: 13533: 13531: 13529: 13523: 13516: 13508: 13502: 13501: 13499: 13497: 13477: 13471: 13470: 13468: 13466: 13452: 13446: 13445: 13417: 13408: 13386: 13380: 13369: 13363: 13362: 13351: 13345: 13344: 13342: 13340: 13335:on 19 March 2012 13331:. Archived from 13325: 13319: 13306: 13300: 13299: 13279: 13273: 13270: 13264: 13263: 13245: 13235: 13211: 13205: 13204: 13202: 13200: 13166: 13142: 13136: 13135: 13133: 13121: 13115: 13114: 13088: 13079:(5): 1746–1767. 13068: 13062: 13061: 13059: 13057: 13051: 13044: 13026: 13017: 13011: 13010: 12982: 12966: 12960: 12959: 12957: 12955: 12949: 12942: 12931: 12925: 12924: 12898: 12878: 12872: 12871: 12869: 12867: 12862:on 18 April 2022 12858:. Archived from 12848: 12842: 12841: 12800: 12794: 12793: 12791: 12789: 12783: 12760: 12748: 12742: 12741: 12739: 12737: 12731: 12710: 12696: 12687: 12686: 12676: 12667:(4): 1175–1183. 12656: 12650: 12649: 12647: 12645: 12625: 12619: 12618: 12616: 12614: 12608: 12588:Appl. Math. Lett 12585: 12576: 12570: 12569: 12559: 12511: 12505: 12504: 12502: 12500: 12468: 12462: 12461: 12451: 12441: 12426:BMC Neuroscience 12417: 12411: 12410: 12376: 12356: 12350: 12349: 12315: 12295: 12289: 12288: 12262: 12242: 12236: 12235: 12201: 12179: 12173: 12172: 12170: 12168: 12149: 12143: 12142: 12140: 12138: 12119: 12113: 12112: 12110: 12108: 12088: 12082: 12081: 12047: 12041: 12040: 12015: 12009: 12008: 12006: 12004: 11995:. Archived from 11985: 11979: 11978: 11944: 11938: 11937: 11909: 11903: 11902: 11900: 11898: 11874: 11868: 11867: 11839: 11833: 11832: 11812: 11806: 11805: 11785: 11779: 11778: 11776: 11774: 11750: 11744: 11743: 11733: 11723: 11706:(6): 1414–1422. 11691: 11685: 11684: 11682: 11680: 11660: 11654: 11653: 11627: 11607: 11601: 11600: 11590: 11561:Lyons S (2016). 11558: 11552: 11551: 11549: 11547: 11507: 11501: 11500: 11498: 11496: 11490: 11484:. Archived from 11451: 11442: 11436: 11435: 11433: 11401: 11395: 11394: 11392: 11390: 11360: 11354: 11353: 11351: 11349: 11321: 11315: 11314: 11312: 11310: 11304: 11257: 11248: 11242: 11241: 11204: 11198: 11197: 11169: 11163: 11162: 11160: 11158: 11138: 11132: 11131: 11129: 11127: 11081: 11075: 11074: 11072: 11070: 11046: 11040: 11026: 11020: 11019: 10991: 10985: 10984: 10982: 10980: 10948: 10942: 10941: 10939: 10937: 10891: 10885: 10884: 10882: 10880: 10834: 10828: 10827: 10825: 10823: 10788:. pp. 1–8. 10777: 10771: 10770: 10768: 10766: 10720: 10714: 10713: 10687: 10681: 10673: 10671: 10659: 10653: 10652: 10650: 10648: 10638: 10618: 10612: 10611: 10609: 10597: 10591: 10590: 10570: 10564: 10563: 10561: 10546: 10540: 10539: 10537: 10535: 10529: 10518: 10509: 10503: 10502: 10500: 10498: 10492: 10481: 10472: 10466: 10465: 10463: 10428: 10422: 10421: 10419: 10417: 10401: 10395: 10394: 10392: 10380: 10374: 10373: 10371: 10365:. Archived from 10364: 10355: 10349: 10348: 10315:(8): 1735–1780. 10297: 10291: 10275: 10269: 10268: 10240: 10234: 10233: 10231: 10229: 10223: 10192: 10183: 10177: 10176: 10174: 10172: 10166: 10135: 10126: 10117: 10116: 10114: 10112: 10103:. Archived from 10074: 10068: 10067: 10065: 10063: 10058:on 25 April 2012 10054:. Archived from 10035: 10029: 10028: 10026: 10024: 10018: 10011: 10000: 9994: 9993: 9991: 9989: 9969: 9937: 9931: 9930: 9904: 9888: 9882: 9881: 9864: 9858: 9857: 9855: 9853: 9834: 9828: 9827: 9825: 9812: 9806: 9805: 9803: 9791: 9785: 9784: 9756: 9750: 9736: 9730: 9718: 9712: 9711: 9691: 9675: 9669: 9668: 9642: 9636: 9635: 9633: 9631: 9593: 9587: 9586: 9576: 9556: 9550: 9549: 9547: 9545: 9517: 9511: 9510: 9490: 9484: 9483: 9459: 9451: 9445: 9444: 9418: 9398: 9392: 9391: 9389: 9387: 9359: 9353: 9352: 9350: 9348: 9320: 9314: 9313: 9311: 9309: 9281: 9275: 9272: 9266: 9265: 9263: 9261: 9241: 9235: 9234: 9232: 9220: 9214: 9213: 9185: 9179: 9178: 9160: 9140: 9134: 9133: 9099: 9093: 9092: 9090: 9078: 9072: 9071: 9045: 9039: 9038: 9036: 9034: 9028: 9013: 9004: 8998: 8997: 8975: 8969: 8968: 8966: 8964: 8958: 8943: 8934: 8928: 8927: 8925: 8923: 8914:. Archived from 8904: 8898: 8897: 8895: 8863: 8857: 8856: 8839:(4): 1150–1156. 8828: 8822: 8821: 8804: 8798: 8797: 8767: 8758: 8757: 8733: 8727: 8726: 8714: 8708: 8707: 8695: 8689: 8688: 8662: 8650: 8644: 8643: 8641: 8629: 8623: 8622: 8620: 8618: 8603: 8597: 8596: 8576: 8554: 8548: 8547: 8545: 8533: 8527: 8526: 8524: 8508: 8502: 8501: 8499: 8487: 8481: 8480: 8479: 8463: 8457: 8456: 8446: 8437: 8431: 8430: 8428: 8426: 8411: 8405: 8404: 8402: 8390: 8384: 8383: 8381: 8379: 8372:SyncedReview.com 8364: 8358: 8357: 8331: 8308: 8302: 8301: 8290: 8284: 8283: 8281: 8279: 8273: 8266: 8255: 8246: 8245: 8227: 8218: 8217: 8215: 8203: 8197: 8196: 8190: 8181: 8175: 8174: 8172: 8160: 8154: 8153: 8151: 8149: 8143: 8132: 8123: 8117: 8116: 8088: 8072: 8066: 8065: 8031: 8025: 8024: 8022: 8020: 8014: 8007: 7996: 7990: 7989: 7987: 7985: 7979: 7960: 7951: 7945: 7944: 7910: 7890: 7884: 7883: 7881: 7879: 7870:. Archived from 7860: 7854: 7841: 7835: 7834: 7779: 7773: 7772: 7771: 7765: 7747: 7704: 7698: 7697: 7675: 7666: 7660: 7659: 7653: 7645: 7613: 7607: 7606: 7580: 7574: 7573: 7548:(8): 1735–1780. 7534: 7528: 7527: 7503: 7497: 7496: 7494: 7492: 7462: 7456: 7438: 7429: 7427: 7425: 7414: 7408: 7407: 7381: 7372: 7366: 7365: 7359: 7347: 7341: 7340: 7338: 7336: 7330:Oxford Reference 7322: 7316: 7315: 7297: 7269: 7263: 7262: 7252: 7242: 7225:(8): 2554–2558. 7210: 7204: 7203: 7171: 7165: 7158: 7152: 7144: 7138: 7131: 7125: 7124: 7122: 7120: 7106:10.1109/5.726791 7099: 7081: 7072: 7066: 7065: 7054:10.1118/1.597177 7028:Zhang W (1994). 7025: 7019: 7018: 6981:Zhang W (1991). 6978: 6972: 6971: 6934:Zhang W (1990). 6931: 6925: 6914: 6908: 6907: 6898:Zhang W (1988). 6895: 6889: 6880:Alexander Waibel 6877: 6871: 6870: 6868: 6857: 6851: 6850: 6818: 6812: 6811: 6800:10.1038/323533a0 6771: 6765: 6764: 6762: 6760: 6754: 6743: 6731: 6725: 6724: 6696: 6690: 6689: 6658: 6652: 6651: 6640: 6634: 6633: 6610: 6604: 6603: 6583: 6577: 6576: 6550: 6530: 6519: 6518: 6482: 6476: 6475: 6443: 6434: 6433: 6413: 6407: 6406: 6404: 6392: 6386: 6385: 6367: 6347: 6341: 6340: 6320: 6314: 6313: 6298: 6292: 6291: 6289: 6262: 6256: 6255: 6253: 6251: 6245: 6226: 6217: 6211: 6210: 6186: 6180: 6179: 6159: 6153: 6152: 6141: 6132: 6131: 6119: 6108: 6102: 6101: 6093: 6084: 6083: 6047: 6041: 6040: 6032: 6026: 6025: 6015: 6009: 6008: 5989:10.1037/h0042519 5982: 5962: 5956: 5953: 5947: 5946: 5926: 5920: 5919: 5899: 5893: 5892: 5872: 5866: 5865: 5863: 5861: 5845: 5839: 5838: 5806: 5797: 5796: 5784: 5771: 5765: 5764: 5762: 5747: 5728: 5727: 5719: 5713: 5712: 5710: 5686: 5680: 5677: 5671: 5670: 5652: 5646: 5645: 5643: 5641: 5623: 5614: 5613: 5595: 5586: 5585: 5567: 5561: 5560: 5558: 5556: 5528: 5522: 5521: 5519: 5517: 5501: 5482: 5383: 5381: 5370: 5369: 5360: 5350: 5348: 5343: 5229:Deep image prior 5152:Content creation 5065:Image processing 5047: 5035: 5023: 5011: 4999: 4987: 4978: 4976: 4975: 4970: 4961: 4960: 4945: 4944: 4929: 4928: 4915: 4894: 4893: 4876: 4874: 4873: 4868: 4866: 4865: 4849: 4847: 4846: 4841: 4838: 4837: 4818: 4727:power and time. 4624: 4622: 4621: 4616: 4614: 4612: 4611: 4610: 4609: 4608: 4593: 4588: 4572: 4571: 4570: 4569: 4555: 4550: 4549: 4475:cross-validation 4473:first is to use 4465: 4458: 4454: 4451: 4445: 4440:this section by 4431:inline citations 4410: 4409: 4402: 4130:adaptive control 4084: 4081: 4078: 4075: 4072: 4069: 4066: 4063: 4060: 4057: 4054: 4051: 4048: 4045: 4042: 4039: 4036: 4033: 4030: 4027: 4024: 4021: 4018: 4015: 4012: 4009: 4006: 4003: 4000: 3997: 3994: 3991: 3988: 3985: 3982: 3979: 3976: 3973: 3970: 3967: 3964: 3961: 3958: 3955: 3952: 3949: 3946: 3943: 3940: 3937: 3934: 3931: 3928: 3925: 3922: 3919: 3916: 3913: 3910: 3907: 3904: 3901: 3898: 3895: 3892: 3889: 3886: 3883: 3880: 3877: 3874: 3871: 3868: 3865: 3862: 3859: 3856: 3853: 3850: 3847: 3844: 3841: 3838: 3835: 3832: 3829: 3826: 3823: 3820: 3817: 3814: 3811: 3808: 3805: 3802: 3799: 3796: 3793: 3790: 3787: 3784: 3781: 3778: 3775: 3772: 3769: 3766: 3763: 3760: 3757: 3754: 3751: 3748: 3745: 3742: 3739: 3736: 3733: 3730: 3727: 3724: 3721: 3718: 3715: 3712: 3709: 3706: 3703: 3700: 3697: 3694: 3691: 3688: 3685: 3682: 3679: 3676: 3673: 3670: 3667: 3664: 3661: 3658: 3655: 3652: 3649: 3646: 3643: 3640: 3637: 3634: 3631: 3628: 3625: 3622: 3619: 3616: 3613: 3610: 3607: 3604: 3601: 3598: 3595: 3592: 3589: 3586: 3583: 3580: 3577: 3574: 3571: 3568: 3565: 3562: 3559: 3556: 3553: 3550: 3547: 3544: 3541: 3538: 3535: 3532: 3529: 3526: 3523: 3520: 3517: 3514: 3511: 3508: 3505: 3502: 3499: 3496: 3493: 3490: 3487: 3484: 3481: 3478: 3475: 3472: 3469: 3466: 3463: 3460: 3457: 3454: 3451: 3448: 3445: 3442: 3439: 3436: 3433: 3430: 3427: 3424: 3421: 3418: 3415: 3412: 3409: 3406: 3403: 3400: 3397: 3394: 3391: 3388: 3385: 3382: 3379: 3376: 3373: 3370: 3367: 3364: 3361: 3358: 3355: 3352: 3349: 3346: 3343: 3340: 3337: 3334: 3331: 3328: 3325: 3322: 3319: 3316: 3313: 3189: 3182: 3178: 3175: 3169: 3164:this section by 3155:inline citations 3134: 3133: 3126: 2968: 2966: 2965: 2960: 2954: 2953: 2941: 2940: 2931: 2926: 2925: 2896: 2894: 2893: 2888: 2882: 2881: 2872: 2867: 2866: 2843: 2841: 2840: 2835: 2829: 2828: 2819: 2814: 2813: 2790: 2788: 2787: 2782: 2773: 2772: 2771: 2747: 2746: 2728: 2726: 2725: 2720: 2711: 2710: 2709: 2685: 2684: 2604: 2602: 2601: 2596: 2573: 2571: 2570: 2565: 2543: 2541: 2540: 2535: 2521: 2519: 2518: 2513: 2507: 2506: 2456: 2454: 2453: 2448: 2434: 2432: 2431: 2426: 2391: 2389: 2388: 2383: 2311: 2304: 2300: 2297: 2291: 2286:this section by 2277:inline citations 2256: 2255: 2248: 2133:Machine learning 2116: 2109: 2105: 2102: 2096: 2091:this section by 2082:inline citations 2061: 2060: 2053: 1890: 1883: 1879: 1876: 1870: 1846: 1845: 1838: 1770:Stable Diffusion 1762:Diffusion models 1746:gradient descent 1738:generative model 1683:Andrew Zisserman 1523:Hopfield network 1519:Hebbian learning 1414:Seppo Linnainmaa 1279:Frank Rosenblatt 1260:Hebbian learning 1233:Warren McCulloch 1156: 1132: 1091:adaptive control 994:machine learning 982: 975: 968: 929:Related articles 806:Confusion matrix 559:Isolation forest 504:Graphical models 283: 282: 235:Learning to rank 230:Feature learning 68:Machine learning 59: 58: 21: 18:Neural computing 17885: 17884: 17880: 17879: 17878: 17876: 17875: 17874: 17855:Market research 17825: 17824: 17823: 17814: 17788: 17741: 17739: 17738:Organizations, 17730: 17729:Projects & 17728: 17727:Organizations, 17722: 17655: 17649: 17637: 17596: 17543: 17537: 17520:LUTZ Pathfinder 17457: 17429: 17427: 17421: 17362: 17360: 17345: 17293: 17287: 17281: 17251: 17246: 17214: 17200:Neurotechnology 17195:Neuroplasticity 17190:Neuromodulation 17185:Neuromanagement 17108: 17079:Neurophilosophy 16976: 16970: 16956:Neuropsychology 16917: 16910: 16871:Neuropsychiatry 16831:Neuroimmunology 16816:Neurocardiology 16792: 16785: 16776: 16767:Neurophysiology 16757:Neuromorphology 16712:Neural decoding 16653: 16646: 16628: 16623: 16593: 16588: 16540: 16454: 16420:Google DeepMind 16398: 16364:Geoffrey Hinton 16323: 16260: 16186:Project Debater 16132: 16030:Implementations 16025: 15979: 15943: 15886: 15828:Backpropagation 15762: 15748:Tensor calculus 15702: 15699: 15669: 15664: 15650:Process control 15630:Electric motors 15606: 15565: 15474: 15431:Digital control 15426: 15417:System dynamics 15344:Controllability 15317: 15298:Optimal control 15250:Digital control 15228: 15223: 15193: 15188: 15154:System dynamics 15115: 15101:Spatial ecology 15062: 15004: 14990:Systems biology 14941: 14908: 14879:Artificial life 14849: 14840: 14836:Swarm behaviour 14821:Synchronization 14781:Social dynamics 14772: 14765: 14744: 14742:Complex systems 14739: 14709: 14703: 14676: 14649: 14595: 14563: 14528: 14501: 14474: 14447: 14427: 14413: 14386: 14359: 14324: 14322: 14318: 14311: 14261: 14234: 14186: 14159: 14134:(10): 966–979. 14123: 14114: 14109: 14094: 14072: 14068: 14041: 14037: 14000: 13996: 13986: 13984: 13969: 13968: 13964: 13927: 13920: 13883: 13870: 13847:(3): 973–1007. 13833: 13818: 13808: 13806: 13802: 13788: 13769: 13761: 13757: 13747: 13745: 13708: 13701: 13664: 13657: 13610: 13603: 13598: 13594: 13541: 13537: 13527: 13525: 13521: 13514: 13510: 13509: 13505: 13495: 13493: 13478: 13474: 13464: 13462: 13454: 13453: 13449: 13434:10.1145/2771283 13418: 13411: 13397:Wayback Machine 13387: 13383: 13377:Cerebral Cortex 13370: 13366: 13353: 13352: 13348: 13338: 13336: 13327: 13326: 13322: 13316:Wayback Machine 13307: 13303: 13296: 13280: 13276: 13271: 13267: 13220:Neural Networks 13212: 13208: 13198: 13196: 13143: 13139: 13122: 13118: 13069: 13065: 13055: 13053: 13049: 13024: 13018: 13014: 12999: 12967: 12963: 12953: 12951: 12947: 12940: 12932: 12928: 12879: 12875: 12865: 12863: 12850: 12849: 12845: 12830: 12801: 12797: 12787: 12785: 12781: 12758: 12749: 12745: 12735: 12733: 12729: 12723: 12708: 12697: 12690: 12674:10.1.1.411.7782 12657: 12653: 12643: 12641: 12626: 12622: 12612: 12610: 12606: 12583: 12577: 12573: 12526:(7990): 80–85. 12512: 12508: 12498: 12496: 12469: 12465: 12418: 12414: 12357: 12353: 12296: 12292: 12243: 12239: 12180: 12176: 12166: 12164: 12151: 12150: 12146: 12136: 12134: 12121: 12120: 12116: 12106: 12104: 12097:Quanta Magazine 12089: 12085: 12070: 12048: 12044: 12038: 12016: 12012: 12002: 12000: 11999:on 14 July 2019 11987: 11986: 11982: 11967: 11945: 11941: 11910: 11906: 11896: 11894: 11875: 11871: 11844:Ocean Modelling 11840: 11836: 11813: 11809: 11786: 11782: 11772: 11770: 11751: 11747: 11692: 11688: 11678: 11676: 11661: 11657: 11608: 11604: 11559: 11555: 11545: 11543: 11508: 11504: 11494: 11492: 11488: 11449: 11443: 11439: 11431:10.5120/476-783 11402: 11398: 11388: 11386: 11361: 11357: 11347: 11345: 11322: 11318: 11308: 11306: 11302: 11255: 11249: 11245: 11205: 11201: 11170: 11166: 11156: 11154: 11139: 11135: 11125: 11123: 11108: 11082: 11078: 11068: 11066: 11047: 11043: 11037:Wayback Machine 11027: 11023: 10992: 10988: 10978: 10976: 10949: 10945: 10935: 10933: 10918: 10892: 10888: 10878: 10876: 10861: 10835: 10831: 10821: 10819: 10804: 10778: 10774: 10764: 10762: 10747: 10721: 10717: 10710: 10688: 10684: 10660: 10656: 10646: 10644: 10619: 10615: 10598: 10594: 10571: 10567: 10547: 10543: 10533: 10531: 10527: 10516: 10510: 10506: 10496: 10494: 10490: 10479: 10473: 10469: 10436:"Deep Learning" 10429: 10425: 10415: 10413: 10402: 10398: 10381: 10377: 10369: 10362: 10356: 10352: 10298: 10294: 10289:Wayback Machine 10276: 10272: 10241: 10237: 10227: 10225: 10221: 10190: 10184: 10180: 10170: 10168: 10164: 10133: 10127: 10120: 10110: 10108: 10101: 10075: 10071: 10061: 10059: 10036: 10032: 10022: 10020: 10016: 10009: 10001: 9997: 9987: 9985: 9978: 9938: 9934: 9889: 9885: 9879: 9865: 9861: 9851: 9849: 9836: 9835: 9831: 9813: 9809: 9792: 9788: 9757: 9753: 9747:Wayback Machine 9737: 9733: 9719: 9715: 9708: 9689:10.1.1.137.8288 9676: 9672: 9665: 9643: 9639: 9629: 9627: 9620: 9594: 9590: 9574:10.1.1.392.4034 9557: 9553: 9543: 9541: 9534: 9518: 9514: 9491: 9487: 9480: 9452: 9448: 9399: 9395: 9385: 9383: 9376: 9360: 9356: 9346: 9344: 9337: 9321: 9317: 9307: 9305: 9298: 9282: 9278: 9273: 9269: 9259: 9257: 9242: 9238: 9221: 9217: 9190:Neural Networks 9186: 9182: 9158:10.1.1.217.3692 9141: 9137: 9122: 9100: 9096: 9079: 9075: 9060: 9046: 9042: 9032: 9030: 9026: 9011: 9005: 9001: 8994: 8978:Zell A (1994). 8976: 8972: 8962: 8960: 8956: 8941: 8935: 8931: 8921: 8919: 8906: 8905: 8901: 8864: 8860: 8829: 8825: 8818: 8806: 8805: 8801: 8786: 8768: 8761: 8734: 8730: 8715: 8711: 8696: 8692: 8660: 8651: 8647: 8630: 8626: 8616: 8614: 8604: 8600: 8593: 8555: 8551: 8534: 8530: 8509: 8505: 8488: 8484: 8464: 8460: 8444: 8438: 8434: 8424: 8422: 8417:. witness.org. 8413: 8412: 8408: 8391: 8387: 8377: 8375: 8366: 8365: 8361: 8316:Neural Networks 8309: 8305: 8291: 8287: 8277: 8275: 8271: 8264: 8256: 8249: 8242: 8228: 8221: 8204: 8200: 8188: 8182: 8178: 8161: 8157: 8147: 8145: 8141: 8130: 8124: 8120: 8105: 8073: 8069: 8054: 8032: 8028: 8018: 8016: 8012: 8005: 7997: 7993: 7983: 7981: 7977: 7958: 7952: 7948: 7891: 7887: 7877: 7875: 7862: 7861: 7857: 7851:Wayback Machine 7842: 7838: 7780: 7776: 7766: 7705: 7701: 7694: 7673: 7667: 7663: 7647: 7646: 7614: 7610: 7603: 7581: 7577: 7535: 7531: 7507:Sepp Hochreiter 7504: 7500: 7490: 7488: 7481: 7463: 7459: 7449:Wayback Machine 7439: 7432: 7423: 7415: 7411: 7379: 7373: 7369: 7357: 7348: 7344: 7334: 7332: 7324: 7323: 7319: 7270: 7266: 7211: 7207: 7172: 7168: 7159: 7155: 7145: 7141: 7132: 7128: 7118: 7116: 7079: 7073: 7069: 7034:Medical Physics 7026: 7022: 6979: 6975: 6932: 6928: 6915: 6911: 6896: 6892: 6878: 6874: 6866: 6858: 6854: 6819: 6815: 6772: 6768: 6758: 6756: 6752: 6741: 6732: 6728: 6721: 6697: 6693: 6659: 6655: 6641: 6637: 6624:(10): 947–954. 6611: 6607: 6600: 6584: 6580: 6535:Neural Networks 6531: 6522: 6483: 6479: 6444: 6437: 6430: 6414: 6410: 6393: 6389: 6348: 6344: 6321: 6317: 6299: 6295: 6263: 6259: 6249: 6247: 6243: 6224: 6218: 6214: 6187: 6183: 6176: 6160: 6156: 6142: 6135: 6128: 6117: 6109: 6105: 6094: 6087: 6048: 6044: 6037:Report 85-460-1 6033: 6029: 6016: 6012: 5980:10.1.1.588.3775 5963: 5959: 5954: 5950: 5927: 5923: 5900: 5896: 5889: 5875:Hebb D (1949). 5873: 5869: 5859: 5857: 5846: 5842: 5807: 5800: 5793: 5772: 5768: 5748: 5731: 5720: 5716: 5687: 5683: 5678: 5674: 5667: 5659:. Sourcebooks. 5653: 5649: 5639: 5637: 5624: 5617: 5610: 5596: 5589: 5582: 5568: 5564: 5554: 5552: 5545: 5529: 5525: 5515: 5513: 5502: 5498: 5494: 5489: 5454:Wayback Machine 5436:Wayback Machine 5414:Wayback Machine 5397: 5396: 5385: 5379: 5377: 5374:This audio file 5371: 5364: 5355: 5352: 5346: 5345: 5341: 5338: 5333: 5184: 5154: 5145:medical imaging 5141: 5109: 5103: 5094: 5092:Control systems 5085: 5076: 5067: 5058: 5051: 5048: 5039: 5036: 5027: 5024: 5015: 5012: 5003: 5000: 4991: 4988: 4979: 4956: 4952: 4937: 4933: 4924: 4920: 4911: 4889: 4885: 4882: 4879: 4878: 4861: 4857: 4855: 4852: 4851: 4833: 4829: 4826: 4823: 4822: 4819: 4810: 4793:law enforcement 4784: 4772: 4763: 4709: 4652: 4639: 4634: 4628: 4604: 4600: 4599: 4595: 4589: 4578: 4573: 4565: 4561: 4560: 4556: 4554: 4545: 4541: 4539: 4536: 4535: 4520:By assigning a 4479:hyperparameters 4466: 4455: 4449: 4446: 4435: 4421:related reading 4411: 4407: 4400: 4372: 4354: 4311: 4306: 4134:process control 4116:Data processing 4112:, and modeling) 4091: 4083: 4082: 4079: 4076: 4073: 4070: 4067: 4064: 4061: 4058: 4055: 4052: 4049: 4046: 4043: 4040: 4037: 4034: 4031: 4028: 4025: 4022: 4019: 4016: 4013: 4010: 4007: 4004: 4001: 3998: 3995: 3992: 3989: 3986: 3983: 3980: 3977: 3974: 3971: 3968: 3965: 3962: 3959: 3956: 3953: 3950: 3947: 3944: 3941: 3938: 3935: 3932: 3929: 3926: 3923: 3920: 3917: 3914: 3911: 3908: 3905: 3902: 3899: 3896: 3893: 3890: 3887: 3884: 3881: 3878: 3875: 3872: 3869: 3866: 3863: 3860: 3857: 3854: 3851: 3848: 3845: 3842: 3839: 3836: 3833: 3830: 3827: 3824: 3821: 3818: 3815: 3812: 3809: 3806: 3803: 3800: 3797: 3794: 3791: 3788: 3785: 3782: 3779: 3776: 3773: 3770: 3767: 3764: 3761: 3758: 3755: 3752: 3749: 3746: 3743: 3740: 3737: 3734: 3731: 3728: 3725: 3722: 3719: 3716: 3713: 3710: 3707: 3704: 3701: 3698: 3695: 3692: 3689: 3686: 3683: 3680: 3677: 3674: 3671: 3668: 3665: 3662: 3659: 3656: 3653: 3650: 3647: 3644: 3641: 3638: 3635: 3632: 3629: 3626: 3623: 3620: 3617: 3614: 3611: 3608: 3605: 3602: 3599: 3596: 3593: 3590: 3587: 3584: 3581: 3578: 3575: 3572: 3569: 3566: 3563: 3560: 3557: 3554: 3551: 3548: 3545: 3542: 3539: 3536: 3533: 3530: 3527: 3524: 3521: 3518: 3515: 3512: 3509: 3506: 3503: 3500: 3497: 3494: 3491: 3488: 3485: 3482: 3479: 3476: 3473: 3470: 3467: 3464: 3461: 3458: 3455: 3452: 3449: 3446: 3443: 3440: 3437: 3434: 3431: 3428: 3425: 3422: 3419: 3416: 3413: 3410: 3407: 3404: 3401: 3398: 3395: 3392: 3389: 3386: 3383: 3380: 3377: 3374: 3371: 3368: 3365: 3362: 3359: 3356: 3353: 3350: 3347: 3344: 3341: 3338: 3335: 3332: 3329: 3326: 3323: 3320: 3317: 3314: 3311: 3303: 3298:Hyperparameters 3282:and AutoKeras. 3263:hyperparameters 3248: 3213: 3207: 3190: 3179: 3173: 3170: 3159: 3145:related reading 3135: 3131: 3124: 3084: 3050: 3035: 3029: 3020: 3010: 2988:, video games, 2986:vehicle routing 2949: 2945: 2936: 2932: 2927: 2915: 2911: 2902: 2899: 2898: 2877: 2873: 2868: 2862: 2858: 2849: 2846: 2845: 2824: 2820: 2815: 2809: 2805: 2796: 2793: 2792: 2767: 2763: 2742: 2738: 2737: 2734: 2731: 2730: 2705: 2701: 2680: 2676: 2675: 2672: 2669: 2668: 2648: 2642: 2636: 2579: 2576: 2575: 2557: 2554: 2553: 2527: 2524: 2523: 2502: 2498: 2462: 2459: 2458: 2440: 2437: 2436: 2403: 2400: 2399: 2375: 2372: 2371: 2364: 2336: 2312: 2301: 2295: 2292: 2281: 2267:related reading 2257: 2253: 2246: 2215: 2213:Backpropagation 2209: 2207:Backpropagation 2189: 2167: 2161: 2135: 2117: 2106: 2100: 2097: 2086: 2072:related reading 2062: 2058: 2051: 2031: 2025: 1981: 1938: 1901: 1891: 1880: 1874: 1871: 1860: 1847: 1843: 1836: 1794: 1781:highway network 1671:Geoffrey Hinton 1663:Alex Krizhevsky 1628: 1604:Geoffrey Hinton 1596:Terry Sejnowski 1577:Sepp Hochreiter 1545:(1986) and the 1511:Shun'ichi Amari 1503: 1445:computer vision 1430: 1406:Henry J. Kelley 1394:Backpropagation 1391: 1389:Backpropagation 1351:introduced the 1334:Shun'ichi Amari 1303: 1187: 1182: 1176: 1171: 1170: 1169: 1168: 1167: 1165: 1157: 1149: 1148: 1133: 1111:backpropagation 1103: 986: 957: 956: 930: 922: 921: 882: 874: 873: 834:Kernel machines 829: 821: 820: 796: 788: 787: 768:Active learning 763: 755: 754: 723: 713: 712: 638:Diffusion model 574: 564: 563: 536: 526: 525: 499: 489: 488: 444:Factor analysis 439: 429: 428: 412: 375: 365: 364: 285: 284: 268: 267: 266: 255: 254: 160: 152: 151: 117:Online learning 82: 70: 35: 28: 23: 22: 15: 12: 11: 5: 17883: 17873: 17872: 17870:Bioinspiration 17867: 17862: 17857: 17852: 17847: 17842: 17837: 17820: 17819: 17816: 17815: 17813: 17812: 17807: 17805:Alberto Broggi 17802: 17800:Harold Goddijn 17796: 17794: 17790: 17789: 17787: 17786: 17781: 17776: 17771: 17766: 17761: 17756: 17751: 17745: 17743: 17732: 17724: 17723: 17721: 17720: 17715: 17710: 17705: 17700: 17695: 17690: 17685: 17680: 17675: 17670: 17665: 17659: 17657: 17651: 17650: 17645: 17643: 17639: 17638: 17636: 17635: 17632: 17626: 17621: 17616: 17610: 17608: 17602: 17601: 17598: 17597: 17595: 17594: 17589: 17584: 17579: 17574: 17568: 17563: 17558: 17553: 17547: 17545: 17539: 17538: 17536: 17535: 17529: 17523: 17517: 17510:Tesla Model S 17507: 17501: 17495: 17489: 17483: 17476: 17474: 17467: 17463: 17462: 17459: 17458: 17456: 17455: 17450: 17445: 17439: 17433: 17431: 17430:(Levels 3,4,5) 17423: 17422: 17420: 17419: 17414: 17409: 17404: 17399: 17398: 17397: 17390:Cruise control 17387: 17382: 17377: 17372: 17366: 17364: 17363:(Levels 0,1,2) 17353: 17347: 17346: 17344: 17343: 17338: 17333: 17328: 17323: 17318: 17313: 17308: 17303: 17297: 17295: 17289: 17288: 17280: 17279: 17272: 17265: 17257: 17248: 17247: 17245: 17244: 17232: 17219: 17216: 17215: 17213: 17212: 17210:Self-awareness 17207: 17202: 17197: 17192: 17187: 17182: 17177: 17172: 17167: 17165:Neurodiversity 17162: 17157: 17152: 17147: 17142: 17137: 17132: 17127: 17122: 17116: 17114: 17110: 17109: 17107: 17106: 17101: 17096: 17091: 17086: 17081: 17076: 17071: 17066: 17064:Neuromarketing 17061: 17056: 17051: 17046: 17041: 17039:Neuroesthetics 17036: 17031: 17029:Neuroeconomics 17026: 17021: 17016: 17011: 17006: 17001: 16996: 16991: 16986: 16980: 16978: 16972: 16971: 16969: 16968: 16963: 16958: 16953: 16948: 16943: 16938: 16933: 16928: 16922: 16920: 16912: 16911: 16909: 16908: 16903: 16898: 16893: 16888: 16883: 16878: 16876:Neuroradiology 16873: 16868: 16863: 16858: 16856:Neuropathology 16853: 16848: 16846:Neuro-oncology 16843: 16838: 16833: 16828: 16823: 16818: 16813: 16808: 16803: 16797: 16795: 16787: 16786: 16779: 16777: 16775: 16774: 16769: 16764: 16759: 16754: 16749: 16744: 16739: 16734: 16732:Neurochemistry 16729: 16724: 16719: 16714: 16709: 16704: 16699: 16694: 16689: 16684: 16679: 16674: 16669: 16664: 16658: 16656: 16648: 16647: 16645: 16644: 16639: 16633: 16630: 16629: 16622: 16621: 16614: 16607: 16599: 16590: 16589: 16587: 16586: 16585: 16584: 16579: 16566: 16565: 16564: 16559: 16545: 16542: 16541: 16539: 16538: 16533: 16528: 16523: 16518: 16513: 16508: 16503: 16498: 16493: 16488: 16483: 16478: 16473: 16468: 16462: 16460: 16456: 16455: 16453: 16452: 16447: 16442: 16437: 16432: 16427: 16422: 16417: 16412: 16406: 16404: 16400: 16399: 16397: 16396: 16394:Ilya Sutskever 16391: 16386: 16381: 16376: 16371: 16366: 16361: 16359:Demis Hassabis 16356: 16351: 16349:Ian Goodfellow 16346: 16341: 16335: 16333: 16329: 16328: 16325: 16324: 16322: 16321: 16316: 16315: 16314: 16304: 16299: 16294: 16289: 16284: 16279: 16274: 16268: 16266: 16262: 16261: 16259: 16258: 16253: 16248: 16243: 16238: 16233: 16228: 16223: 16218: 16213: 16208: 16203: 16198: 16193: 16188: 16183: 16178: 16177: 16176: 16166: 16161: 16156: 16151: 16146: 16140: 16138: 16134: 16133: 16131: 16130: 16125: 16124: 16123: 16118: 16108: 16107: 16106: 16101: 16096: 16086: 16081: 16076: 16071: 16066: 16061: 16056: 16051: 16046: 16040: 16038: 16031: 16027: 16026: 16024: 16023: 16018: 16013: 16008: 16003: 15998: 15993: 15987: 15985: 15981: 15980: 15978: 15977: 15972: 15967: 15962: 15957: 15951: 15949: 15945: 15944: 15942: 15941: 15940: 15939: 15932:Language model 15929: 15924: 15919: 15918: 15917: 15907: 15906: 15905: 15894: 15892: 15888: 15887: 15885: 15884: 15882:Autoregression 15879: 15874: 15873: 15872: 15862: 15860:Regularization 15857: 15856: 15855: 15850: 15845: 15835: 15830: 15825: 15823:Loss functions 15820: 15815: 15810: 15805: 15800: 15799: 15798: 15788: 15783: 15782: 15781: 15770: 15768: 15764: 15763: 15761: 15760: 15758:Inductive bias 15755: 15750: 15745: 15740: 15735: 15730: 15725: 15720: 15712: 15710: 15704: 15703: 15698: 15697: 15690: 15683: 15675: 15666: 15665: 15663: 15662: 15657: 15652: 15647: 15645:Motion control 15642: 15637: 15632: 15627: 15622: 15614: 15612: 15608: 15607: 15605: 15604: 15599: 15597:PID controller 15594: 15589: 15584: 15579: 15573: 15571: 15567: 15566: 15564: 15563: 15561:Vector control 15558: 15556:State observer 15553: 15548: 15543: 15538: 15533: 15528: 15523: 15518: 15513: 15508: 15503: 15498: 15493: 15488: 15482: 15480: 15476: 15475: 15473: 15472: 15467: 15462: 15456: 15450: 15445: 15440: 15434: 15432: 15428: 15427: 15425: 15424: 15419: 15414: 15408: 15402: 15397: 15392: 15390:Servomechanism 15387: 15381: 15376: 15371: 15366: 15361: 15356: 15351: 15346: 15341: 15336: 15331: 15325: 15323: 15319: 15318: 15316: 15315: 15310: 15308:Robust control 15305: 15300: 15295: 15290: 15284: 15278: 15273: 15268: 15262: 15257: 15252: 15247: 15245:Control theory 15242: 15236: 15234: 15230: 15229: 15226:Control theory 15222: 15221: 15214: 15207: 15199: 15190: 15189: 15187: 15186: 15181: 15176: 15171: 15166: 15161: 15156: 15151: 15146: 15144:Self-reference 15141: 15136: 15131: 15125: 15123: 15121:Systems theory 15117: 15116: 15114: 15113: 15108: 15103: 15098: 15093: 15088: 15083: 15078: 15072: 15070: 15064: 15063: 15061: 15060: 15055: 15050: 15048:Multistability 15045: 15040: 15035: 15030: 15025: 15020: 15014: 15012: 15006: 15005: 15003: 15002: 14997: 14992: 14987: 14982: 14977: 14972: 14967: 14962: 14957: 14951: 14949: 14943: 14942: 14940: 14939: 14934: 14929: 14924: 14918: 14916: 14910: 14909: 14907: 14906: 14901: 14896: 14891: 14886: 14881: 14876: 14871: 14866: 14861: 14855: 14853: 14842: 14841: 14839: 14838: 14833: 14828: 14823: 14818: 14813: 14808: 14806:Herd mentality 14803: 14798: 14793: 14788: 14783: 14777: 14775: 14767: 14766: 14764: 14763: 14758: 14752: 14750: 14746: 14745: 14738: 14737: 14730: 14723: 14715: 14708: 14707: 14701: 14688: 14674: 14661: 14647: 14634: 14614:(2): 331–360. 14599: 14593: 14575: 14561: 14540: 14526: 14513: 14499: 14486: 14472: 14459: 14445: 14425: 14411: 14398: 14384: 14371: 14357: 14344: 14343: 14342: 14304: 14291:10.1.1.21.5444 14273: 14259: 14246: 14232: 14219: 14198: 14184: 14171: 14157: 14144: 14115: 14113: 14110: 14108: 14107: 14092: 14066: 14035: 13994: 13962: 13941:(3): 277–304. 13918: 13868: 13816: 13786: 13755: 13722:(1): 134–140. 13699: 13655: 13624:(10): 100347. 13601: 13592: 13535: 13503: 13472: 13447: 13409: 13399:," BMI Press, 13381: 13364: 13346: 13320: 13301: 13294: 13274: 13265: 13206: 13137: 13116: 13063: 13012: 12997: 12961: 12926: 12889:(12): 124002. 12873: 12843: 12828: 12795: 12743: 12721: 12688: 12651: 12620: 12571: 12506: 12463: 12412: 12367:(25): 250503. 12351: 12306:(25): 250502. 12290: 12253:(21): 214306. 12237: 12192:(25): 250501. 12174: 12144: 12114: 12083: 12068: 12042: 12036: 12010: 11980: 11965: 11939: 11920:(1): 327–343. 11904: 11889:(7): 324–331. 11869: 11834: 11823:(2): 124–137. 11807: 11796:(2): 115–123. 11780: 11765:(2): 319–330. 11745: 11686: 11655: 11618:(6): 443–458. 11602: 11573:(3): 289–299. 11553: 11502: 11437: 11396: 11355: 11316: 11243: 11210:J. Chem. Phys. 11199: 11164: 11133: 11106: 11076: 11041: 11021: 11002:(1): 118–129. 10986: 10943: 10916: 10886: 10859: 10829: 10802: 10772: 10745: 10715: 10708: 10682: 10654: 10613: 10592: 10581:: 53:1–53:32. 10565: 10541: 10504: 10467: 10446:(11): 85–117. 10423: 10396: 10375: 10350: 10292: 10270: 10251:(4): 541–551. 10235: 10178: 10118: 10099: 10069: 10030: 9995: 9976: 9932: 9883: 9877: 9859: 9842:Science | AAAS 9829: 9807: 9786: 9767:(6): 637–667. 9751: 9731: 9713: 9706: 9670: 9663: 9637: 9618: 9588: 9551: 9532: 9512: 9501:(4): 241–251. 9485: 9478: 9446: 9393: 9374: 9354: 9335: 9315: 9296: 9276: 9267: 9236: 9215: 9180: 9151:(1): 489–501. 9145:Neurocomputing 9135: 9120: 9094: 9073: 9058: 9040: 8999: 8992: 8970: 8929: 8899: 8858: 8823: 8816: 8799: 8784: 8759: 8728: 8709: 8690: 8671:(1): 131–139. 8645: 8624: 8598: 8591: 8549: 8528: 8503: 8482: 8458: 8432: 8406: 8385: 8359: 8303: 8298:Proc. SAB'1991 8285: 8247: 8240: 8219: 8198: 8176: 8155: 8118: 8103: 8067: 8052: 8026: 7991: 7946: 7885: 7855: 7836: 7774: 7730:(5): 889–904. 7699: 7692: 7661: 7628:(1): 147–169. 7608: 7601: 7575: 7529: 7498: 7479: 7457: 7430: 7409: 7390:(2): 234–242. 7367: 7353:(April 1991). 7342: 7317: 7264: 7205: 7166: 7153: 7139: 7126: 7097:10.1.1.32.9552 7067: 7020: 6993:(29): 4211–7. 6987:Applied Optics 6973: 6946:(32): 4790–7. 6940:Applied Optics 6926: 6909: 6890: 6872: 6852: 6833:(6): 455–469. 6813: 6766: 6726: 6719: 6691: 6672:(2): 146–160. 6653: 6635: 6630:10.2514/8.5282 6605: 6598: 6578: 6520: 6493:(4): 193–202. 6477: 6435: 6428: 6408: 6387: 6358:(2): 233–268. 6342: 6331:(4): 322–333. 6315: 6312:(16): 279–307. 6293: 6257: 6212: 6201:(2): 207–219. 6181: 6174: 6154: 6133: 6126: 6103: 6085: 6058:(3): 611–659. 6042: 6027: 6010: 5973:(6): 386–408. 5957: 5948: 5921: 5894: 5887: 5867: 5840: 5821:(4): 115–133. 5798: 5791: 5766: 5729: 5714: 5701:(3): 465–474. 5681: 5672: 5665: 5647: 5615: 5608: 5587: 5580: 5562: 5543: 5523: 5495: 5493: 5490: 5488: 5485: 5484: 5483: 5456: 5444: 5439: 5426: 5421: 5416: 5404: 5386: 5372: 5365: 5353: 5340: 5339: 5337: 5336:External links 5334: 5332: 5331: 5326: 5321: 5316: 5311: 5306: 5301: 5296: 5291: 5286: 5281: 5276: 5271: 5266: 5261: 5256: 5251: 5246: 5241: 5236: 5231: 5226: 5221: 5216: 5211: 5206: 5201: 5196: 5191: 5185: 5183: 5180: 5153: 5150: 5140: 5137: 5128: 5127: 5124: 5117:credit scoring 5102: 5099: 5093: 5090: 5084: 5081: 5075: 5072: 5066: 5063: 5057: 5054: 5053: 5052: 5049: 5042: 5040: 5037: 5030: 5028: 5025: 5018: 5016: 5013: 5006: 5004: 5001: 4994: 4992: 4989: 4982: 4980: 4967: 4964: 4959: 4955: 4951: 4948: 4943: 4940: 4936: 4932: 4927: 4923: 4919: 4914: 4910: 4906: 4903: 4900: 4897: 4892: 4888: 4864: 4860: 4836: 4832: 4820: 4813: 4809: 4806: 4802:synthetic data 4783: 4780: 4771: 4768: 4762: 4759: 4708: 4705: 4692:explainability 4651: 4648: 4638: 4635: 4633: 4630: 4626: 4625: 4607: 4603: 4598: 4592: 4587: 4584: 4581: 4577: 4568: 4564: 4559: 4553: 4548: 4544: 4487:regularization 4468: 4467: 4425:external links 4414: 4412: 4405: 4399: 4396: 4371: 4368: 4363:measure theory 4353: 4350: 4310: 4307: 4305: 4302: 4252: 4251: 4246: 4240: 4237: 4232: 4227: 4222: 4217: 4212: 4201: 4191: 4181: 4178:image analysis 4174: 4159: 4141: 4123: 4113: 4090: 4087: 4065:"b2" 4053:"w2" 4041:"b1" 4029:"w1" 3310: 3273: 3272: 3266: 3256: 3247: 3246:Network design 3244: 3243: 3242: 3235: 3209:Main article: 3206: 3203: 3192: 3191: 3149:external links 3138: 3136: 3129: 3123: 3120: 3083: 3080: 3049: 3046: 3038:Neuroevolution 3033:Neuroevolution 3031:Main article: 3028: 3027:Neuroevolution 3025: 3018: 3009: 3006: 2998:discretization 2957: 2952: 2948: 2944: 2939: 2935: 2930: 2924: 2921: 2918: 2914: 2910: 2907: 2885: 2880: 2876: 2871: 2865: 2861: 2857: 2854: 2832: 2827: 2823: 2818: 2812: 2808: 2804: 2801: 2779: 2776: 2770: 2766: 2762: 2759: 2756: 2753: 2750: 2745: 2741: 2717: 2714: 2708: 2704: 2700: 2697: 2694: 2691: 2688: 2683: 2679: 2638:Main article: 2635: 2632: 2593: 2590: 2587: 2584: 2562: 2532: 2510: 2505: 2501: 2497: 2494: 2491: 2488: 2485: 2482: 2479: 2476: 2473: 2470: 2467: 2445: 2423: 2420: 2417: 2414: 2411: 2408: 2380: 2363: 2360: 2335: 2332: 2314: 2313: 2271:external links 2260: 2258: 2251: 2245: 2242: 2211:Main article: 2208: 2205: 2188: 2185: 2163:Main article: 2160: 2157: 2119: 2118: 2076:external links 2065: 2063: 2056: 2050: 2047: 2037:is a constant 2035:hyperparameter 2027:Main article: 2024: 2023:Hyperparameter 2021: 1980: 1977: 1950:output neurons 1937: 1934: 1918:weighted graph 1893: 1892: 1850: 1848: 1841: 1835: 1832: 1814:. Many modern 1790:Main article: 1740:that models a 1726:Ian Goodfellow 1679:Karen Simonyan 1667:Ilya Sutskever 1627: 1624: 1543:Jordan network 1502: 1499: 1429: 1426: 1410:control theory 1390: 1387: 1381:introduced by 1302: 1299: 1186: 1183: 1178:Main article: 1175: 1172: 1162:false positive 1158: 1151: 1150: 1134: 1127: 1126: 1125: 1124: 1123: 1102: 1099: 1008:, abbreviated 998:neural network 988: 987: 985: 984: 977: 970: 962: 959: 958: 955: 954: 949: 948: 947: 937: 931: 928: 927: 924: 923: 920: 919: 914: 909: 904: 899: 894: 889: 883: 880: 879: 876: 875: 872: 871: 866: 861: 856: 854:Occam learning 851: 846: 841: 836: 830: 827: 826: 823: 822: 819: 818: 813: 811:Learning curve 808: 803: 797: 794: 793: 790: 789: 786: 785: 780: 775: 770: 764: 761: 760: 757: 756: 753: 752: 751: 750: 740: 735: 730: 724: 719: 718: 715: 714: 711: 710: 704: 699: 694: 689: 688: 687: 677: 672: 671: 670: 665: 660: 655: 645: 640: 635: 630: 629: 628: 618: 617: 616: 611: 606: 601: 591: 586: 581: 575: 570: 569: 566: 565: 562: 561: 556: 551: 543: 537: 532: 531: 528: 527: 524: 523: 522: 521: 516: 511: 500: 495: 494: 491: 490: 487: 486: 481: 476: 471: 466: 461: 456: 451: 446: 440: 435: 434: 431: 430: 427: 426: 421: 416: 410: 405: 400: 392: 387: 382: 376: 371: 370: 367: 366: 363: 362: 357: 352: 347: 342: 337: 332: 327: 319: 318: 317: 312: 307: 297: 295:Decision trees 292: 286: 272:classification 262: 261: 260: 257: 256: 253: 252: 247: 242: 237: 232: 227: 222: 217: 212: 207: 202: 197: 192: 187: 182: 177: 172: 167: 165:Classification 161: 158: 157: 154: 153: 150: 149: 144: 139: 134: 129: 124: 122:Batch learning 119: 114: 109: 104: 99: 94: 89: 83: 80: 79: 76: 75: 64: 63: 26: 9: 6: 4: 3: 2: 17882: 17871: 17868: 17866: 17863: 17861: 17858: 17856: 17853: 17851: 17848: 17846: 17843: 17841: 17838: 17836: 17833: 17832: 17830: 17811: 17808: 17806: 17803: 17801: 17798: 17797: 17795: 17791: 17785: 17782: 17780: 17777: 17775: 17772: 17770: 17767: 17765: 17762: 17760: 17757: 17755: 17752: 17750: 17747: 17746: 17744: 17736: 17733: 17725: 17719: 17716: 17714: 17711: 17709: 17706: 17704: 17701: 17699: 17696: 17694: 17691: 17689: 17686: 17684: 17681: 17679: 17676: 17674: 17671: 17669: 17666: 17664: 17661: 17660: 17658: 17652: 17648: 17644: 17640: 17633: 17630: 17627: 17625: 17622: 17620: 17617: 17615: 17612: 17611: 17609: 17607: 17603: 17593: 17590: 17588: 17585: 17583: 17580: 17578: 17575: 17572: 17569: 17567: 17566:Navia shuttle 17564: 17562: 17559: 17557: 17554: 17552: 17549: 17548: 17546: 17540: 17533: 17530: 17527: 17524: 17521: 17518: 17515: 17513: 17508: 17505: 17502: 17499: 17498:MadeInGermany 17496: 17493: 17490: 17487: 17484: 17481: 17478: 17477: 17475: 17471: 17468: 17464: 17454: 17451: 17449: 17448:Connected car 17446: 17443: 17440: 17438: 17435: 17434: 17432: 17424: 17418: 17415: 17413: 17410: 17408: 17405: 17403: 17400: 17396: 17393: 17392: 17391: 17388: 17386: 17383: 17381: 17378: 17376: 17373: 17371: 17368: 17367: 17365: 17357: 17354: 17352: 17348: 17342: 17339: 17337: 17334: 17332: 17329: 17327: 17324: 17322: 17319: 17317: 17314: 17312: 17309: 17307: 17304: 17302: 17299: 17298: 17296: 17292:Overview and 17290: 17285: 17278: 17273: 17271: 17266: 17264: 17259: 17258: 17255: 17243: 17242: 17233: 17231: 17230: 17221: 17220: 17217: 17211: 17208: 17206: 17203: 17201: 17198: 17196: 17193: 17191: 17188: 17186: 17183: 17181: 17178: 17176: 17173: 17171: 17168: 17166: 17163: 17161: 17158: 17156: 17153: 17151: 17148: 17146: 17143: 17141: 17138: 17136: 17133: 17131: 17128: 17126: 17123: 17121: 17118: 17117: 17115: 17111: 17105: 17102: 17100: 17097: 17095: 17094:Neurotheology 17092: 17090: 17089:Neurorobotics 17087: 17085: 17084:Neuropolitics 17082: 17080: 17077: 17075: 17072: 17070: 17067: 17065: 17062: 17060: 17057: 17055: 17052: 17050: 17049:Neuroethology 17047: 17045: 17042: 17040: 17037: 17035: 17032: 17030: 17027: 17025: 17022: 17020: 17017: 17015: 17012: 17010: 17007: 17005: 17002: 17000: 16997: 16995: 16992: 16990: 16987: 16985: 16982: 16981: 16979: 16973: 16967: 16964: 16962: 16959: 16957: 16954: 16952: 16949: 16947: 16946:Motor control 16944: 16942: 16939: 16937: 16936:Chronobiology 16934: 16932: 16929: 16927: 16924: 16923: 16921: 16919: 16913: 16907: 16904: 16902: 16899: 16897: 16896:Neurovirology 16894: 16892: 16889: 16887: 16884: 16882: 16879: 16877: 16874: 16872: 16869: 16867: 16864: 16862: 16859: 16857: 16854: 16852: 16849: 16847: 16844: 16842: 16839: 16837: 16834: 16832: 16829: 16827: 16824: 16822: 16819: 16817: 16814: 16812: 16809: 16807: 16804: 16802: 16799: 16798: 16796: 16794: 16788: 16783: 16773: 16770: 16768: 16765: 16763: 16760: 16758: 16755: 16753: 16750: 16748: 16745: 16743: 16742:Neurogenetics 16740: 16738: 16735: 16733: 16730: 16728: 16725: 16723: 16720: 16718: 16715: 16713: 16710: 16708: 16705: 16703: 16700: 16698: 16695: 16693: 16690: 16688: 16685: 16683: 16680: 16678: 16677:Brain-reading 16675: 16673: 16672:Brain mapping 16670: 16668: 16665: 16663: 16660: 16659: 16657: 16655: 16649: 16643: 16640: 16638: 16635: 16634: 16631: 16627: 16620: 16615: 16613: 16608: 16606: 16601: 16600: 16597: 16583: 16580: 16578: 16575: 16574: 16567: 16563: 16560: 16558: 16555: 16554: 16551: 16547: 16546: 16543: 16537: 16534: 16532: 16529: 16527: 16524: 16522: 16519: 16517: 16514: 16512: 16509: 16507: 16504: 16502: 16499: 16497: 16494: 16492: 16489: 16487: 16484: 16482: 16479: 16477: 16474: 16472: 16469: 16467: 16464: 16463: 16461: 16459:Architectures 16457: 16451: 16448: 16446: 16443: 16441: 16438: 16436: 16433: 16431: 16428: 16426: 16423: 16421: 16418: 16416: 16413: 16411: 16408: 16407: 16405: 16403:Organizations 16401: 16395: 16392: 16390: 16387: 16385: 16382: 16380: 16377: 16375: 16372: 16370: 16367: 16365: 16362: 16360: 16357: 16355: 16352: 16350: 16347: 16345: 16342: 16340: 16339:Yoshua Bengio 16337: 16336: 16334: 16330: 16320: 16319:Robot control 16317: 16313: 16310: 16309: 16308: 16305: 16303: 16300: 16298: 16295: 16293: 16290: 16288: 16285: 16283: 16280: 16278: 16275: 16273: 16270: 16269: 16267: 16263: 16257: 16254: 16252: 16249: 16247: 16244: 16242: 16239: 16237: 16236:Chinchilla AI 16234: 16232: 16229: 16227: 16224: 16222: 16219: 16217: 16214: 16212: 16209: 16207: 16204: 16202: 16199: 16197: 16194: 16192: 16189: 16187: 16184: 16182: 16179: 16175: 16172: 16171: 16170: 16167: 16165: 16162: 16160: 16157: 16155: 16152: 16150: 16147: 16145: 16142: 16141: 16139: 16135: 16129: 16126: 16122: 16119: 16117: 16114: 16113: 16112: 16109: 16105: 16102: 16100: 16097: 16095: 16092: 16091: 16090: 16087: 16085: 16082: 16080: 16077: 16075: 16072: 16070: 16067: 16065: 16062: 16060: 16057: 16055: 16052: 16050: 16047: 16045: 16042: 16041: 16039: 16035: 16032: 16028: 16022: 16019: 16017: 16014: 16012: 16009: 16007: 16004: 16002: 15999: 15997: 15994: 15992: 15989: 15988: 15986: 15982: 15976: 15973: 15971: 15968: 15966: 15963: 15961: 15958: 15956: 15953: 15952: 15950: 15946: 15938: 15935: 15934: 15933: 15930: 15928: 15925: 15923: 15920: 15916: 15915:Deep learning 15913: 15912: 15911: 15908: 15904: 15901: 15900: 15899: 15896: 15895: 15893: 15889: 15883: 15880: 15878: 15875: 15871: 15868: 15867: 15866: 15863: 15861: 15858: 15854: 15851: 15849: 15846: 15844: 15841: 15840: 15839: 15836: 15834: 15831: 15829: 15826: 15824: 15821: 15819: 15816: 15814: 15811: 15809: 15806: 15804: 15803:Hallucination 15801: 15797: 15794: 15793: 15792: 15789: 15787: 15784: 15780: 15777: 15776: 15775: 15772: 15771: 15769: 15765: 15759: 15756: 15754: 15751: 15749: 15746: 15744: 15741: 15739: 15736: 15734: 15731: 15729: 15726: 15724: 15721: 15719: 15718: 15714: 15713: 15711: 15709: 15705: 15696: 15691: 15689: 15684: 15682: 15677: 15676: 15673: 15661: 15658: 15656: 15653: 15651: 15648: 15646: 15643: 15641: 15638: 15636: 15633: 15631: 15628: 15626: 15623: 15621: 15620: 15616: 15615: 15613: 15609: 15603: 15600: 15598: 15595: 15593: 15590: 15588: 15585: 15583: 15580: 15578: 15575: 15574: 15572: 15568: 15562: 15559: 15557: 15554: 15552: 15549: 15547: 15544: 15542: 15539: 15537: 15536:Least squares 15534: 15532: 15529: 15527: 15526:Kalman filter 15524: 15522: 15519: 15517: 15514: 15512: 15509: 15507: 15504: 15502: 15499: 15497: 15494: 15492: 15489: 15487: 15484: 15483: 15481: 15477: 15471: 15468: 15466: 15463: 15460: 15457: 15454: 15451: 15449: 15446: 15444: 15441: 15439: 15436: 15435: 15433: 15429: 15423: 15420: 15418: 15415: 15412: 15409: 15406: 15403: 15401: 15398: 15396: 15393: 15391: 15388: 15385: 15382: 15380: 15377: 15375: 15372: 15370: 15369:Observability 15367: 15365: 15362: 15360: 15357: 15355: 15352: 15350: 15347: 15345: 15342: 15340: 15337: 15335: 15334:Block diagram 15332: 15330: 15327: 15326: 15324: 15320: 15314: 15311: 15309: 15306: 15304: 15301: 15299: 15296: 15294: 15291: 15288: 15285: 15282: 15281:Multivariable 15279: 15277: 15274: 15272: 15269: 15266: 15263: 15261: 15260:Fuzzy control 15258: 15256: 15253: 15251: 15248: 15246: 15243: 15241: 15238: 15237: 15235: 15231: 15227: 15220: 15215: 15213: 15208: 15206: 15201: 15200: 15197: 15185: 15182: 15180: 15177: 15175: 15172: 15170: 15167: 15165: 15162: 15160: 15157: 15155: 15152: 15150: 15149:Goal-oriented 15147: 15145: 15142: 15140: 15137: 15135: 15132: 15130: 15127: 15126: 15124: 15122: 15118: 15112: 15111:Geomorphology 15109: 15107: 15104: 15102: 15099: 15097: 15094: 15092: 15089: 15087: 15084: 15082: 15079: 15077: 15074: 15073: 15071: 15069: 15065: 15059: 15056: 15054: 15051: 15049: 15046: 15044: 15041: 15039: 15036: 15034: 15031: 15029: 15026: 15024: 15021: 15019: 15016: 15015: 15013: 15011: 15007: 15001: 14998: 14996: 14993: 14991: 14988: 14986: 14983: 14981: 14978: 14976: 14973: 14971: 14968: 14966: 14963: 14961: 14958: 14956: 14953: 14952: 14950: 14948: 14944: 14938: 14935: 14933: 14930: 14928: 14925: 14923: 14920: 14919: 14917: 14915: 14911: 14905: 14902: 14900: 14897: 14895: 14892: 14890: 14887: 14885: 14882: 14880: 14877: 14875: 14872: 14870: 14867: 14865: 14862: 14860: 14857: 14856: 14854: 14852: 14847: 14843: 14837: 14834: 14832: 14829: 14827: 14824: 14822: 14819: 14817: 14814: 14812: 14809: 14807: 14804: 14802: 14799: 14797: 14794: 14792: 14789: 14787: 14784: 14782: 14779: 14778: 14776: 14774: 14768: 14762: 14759: 14757: 14754: 14753: 14751: 14747: 14743: 14736: 14731: 14729: 14724: 14722: 14717: 14716: 14713: 14704: 14698: 14694: 14689: 14685: 14681: 14677: 14671: 14667: 14662: 14658: 14654: 14650: 14644: 14640: 14635: 14631: 14627: 14622: 14617: 14613: 14609: 14605: 14600: 14596: 14590: 14586: 14585: 14580: 14576: 14572: 14568: 14564: 14558: 14554: 14550: 14547:. CRC Press. 14546: 14541: 14537: 14533: 14529: 14523: 14519: 14514: 14510: 14506: 14502: 14496: 14492: 14487: 14483: 14479: 14475: 14469: 14465: 14460: 14456: 14452: 14448: 14442: 14438: 14434: 14430: 14426: 14422: 14418: 14414: 14408: 14404: 14399: 14395: 14391: 14387: 14381: 14377: 14372: 14368: 14364: 14360: 14354: 14351:. UCL Press. 14350: 14345: 14340: 14336: 14332: 14331: 14321:on 3 May 2013 14317: 14310: 14305: 14301: 14297: 14292: 14287: 14283: 14279: 14274: 14270: 14266: 14262: 14256: 14252: 14247: 14243: 14239: 14235: 14229: 14225: 14220: 14218: 14213: 14212: 14207: 14203: 14199: 14195: 14191: 14187: 14181: 14177: 14172: 14168: 14164: 14160: 14154: 14150: 14145: 14141: 14137: 14133: 14129: 14122: 14117: 14116: 14103: 14099: 14095: 14089: 14085: 14081: 14077: 14070: 14062: 14058: 14054: 14050: 14046: 14039: 14031: 14027: 14022: 14017: 14013: 14009: 14005: 13998: 13982: 13978: 13977: 13972: 13966: 13958: 13954: 13949: 13944: 13940: 13936: 13932: 13925: 13923: 13914: 13910: 13905: 13900: 13896: 13892: 13888: 13881: 13879: 13877: 13875: 13873: 13864: 13860: 13855: 13850: 13846: 13842: 13838: 13831: 13829: 13827: 13825: 13823: 13821: 13801: 13797: 13793: 13789: 13783: 13779: 13775: 13768: 13767: 13759: 13743: 13739: 13735: 13730: 13725: 13721: 13717: 13713: 13706: 13704: 13695: 13691: 13686: 13681: 13678:(10): 29–40. 13677: 13673: 13669: 13662: 13660: 13651: 13647: 13642: 13637: 13632: 13627: 13623: 13619: 13615: 13608: 13606: 13596: 13588: 13584: 13579: 13574: 13570: 13566: 13562: 13558: 13554: 13550: 13546: 13539: 13520: 13513: 13507: 13491: 13487: 13483: 13476: 13461: 13457: 13451: 13443: 13439: 13435: 13431: 13427: 13423: 13416: 13414: 13406: 13402: 13398: 13394: 13391: 13385: 13378: 13374: 13368: 13360: 13356: 13350: 13334: 13330: 13324: 13317: 13313: 13310: 13305: 13297: 13291: 13287: 13286: 13278: 13269: 13261: 13257: 13253: 13249: 13244: 13239: 13234: 13229: 13225: 13221: 13217: 13210: 13194: 13190: 13186: 13182: 13178: 13174: 13170: 13165: 13160: 13156: 13152: 13148: 13141: 13132: 13127: 13120: 13112: 13108: 13104: 13100: 13096: 13092: 13087: 13082: 13078: 13074: 13067: 13048: 13043: 13038: 13035:: 5301–5310. 13034: 13030: 13023: 13016: 13008: 13004: 13000: 12994: 12990: 12986: 12981: 12976: 12972: 12965: 12946: 12939: 12938: 12930: 12922: 12918: 12914: 12910: 12906: 12902: 12897: 12892: 12888: 12884: 12877: 12861: 12857: 12853: 12847: 12839: 12835: 12831: 12825: 12821: 12817: 12813: 12809: 12805: 12799: 12780: 12776: 12772: 12768: 12765:. EC-14 (3). 12764: 12757: 12753: 12747: 12728: 12724: 12718: 12714: 12707: 12706: 12701: 12695: 12693: 12684: 12680: 12675: 12670: 12666: 12662: 12655: 12639: 12635: 12631: 12624: 12605: 12601: 12597: 12593: 12589: 12582: 12575: 12567: 12563: 12558: 12553: 12549: 12545: 12541: 12537: 12533: 12529: 12525: 12521: 12517: 12510: 12494: 12490: 12486: 12482: 12478: 12474: 12467: 12459: 12455: 12450: 12445: 12440: 12435: 12431: 12427: 12423: 12416: 12408: 12404: 12400: 12396: 12392: 12388: 12384: 12380: 12375: 12370: 12366: 12362: 12355: 12347: 12343: 12339: 12335: 12331: 12327: 12323: 12319: 12314: 12309: 12305: 12301: 12294: 12286: 12282: 12278: 12274: 12270: 12266: 12261: 12256: 12252: 12248: 12241: 12233: 12229: 12225: 12221: 12217: 12213: 12209: 12205: 12200: 12195: 12191: 12187: 12186: 12178: 12162: 12158: 12154: 12148: 12132: 12128: 12124: 12118: 12102: 12098: 12094: 12087: 12079: 12075: 12071: 12065: 12061: 12057: 12053: 12046: 12039: 12033: 12029: 12025: 12021: 12014: 11998: 11994: 11990: 11984: 11976: 11972: 11968: 11962: 11958: 11954: 11950: 11943: 11935: 11931: 11927: 11923: 11919: 11915: 11914:Geomorphology 11908: 11892: 11888: 11884: 11880: 11873: 11865: 11861: 11857: 11853: 11849: 11845: 11838: 11830: 11826: 11822: 11818: 11811: 11803: 11799: 11795: 11791: 11784: 11768: 11764: 11760: 11756: 11749: 11741: 11737: 11732: 11727: 11722: 11717: 11713: 11709: 11705: 11701: 11697: 11690: 11674: 11670: 11666: 11659: 11651: 11647: 11643: 11639: 11635: 11631: 11626: 11621: 11617: 11613: 11606: 11598: 11594: 11589: 11584: 11580: 11576: 11572: 11568: 11564: 11557: 11541: 11537: 11533: 11529: 11525: 11521: 11517: 11513: 11506: 11487: 11483: 11479: 11475: 11471: 11467: 11463: 11459: 11455: 11448: 11441: 11432: 11427: 11423: 11419: 11416:(26): 81–97. 11415: 11411: 11407: 11400: 11384: 11380: 11376: 11372: 11371: 11366: 11359: 11343: 11339: 11335: 11331: 11327: 11320: 11301: 11297: 11293: 11289: 11285: 11281: 11277: 11273: 11269: 11265: 11261: 11254: 11247: 11239: 11235: 11231: 11227: 11223: 11219: 11216:(7): 074104. 11215: 11212: 11211: 11203: 11195: 11191: 11187: 11183: 11179: 11175: 11168: 11152: 11148: 11144: 11137: 11121: 11117: 11113: 11109: 11103: 11099: 11095: 11091: 11087: 11080: 11064: 11060: 11056: 11052: 11045: 11038: 11034: 11031: 11025: 11017: 11013: 11009: 11005: 11001: 10997: 10990: 10974: 10970: 10966: 10962: 10958: 10954: 10947: 10931: 10927: 10923: 10919: 10913: 10909: 10905: 10901: 10897: 10890: 10874: 10870: 10866: 10862: 10856: 10852: 10848: 10844: 10840: 10833: 10817: 10813: 10809: 10805: 10799: 10795: 10791: 10787: 10783: 10776: 10760: 10756: 10752: 10748: 10742: 10738: 10734: 10730: 10726: 10719: 10711: 10705: 10701: 10697: 10693: 10686: 10680: 10676: 10670: 10665: 10658: 10642: 10637: 10632: 10628: 10624: 10617: 10608: 10603: 10596: 10588: 10584: 10580: 10576: 10569: 10560: 10555: 10551: 10545: 10526: 10522: 10515: 10508: 10489: 10485: 10478: 10471: 10462: 10457: 10453: 10449: 10445: 10441: 10437: 10433: 10432:Schmidhuber J 10427: 10411: 10407: 10400: 10391: 10386: 10379: 10368: 10361: 10354: 10346: 10342: 10338: 10334: 10330: 10326: 10322: 10318: 10314: 10310: 10306: 10305:Schmidhuber J 10302: 10296: 10290: 10286: 10283: 10279: 10274: 10266: 10262: 10258: 10254: 10250: 10246: 10239: 10220: 10216: 10212: 10208: 10204: 10200: 10196: 10189: 10182: 10163: 10159: 10155: 10151: 10147: 10143: 10139: 10132: 10125: 10123: 10106: 10102: 10096: 10092: 10088: 10084: 10080: 10073: 10057: 10053: 10049: 10045: 10041: 10034: 10015: 10008: 10007: 9999: 9983: 9979: 9973: 9968: 9963: 9959: 9955: 9951: 9947: 9943: 9936: 9928: 9924: 9920: 9916: 9912: 9908: 9903: 9898: 9894: 9887: 9880: 9874: 9870: 9863: 9847: 9843: 9839: 9833: 9824: 9819: 9811: 9802: 9797: 9790: 9782: 9778: 9774: 9770: 9766: 9762: 9755: 9748: 9744: 9741: 9735: 9728: 9724: 9717: 9709: 9703: 9699: 9695: 9690: 9685: 9681: 9674: 9666: 9664:0-7803-6375-2 9660: 9656: 9652: 9648: 9641: 9625: 9621: 9619:0-86740-525-2 9615: 9611: 9607: 9603: 9599: 9592: 9584: 9580: 9575: 9570: 9566: 9562: 9555: 9539: 9535: 9529: 9525: 9524: 9516: 9508: 9504: 9500: 9496: 9489: 9481: 9479:0-7803-0164-1 9475: 9471: 9467: 9463: 9458: 9450: 9442: 9438: 9434: 9430: 9426: 9422: 9417: 9412: 9408: 9404: 9397: 9381: 9377: 9371: 9367: 9366: 9358: 9342: 9338: 9332: 9328: 9327: 9319: 9303: 9299: 9293: 9289: 9288: 9280: 9271: 9255: 9251: 9247: 9240: 9231: 9226: 9219: 9211: 9207: 9203: 9199: 9195: 9191: 9184: 9176: 9172: 9168: 9164: 9159: 9154: 9150: 9146: 9139: 9131: 9127: 9123: 9117: 9113: 9109: 9105: 9098: 9089: 9084: 9077: 9069: 9065: 9061: 9055: 9051: 9044: 9025: 9021: 9017: 9010: 9003: 8995: 8993:3-89319-554-8 8989: 8985: 8981: 8974: 8955: 8952:: 1237–1242. 8951: 8947: 8940: 8933: 8917: 8913: 8909: 8903: 8894: 8889: 8885: 8881: 8877: 8873: 8869: 8862: 8854: 8850: 8846: 8842: 8838: 8834: 8827: 8819: 8817:0-201-53377-4 8813: 8809: 8803: 8795: 8791: 8787: 8781: 8777: 8773: 8766: 8764: 8755: 8751: 8747: 8743: 8739: 8732: 8724: 8720: 8719:Schmidhuber J 8713: 8705: 8701: 8694: 8686: 8682: 8678: 8674: 8670: 8666: 8659: 8655: 8654:Schmidhuber J 8649: 8640: 8635: 8628: 8613: 8609: 8602: 8594: 8588: 8584: 8580: 8575: 8570: 8566: 8562: 8561: 8553: 8544: 8539: 8532: 8523: 8518: 8514: 8507: 8498: 8493: 8486: 8478: 8473: 8469: 8462: 8454: 8450: 8443: 8436: 8420: 8416: 8410: 8401: 8396: 8389: 8373: 8369: 8363: 8355: 8351: 8347: 8343: 8339: 8335: 8330: 8325: 8321: 8317: 8313: 8312:Schmidhuber J 8307: 8299: 8295: 8294:Schmidhuber J 8289: 8270: 8263: 8262: 8254: 8252: 8243: 8237: 8233: 8226: 8224: 8214: 8209: 8202: 8194: 8187: 8180: 8171: 8166: 8159: 8140: 8136: 8129: 8122: 8114: 8110: 8106: 8100: 8096: 8092: 8087: 8082: 8078: 8071: 8063: 8059: 8055: 8049: 8045: 8041: 8037: 8030: 8011: 8004: 8003: 7995: 7976: 7972: 7968: 7964: 7957: 7950: 7942: 7938: 7934: 7930: 7926: 7922: 7918: 7914: 7909: 7904: 7900: 7896: 7889: 7873: 7869: 7865: 7859: 7852: 7848: 7845: 7840: 7832: 7828: 7824: 7820: 7816: 7812: 7808: 7804: 7800: 7796: 7792: 7788: 7784: 7778: 7770: 7763: 7759: 7755: 7751: 7746: 7741: 7737: 7733: 7729: 7725: 7721: 7717: 7713: 7709: 7703: 7695: 7693:0-262-68053-X 7689: 7685: 7681: 7680: 7672: 7665: 7657: 7651: 7643: 7639: 7635: 7631: 7627: 7623: 7619: 7612: 7604: 7602:0-85296-721-7 7598: 7594: 7590: 7586: 7579: 7571: 7567: 7563: 7559: 7555: 7551: 7547: 7543: 7539: 7533: 7526: 7522: 7518: 7517: 7512: 7508: 7502: 7486: 7482: 7476: 7472: 7468: 7461: 7454: 7450: 7446: 7443: 7437: 7435: 7422: 7421: 7413: 7405: 7401: 7397: 7393: 7389: 7385: 7378: 7371: 7363: 7356: 7352: 7351:Schmidhuber J 7346: 7331: 7327: 7321: 7313: 7309: 7305: 7301: 7296: 7291: 7287: 7283: 7279: 7275: 7268: 7260: 7256: 7251: 7246: 7241: 7236: 7232: 7228: 7224: 7220: 7216: 7209: 7201: 7197: 7193: 7189: 7185: 7181: 7177: 7170: 7163: 7157: 7150: 7143: 7136: 7130: 7115: 7111: 7107: 7103: 7098: 7093: 7089: 7085: 7078: 7071: 7063: 7059: 7055: 7051: 7047: 7043: 7040:(4): 517–24. 7039: 7035: 7031: 7024: 7016: 7012: 7008: 7004: 7000: 6996: 6992: 6988: 6984: 6977: 6969: 6965: 6961: 6957: 6953: 6949: 6945: 6941: 6937: 6930: 6923: 6919: 6913: 6905: 6901: 6894: 6887: 6886: 6881: 6876: 6865: 6864: 6856: 6848: 6844: 6840: 6836: 6832: 6828: 6824: 6817: 6809: 6805: 6801: 6797: 6793: 6789: 6785: 6781: 6777: 6770: 6751: 6747: 6740: 6736: 6730: 6722: 6716: 6712: 6708: 6704: 6703: 6695: 6687: 6683: 6679: 6675: 6671: 6667: 6663: 6657: 6649: 6645: 6639: 6631: 6627: 6623: 6619: 6615: 6609: 6601: 6599:9780598818461 6595: 6591: 6590: 6582: 6574: 6570: 6566: 6562: 6558: 6554: 6549: 6544: 6540: 6536: 6529: 6527: 6525: 6516: 6512: 6508: 6504: 6500: 6496: 6492: 6488: 6481: 6473: 6469: 6465: 6461: 6457: 6453: 6449: 6442: 6440: 6431: 6425: 6422:. MIT Press. 6421: 6420: 6412: 6403: 6398: 6391: 6383: 6379: 6375: 6371: 6366: 6361: 6357: 6353: 6346: 6338: 6334: 6330: 6326: 6319: 6311: 6307: 6303: 6297: 6288: 6283: 6279: 6275: 6271: 6267: 6261: 6242: 6238: 6234: 6230: 6223: 6216: 6208: 6204: 6200: 6196: 6192: 6185: 6177: 6171: 6167: 6166: 6158: 6150: 6146: 6140: 6138: 6129: 6123: 6116: 6115: 6107: 6099: 6092: 6090: 6081: 6077: 6073: 6069: 6065: 6061: 6057: 6053: 6046: 6038: 6031: 6023: 6022: 6014: 6006: 6002: 5998: 5994: 5990: 5986: 5981: 5976: 5972: 5968: 5961: 5952: 5944: 5940: 5936: 5932: 5925: 5917: 5913: 5909: 5905: 5898: 5890: 5884: 5880: 5879: 5871: 5855: 5851: 5844: 5836: 5832: 5828: 5824: 5820: 5816: 5812: 5805: 5803: 5794: 5792:0-674-40340-1 5788: 5783: 5782: 5776: 5770: 5761: 5756: 5752: 5751:Schmidhuber J 5746: 5744: 5742: 5740: 5738: 5736: 5734: 5725: 5718: 5709: 5704: 5700: 5696: 5692: 5685: 5676: 5668: 5662: 5658: 5651: 5635: 5632:. MIT Press. 5631: 5630: 5629:Deep Learning 5622: 5620: 5611: 5605: 5601: 5594: 5592: 5583: 5577: 5573: 5566: 5550: 5546: 5540: 5536: 5535: 5527: 5511: 5507: 5500: 5496: 5480: 5476: 5472: 5471: 5466: 5464: 5457: 5455: 5451: 5448: 5445: 5443: 5440: 5437: 5433: 5430: 5427: 5425: 5422: 5420: 5417: 5415: 5411: 5408: 5405: 5402: 5399: 5398: 5394: 5390: 5375: 5330: 5327: 5325: 5322: 5320: 5317: 5315: 5312: 5310: 5307: 5305: 5302: 5300: 5297: 5295: 5292: 5290: 5287: 5285: 5282: 5280: 5277: 5275: 5272: 5270: 5267: 5265: 5262: 5260: 5257: 5255: 5252: 5250: 5247: 5245: 5242: 5240: 5237: 5235: 5232: 5230: 5227: 5225: 5222: 5220: 5217: 5215: 5212: 5210: 5207: 5205: 5202: 5200: 5197: 5195: 5192: 5190: 5187: 5186: 5179: 5177: 5173: 5168: 5163: 5159: 5149: 5146: 5136: 5134: 5125: 5122: 5121: 5120: 5118: 5114: 5108: 5098: 5089: 5080: 5071: 5062: 5046: 5041: 5034: 5029: 5022: 5017: 5010: 5005: 4998: 4993: 4986: 4981: 4965: 4957: 4953: 4949: 4941: 4938: 4934: 4930: 4925: 4921: 4912: 4908: 4901: 4898: 4895: 4890: 4886: 4862: 4858: 4834: 4830: 4817: 4812: 4811: 4805: 4803: 4798: 4794: 4790: 4779: 4777: 4774:Advocates of 4767: 4758: 4756: 4751: 4747: 4743: 4741: 4737: 4733: 4728: 4726: 4722: 4718: 4714: 4704: 4700: 4697: 4693: 4686: 4682: 4677: 4674: 4672: 4667: 4666: 4661: 4657: 4647: 4645: 4629: 4605: 4601: 4596: 4590: 4585: 4582: 4579: 4575: 4566: 4562: 4557: 4551: 4546: 4542: 4534: 4533: 4532: 4529: 4527: 4523: 4518: 4516: 4512: 4508: 4504: 4495: 4491: 4489: 4488: 4482: 4480: 4476: 4464: 4461: 4453: 4443: 4439: 4433: 4432: 4426: 4422: 4418: 4413: 4404: 4403: 4395: 4393: 4392:Jacobi method 4389: 4388:affine models 4383: 4381: 4376: 4367: 4364: 4360: 4349: 4347: 4343: 4339: 4335: 4331: 4326: 4324: 4320: 4316: 4301: 4297: 4294: 4292: 4288: 4284: 4279: 4277: 4276:cybersecurity 4273: 4272:geomorphology 4269: 4265: 4261: 4255: 4250: 4247: 4244: 4241: 4238: 4236: 4233: 4231: 4228: 4226: 4225:Generative AI 4223: 4221: 4218: 4216: 4213: 4210: 4206: 4202: 4199: 4195: 4192: 4189: 4185: 4182: 4179: 4175: 4172: 4168: 4164: 4160: 4157: 4153: 4149: 4145: 4142: 4139: 4135: 4131: 4127: 4124: 4121: 4117: 4114: 4111: 4107: 4104:, (including 4103: 4099: 4096: 4095: 4094: 4086: 3930:learning_rate 3909:learning_rate 3888:learning_rate 3867:learning_rate 3339:learning_rate 3307: 3302: 3299: 3295: 3293: 3289: 3285: 3281: 3277: 3270: 3267: 3264: 3260: 3257: 3253: 3252: 3251: 3240: 3236: 3233: 3229: 3226: 3225: 3224: 3221: 3219: 3212: 3202: 3199: 3188: 3185: 3177: 3167: 3163: 3157: 3156: 3150: 3146: 3142: 3137: 3128: 3127: 3119: 3117: 3113: 3109: 3105: 3101: 3097: 3093: 3089: 3079: 3077: 3073: 3069: 3065: 3062: 3058: 3054: 3045: 3043: 3039: 3034: 3024: 3017: 3015: 3008:Self-learning 3005: 3003: 2999: 2995: 2991: 2987: 2983: 2979: 2974: 2972: 2950: 2946: 2942: 2937: 2933: 2922: 2919: 2916: 2912: 2905: 2878: 2874: 2863: 2859: 2852: 2825: 2821: 2810: 2806: 2799: 2777: 2774: 2768: 2764: 2760: 2757: 2754: 2751: 2748: 2743: 2739: 2715: 2712: 2706: 2702: 2698: 2695: 2692: 2689: 2686: 2681: 2677: 2666: 2661: 2658: 2657:instantaneous 2654: 2647: 2641: 2631: 2629: 2625: 2621: 2617: 2613: 2608: 2588: 2582: 2560: 2551: 2547: 2530: 2503: 2492: 2486: 2483: 2480: 2471: 2468: 2465: 2443: 2421: 2418: 2412: 2406: 2397: 2396: 2378: 2369: 2359: 2357: 2353: 2349: 2345: 2340: 2331: 2329: 2325: 2321: 2310: 2307: 2299: 2289: 2285: 2279: 2278: 2272: 2268: 2264: 2259: 2250: 2249: 2241: 2239: 2235: 2234: 2229: 2225: 2224:cost function 2221: 2214: 2204: 2202: 2198: 2194: 2187:Cost function 2184: 2181: 2177: 2173: 2166: 2165:Learning rate 2159:Learning rate 2156: 2154: 2150: 2145: 2141: 2140:cost function 2134: 2130: 2126: 2115: 2112: 2104: 2094: 2090: 2084: 2083: 2077: 2073: 2069: 2064: 2055: 2054: 2046: 2044: 2043:learning rate 2040: 2036: 2030: 2020: 2018: 2017: 2012: 2011: 2006: 2002: 1998: 1997:hidden layers 1994: 1990: 1986: 1985:deep learning 1976: 1974: 1970: 1966: 1962: 1958: 1953: 1951: 1947: 1943: 1933: 1930: 1926: 1921: 1919: 1915: 1905: 1900: 1889: 1886: 1878: 1868: 1867:the talk page 1864: 1858: 1856: 1851:This section 1849: 1840: 1839: 1831: 1829: 1825: 1821: 1817: 1813: 1809: 1805: 1804: 1799: 1793: 1788: 1786: 1782: 1778: 1773: 1771: 1767: 1763: 1759: 1755: 1751: 1747: 1743: 1739: 1735: 1734:zero-sum game 1731: 1727: 1723: 1719: 1717: 1713: 1709: 1707: 1703: 1699: 1695: 1690: 1688: 1685:and Google's 1684: 1680: 1676: 1672: 1668: 1664: 1660: 1655: 1653: 1649: 1645: 1641: 1637: 1633: 1626:Deep learning 1623: 1621: 1617: 1613: 1609: 1605: 1601: 1597: 1592: 1590: 1586: 1582: 1578: 1573: 1571: 1567: 1564:) and neural 1563: 1559: 1554: 1552: 1548: 1547:Elman network 1544: 1539: 1536: 1532: 1528: 1524: 1520: 1516: 1512: 1508: 1498: 1496: 1492: 1488: 1483: 1481: 1477: 1473: 1469: 1465: 1461: 1457: 1453: 1448: 1446: 1442: 1438: 1434: 1425: 1423: 1419: 1415: 1411: 1407: 1403: 1399: 1395: 1386: 1384: 1380: 1376: 1371: 1369: 1365: 1360: 1358: 1354: 1350: 1345: 1343: 1339: 1335: 1331: 1327: 1322: 1320: 1316: 1312: 1308: 1307:deep learning 1298: 1296: 1295:deep learning 1291: 1286: 1284: 1280: 1275: 1273: 1269: 1265: 1261: 1257: 1253: 1249: 1244: 1242: 1238: 1234: 1230: 1228: 1227:connectionism 1223: 1218: 1216: 1212: 1208: 1204: 1200: 1196: 1192: 1181: 1163: 1155: 1146: 1142: 1138: 1131: 1122: 1120: 1119:loss function 1116: 1112: 1108: 1098: 1096: 1092: 1088: 1083: 1081: 1080: 1079:hidden layers 1075: 1071: 1066: 1064: 1063: 1058: 1057: 1052: 1048: 1044: 1040: 1036: 1035: 1029: 1027: 1023: 1019: 1015: 1011: 1007: 1003: 999: 995: 983: 978: 976: 971: 969: 964: 963: 961: 960: 953: 950: 946: 943: 942: 941: 938: 936: 933: 932: 926: 925: 918: 915: 913: 910: 908: 905: 903: 900: 898: 895: 893: 890: 888: 885: 884: 878: 877: 870: 867: 865: 862: 860: 857: 855: 852: 850: 847: 845: 842: 840: 837: 835: 832: 831: 825: 824: 817: 814: 812: 809: 807: 804: 802: 799: 798: 792: 791: 784: 781: 779: 776: 774: 773:Crowdsourcing 771: 769: 766: 765: 759: 758: 749: 746: 745: 744: 741: 739: 736: 734: 731: 729: 726: 725: 722: 717: 716: 708: 705: 703: 702:Memtransistor 700: 698: 695: 693: 690: 686: 683: 682: 681: 678: 676: 673: 669: 666: 664: 661: 659: 656: 654: 651: 650: 649: 646: 644: 641: 639: 636: 634: 631: 627: 624: 623: 622: 619: 615: 612: 610: 607: 605: 602: 600: 597: 596: 595: 592: 590: 587: 585: 584:Deep learning 582: 580: 577: 576: 573: 568: 567: 560: 557: 555: 552: 550: 548: 544: 542: 539: 538: 535: 530: 529: 520: 519:Hidden Markov 517: 515: 512: 510: 507: 506: 505: 502: 501: 498: 493: 492: 485: 482: 480: 477: 475: 472: 470: 467: 465: 462: 460: 457: 455: 452: 450: 447: 445: 442: 441: 438: 433: 432: 425: 422: 420: 417: 415: 411: 409: 406: 404: 401: 399: 397: 393: 391: 388: 386: 383: 381: 378: 377: 374: 369: 368: 361: 358: 356: 353: 351: 348: 346: 343: 341: 338: 336: 333: 331: 328: 326: 324: 320: 316: 315:Random forest 313: 311: 308: 306: 303: 302: 301: 298: 296: 293: 291: 288: 287: 280: 279: 274: 273: 265: 259: 258: 251: 248: 246: 243: 241: 238: 236: 233: 231: 228: 226: 223: 221: 218: 216: 213: 211: 208: 206: 203: 201: 200:Data cleaning 198: 196: 193: 191: 188: 186: 183: 181: 178: 176: 173: 171: 168: 166: 163: 162: 156: 155: 148: 145: 143: 140: 138: 135: 133: 130: 128: 125: 123: 120: 118: 115: 113: 112:Meta-learning 110: 108: 105: 103: 100: 98: 95: 93: 90: 88: 85: 84: 78: 77: 74: 69: 66: 65: 61: 60: 54: 50: 46: 41: 37: 33: 19: 17708:Eye tracking 17656:technologies 17619:IEEE 802.11p 17532:Honda Legend 17511: 17239: 17227: 17175:Neuroimaging 17170:Neurogenesis 17054:Neurohistory 17019:Neurobiotics 16918:neuroscience 16886:Neurosurgery 16811:Epileptology 16793:neuroscience 16762:Neurophysics 16752:Neurometrics 16727:Neurobiology 16722:Neuroanatomy 16692:Connectomics 16626:Neuroscience 16425:Hugging Face 16389:David Silver 16037:Audio–visual 15891:Applications 15870:Augmentation 15715: 15640:Mechatronics 15617: 15448:Quantization 15411:Steady state 14975:Graph theory 14904:Evolvability 14692: 14665: 14638: 14611: 14607: 14583: 14544: 14520:. J. Wiley. 14517: 14490: 14466:. Springer. 14463: 14428: 14402: 14375: 14348: 14333:created for 14323:. Retrieved 14316:the original 14281: 14277: 14250: 14223: 14210: 14175: 14148: 14131: 14127: 14112:Bibliography 14075: 14069: 14044: 14038: 14014:(1): 39–65. 14011: 14007: 13997: 13985:. Retrieved 13974: 13965: 13938: 13934: 13894: 13890: 13844: 13840: 13807:. Retrieved 13765: 13758: 13746:. Retrieved 13719: 13715: 13675: 13671: 13621: 13617: 13595: 13552: 13548: 13538: 13526:. Retrieved 13506: 13494:. Retrieved 13485: 13475: 13463:. Retrieved 13459: 13450: 13428:(7): 14–16. 13425: 13421: 13384: 13376: 13367: 13358: 13349: 13337:. Retrieved 13333:the original 13323: 13304: 13284: 13277: 13268: 13223: 13219: 13209: 13197:. Retrieved 13154: 13150: 13140: 13119: 13076: 13072: 13066: 13054:. Retrieved 13032: 13028: 13015: 12970: 12964: 12952:. Retrieved 12936: 12929: 12886: 12882: 12876: 12864:. Retrieved 12860:the original 12855: 12846: 12807: 12798: 12786:. Retrieved 12762: 12746: 12734:. Retrieved 12704: 12664: 12660: 12654: 12642:. Retrieved 12633: 12623: 12611:. Retrieved 12594:(6): 77–80. 12591: 12587: 12574: 12523: 12519: 12509: 12497:. Retrieved 12480: 12476: 12466: 12429: 12425: 12415: 12364: 12360: 12354: 12303: 12299: 12293: 12250: 12246: 12240: 12189: 12183: 12177: 12165:. Retrieved 12156: 12147: 12135:. Retrieved 12126: 12117: 12105:. Retrieved 12096: 12086: 12051: 12045: 12019: 12013: 12001:. Retrieved 11997:the original 11992: 11983: 11948: 11942: 11917: 11913: 11907: 11895:. Retrieved 11886: 11882: 11872: 11847: 11843: 11837: 11820: 11816: 11810: 11793: 11789: 11783: 11771:. Retrieved 11762: 11758: 11748: 11703: 11699: 11689: 11677:. Retrieved 11668: 11658: 11615: 11611: 11605: 11570: 11567:Biology Open 11566: 11556: 11544:. Retrieved 11519: 11515: 11505: 11493:. Retrieved 11486:the original 11457: 11453: 11440: 11413: 11409: 11399: 11387:. Retrieved 11368: 11358: 11346:. Retrieved 11329: 11319: 11307:. Retrieved 11263: 11259: 11246: 11213: 11208: 11202: 11180:(2): 81–96. 11177: 11173: 11167: 11155:. Retrieved 11146: 11136: 11124:. Retrieved 11089: 11079: 11067:. Retrieved 11058: 11054: 11044: 11024: 10999: 10995: 10989: 10977:. Retrieved 10960: 10956: 10946: 10934:. Retrieved 10899: 10889: 10877:. Retrieved 10842: 10832: 10820:. Retrieved 10785: 10775: 10763:. Retrieved 10728: 10718: 10691: 10685: 10657: 10645:. Retrieved 10626: 10616: 10595: 10578: 10574: 10568: 10544: 10532:. Retrieved 10520: 10507: 10495:. Retrieved 10483: 10470: 10443: 10440:Scholarpedia 10439: 10426: 10414:. Retrieved 10409: 10399: 10378: 10367:the original 10353: 10312: 10308: 10301:Hochreiter S 10295: 10273: 10248: 10244: 10238: 10226:. Retrieved 10198: 10194: 10181: 10169:. Retrieved 10144:(1): 49–61. 10141: 10137: 10109:. Retrieved 10105:the original 10082: 10072: 10060:. Retrieved 10056:the original 10043: 10033: 10021:. Retrieved 10005: 9998: 9986:. Retrieved 9967:11311/255236 9945: 9935: 9892: 9886: 9868: 9862: 9850:. Retrieved 9841: 9832: 9810: 9789: 9764: 9760: 9754: 9734: 9716: 9679: 9673: 9646: 9640: 9628:. Retrieved 9601: 9591: 9564: 9560: 9554: 9542:. Retrieved 9522: 9515: 9498: 9494: 9488: 9461: 9449: 9406: 9402: 9396: 9384:. Retrieved 9364: 9357: 9345:. Retrieved 9325: 9318: 9306:. Retrieved 9286: 9279: 9274:ESANN. 2009. 9270: 9258:. Retrieved 9249: 9239: 9218: 9193: 9189: 9183: 9148: 9144: 9138: 9103: 9097: 9076: 9049: 9043: 9031:. Retrieved 9019: 9015: 9002: 8983: 8979: 8973: 8961:. Retrieved 8949: 8945: 8932: 8920:. Retrieved 8916:the original 8911: 8902: 8878:(1): 47–66. 8875: 8871: 8861: 8836: 8832: 8826: 8807: 8802: 8775: 8771: 8737: 8731: 8722: 8712: 8703: 8693: 8668: 8664: 8648: 8627: 8615:. Retrieved 8611: 8601: 8564: 8559: 8552: 8531: 8512: 8506: 8485: 8467: 8461: 8452: 8448: 8435: 8423:. Retrieved 8409: 8388: 8376:. Retrieved 8371: 8362: 8319: 8315: 8306: 8297: 8288: 8276:. Retrieved 8260: 8231: 8201: 8192: 8179: 8158: 8146:. Retrieved 8134: 8121: 8076: 8070: 8035: 8029: 8017:. Retrieved 8001: 7994: 7982:. Retrieved 7962: 7949: 7898: 7894: 7888: 7876:. Retrieved 7872:the original 7867: 7858: 7839: 7798: 7794: 7777: 7727: 7723: 7702: 7678: 7664: 7650:cite journal 7625: 7621: 7611: 7584: 7578: 7545: 7541: 7538:Hochreiter S 7532: 7515: 7501: 7489:. Retrieved 7470: 7460: 7452: 7419: 7412: 7387: 7383: 7370: 7361: 7345: 7333:. Retrieved 7329: 7320: 7295:10261/348372 7277: 7267: 7222: 7218: 7208: 7183: 7179: 7169: 7161: 7156: 7149:FEBS letters 7148: 7142: 7134: 7129: 7117:. Retrieved 7087: 7083: 7070: 7037: 7033: 7023: 6990: 6986: 6976: 6943: 6939: 6929: 6921: 6917: 6912: 6903: 6893: 6883: 6875: 6862: 6855: 6830: 6826: 6816: 6783: 6779: 6769: 6757:. Retrieved 6745: 6729: 6701: 6694: 6669: 6665: 6662:Linnainmaa S 6656: 6647: 6644:Linnainmaa S 6638: 6621: 6617: 6608: 6588: 6581: 6538: 6534: 6490: 6487:Biol. Cybern 6486: 6480: 6447: 6418: 6411: 6390: 6355: 6351: 6345: 6328: 6324: 6318: 6309: 6305: 6296: 6277: 6273: 6260: 6248:. Retrieved 6228: 6215: 6198: 6194: 6184: 6164: 6157: 6148: 6145:Rosenblatt F 6113: 6106: 6097: 6055: 6051: 6045: 6036: 6030: 6020: 6013: 5970: 5966: 5960: 5951: 5937:(3): 80–93. 5934: 5930: 5924: 5910:(4): 76–84. 5907: 5903: 5897: 5877: 5870: 5858:. Retrieved 5853: 5843: 5818: 5814: 5780: 5769: 5723: 5717: 5698: 5694: 5684: 5675: 5656: 5650: 5638:. Retrieved 5628: 5599: 5571: 5565: 5553:. Retrieved 5533: 5526: 5514:. Retrieved 5499: 5468: 5462: 5224:Connectomics 5162:transformers 5155: 5142: 5129: 5110: 5095: 5086: 5077: 5068: 5059: 4785: 4782:Dataset bias 4773: 4764: 4744: 4729: 4710: 4701: 4688: 4683: 4679: 4675: 4663: 4653: 4640: 4627: 4530: 4519: 4500: 4485: 4483: 4471: 4456: 4447: 4436:Please help 4428: 4384: 4380:Saddle point 4377: 4373: 4359:VC dimension 4355: 4346:super-Turing 4327: 4312: 4298: 4295: 4280: 4256: 4253: 4092: 4089:Applications 4085: 3296: 3274: 3249: 3222: 3214: 3195: 3180: 3171: 3160:Please help 3152: 3085: 3075: 3072:local minima 3068:optimization 3052: 3051: 3036: 3021: 3013: 3011: 2982:neurodynamic 2975: 2971:Markov chain 2729:and actions 2662: 2649: 2393: 2365: 2337: 2317: 2302: 2293: 2282:Please help 2274: 2231: 2216: 2190: 2168: 2149:optimization 2136: 2107: 2098: 2087:Please help 2079: 2032: 2015: 2009: 2000: 1996: 1993:output layer 1992: 1988: 1982: 1979:Organization 1968: 1964: 1960: 1956: 1954: 1949: 1939: 1922: 1910: 1881: 1872: 1861:Please help 1852: 1801: 1795: 1774: 1720: 1710: 1691: 1656: 1639: 1629: 1593: 1574: 1555: 1540: 1504: 1490: 1484: 1449: 1431: 1392: 1379:Neocognitron 1372: 1361: 1346: 1323: 1317:and Lapa in 1304: 1287: 1276: 1245: 1237:Walter Pitts 1231: 1219: 1188: 1104: 1084: 1077: 1074:output layer 1073: 1069: 1067: 1060: 1054: 1042: 1032: 1030: 1013: 1009: 1005: 1001: 997: 991: 859:PAC learning 546: 395: 390:Hierarchical 322: 276: 270: 36: 17614:Legislation 17561:ParkShuttle 17044:Neuroethics 16891:Neurotology 16573:Categories 16521:Autoencoder 16476:Transformer 16344:Alex Graves 16292:OpenAI Five 16196:IBM Watsonx 15818:Convolution 15796:Overfitting 15570:Controllers 15511:Fuzzy logic 15470:Z-transform 15374:Performance 15174:Autopoiesis 15169:Cybernetics 15159:Sensemaking 15129:Homeostasis 15091:Percolation 15053:Bifurcation 15028:Phase space 14914:Game theory 14771:Collective 13897:(5): 3186. 13809:30 December 12769:: 326–334. 12137:19 November 12003:15 February 11850:: 128–140. 11731:10045/81208 11149:(1): 8–12. 10963:: 652–661. 10412:: 1964–1968 10201:(1): 1–16. 10062:30 December 9988:30 December 9196:: 182–188. 8612:The AI Blog 8425:25 November 6618:ARS Journal 5470:3Blue1Brown 5194:Autoencoder 4662:, a former 4450:August 2019 4442:introducing 4370:Convergence 4334:real number 4243:E-mail spam 4196:(including 4194:Data mining 4171:handwritten 3174:August 2019 3166:introducing 2624:compression 2546:compression 2296:August 2019 2288:introducing 2176:oscillation 2151:theory and 2101:August 2019 2093:introducing 1989:input layer 1961:connections 1768:(2022) and 1687:Inceptionv3 1652:max-pooling 1600:Peter Dayan 1515:Ising model 1456:Alex Waibel 1441:max pooling 1418:Paul Werbos 1400:derived by 1328:trained by 1213:(1805) and 1141:sea urchins 1070:input layer 1051:real number 743:Multi-agent 680:Transformer 579:Autoencoder 335:Naive Bayes 73:data mining 17829:Categories 17742:and events 17606:Regulation 17351:SAE Levels 17205:Neurotoxin 16906:Psychiatry 16562:Technology 16415:EleutherAI 16374:Fei-Fei Li 16369:Yann LeCun 16282:Q-learning 16265:Decisional 16191:IBM Watson 16099:Midjourney 15991:TensorFlow 15838:Activation 15791:Regression 15786:Clustering 15384:Root locus 14985:Robustness 14965:Centrality 14851:adaptation 14749:Background 14178:. Vieweg. 13987:9 December 13841:Algorithms 13748:9 December 13388:J. Weng, " 13233:1802.07569 13164:2007.14313 13131:1906.09235 13086:1901.06523 13042:1806.08734 12980:1807.01251 12896:1902.06720 12613:10 January 12499:20 January 12432:(27): 27. 12374:1902.10104 12313:1902.05131 12260:1902.07006 12199:1902.09483 12167:20 January 11625:1708.08551 11309:31 January 11157:21 October 11147:RiuNet UPV 10669:1502.02127 10636:1806.10282 10607:1611.01578 10559:1712.01815 10484:Google.com 10278:Yann LeCun 10228:30 January 10171:30 January 9902:2007.06823 9852:7 February 9823:1712.06567 9801:1703.03864 9416:1705.05584 9409:: 97–116. 9230:1507.07680 9088:1905.00094 9068:1162184998 8922:4 November 8639:1706.03762 8574:1512.03385 8543:1505.00387 8522:1512.03385 8497:1502.01852 8400:1710.10196 8329:1906.04493 6541:: 85–117. 6402:1710.05941 6365:1505.03654 6280:(3): 400. 6250:5 November 6195:Automatica 5775:Stigler SM 5760:2212.11279 5492:References 5461:"But what 5389:Audio help 5380:2011-11-27 5279:Neural gas 4757:, or TPU. 4342:irrational 4260:geoscience 4188:prostheses 3288:TensorFlow 3269:Robustness 3198:stochastic 3064:stochastic 2644:See also: 2616:clustering 2612:estimation 2352:regression 2123:See also: 1969:activation 1875:April 2017 1857:to readers 1618:, and the 1480:Yann LeCun 1460:Yann LeCun 1398:chain rule 1264:perception 1252:hypothesis 1248:D. O. Hebb 1191:statistics 1185:Early work 1024:in animal 1006:neural net 728:Q-learning 626:Restricted 424:Mean shift 373:Clustering 350:Perceptron 278:regression 180:Clustering 175:Regression 17740:projects 17654:Enabling 17642:Liability 17514:Autopilot 17150:Neurochip 16916:Cognitive 16841:Neurology 16445:MIT CSAIL 16410:Anthropic 16379:Andrew Ng 16277:AlphaZero 16121:VideoPoet 16084:AlphaFold 16021:MindSpore 15975:SpiNNaker 15970:Memristor 15877:Diffusion 15853:Rectifier 15833:Batchnorm 15813:Attention 15808:Adversary 15453:Real time 15405:Stability 15329:Bode plot 15033:Attractor 14846:Evolution 14756:Emergence 14579:Ripley BD 14571:242963768 14482:837524179 14325:28 August 14286:CiteSeerX 14202:Cybenko G 14061:2213-0217 14030:0941-0643 13957:1522-8053 13913:2076-3417 13863:1999-4893 13796:198183828 13738:2754-1169 13694:2661-4332 13555:: 18–27. 13252:0893-6080 13226:: 54–71. 13199:5 October 13189:220831156 13181:2374-3468 12838:204837170 12700:MacKay DJ 12669:CiteSeerX 12548:1476-4687 12407:119504484 12346:119357494 12285:119470636 12232:119074378 11740:0038-0806 11379:0099-9660 11338:0362-4331 11194:157962452 10869:219735060 10755:240396965 10647:21 August 10390:1410.4281 10329:0899-7667 10111:1 January 10023:8 October 9927:220514248 9919:1556-603X 9684:CiteSeerX 9569:CiteSeerX 9153:CiteSeerX 9033:21 August 8794:249017987 8754:208117506 8723:ICML 2021 8704:ICML 2020 8477:1409.1556 8378:3 October 8354:216056336 8322:: 58–66. 8278:20 August 8234:. Wiley. 8213:1112.6209 8170:1409.1556 8086:1202.2745 7925:0899-7667 7908:1003.0358 7783:Hinton GE 7712:Hinton GE 7642:0364-0213 7525:Q98967430 7304:1073-8584 7200:0018-9340 7119:7 October 7092:CiteSeerX 6847:0031-3203 6808:1476-4687 6686:122357351 6614:Kelley HJ 6548:1404.7828 6515:206775608 6472:206775608 6266:Robbins H 5975:CiteSeerX 5835:0007-4985 5695:Ann. Stat 5274:Memristor 4950:− 4931:∗ 4909:∑ 4902:∗ 4696:attention 4632:Criticism 4576:∑ 4264:hydrology 4245:filtering 2775:∈ 2713:∈ 2628:filtering 2484:− 2197:convexity 2172:Quickprop 2144:statistic 2039:parameter 1932:neurons. 1758:deepfakes 1692:In 2012, 1575:In 1991, 1347:In 1969, 1272:Rochester 887:ECML PKDD 869:VC theory 816:ROC curve 748:Self-play 668:DeepDream 509:Bayes net 300:Ensembles 81:Paradigms 17571:NuTonomy 17556:CAVForth 17544:vehicles 17466:Vehicles 17229:Category 17113:Concepts 17059:Neurolaw 16791:Clinical 16553:Portals 16312:Auto-GPT 16144:Word2vec 15948:Hardware 15865:Datasets 15767:Concepts 15655:Robotics 15455:software 15233:Branches 15139:Feedback 14947:Networks 14773:behavior 14684:27429729 14657:27145760 14581:(2007). 14536:29877717 14509:32179420 14455:52377690 14421:21522159 14394:38908586 14367:37875698 14269:41347061 14242:35558945 14204:(2006). 14194:76538146 14167:33101074 14102:17555595 13981:Archived 13800:Archived 13742:Archived 13650:34693373 13618:Patterns 13587:25540468 13519:Archived 13490:Archived 13465:7 August 13442:11026540 13393:Archived 13312:Archived 13260:30780045 13193:Archived 13111:58981616 13047:Archived 13007:49562099 12945:Archived 12921:62841516 12866:10 March 12804:Gerald F 12788:10 March 12779:Archived 12754:(1965). 12727:Archived 12702:(2003). 12638:Archived 12634:EE Times 12604:Archived 12566:38030720 12557:10700131 12493:Archived 12458:25928094 12399:31347877 12338:31347862 12224:31347886 12161:Archived 12131:Archived 12101:Archived 12078:13260377 11891:Archived 11767:Archived 11679:14 March 11673:Archived 11650:36661983 11597:26873952 11546:28 March 11540:Archived 11536:27735002 11482:18182063 11383:Archived 11348:22 April 11342:Archived 11300:Archived 11288:26819042 11238:19708729 11151:Archived 11126:18 March 11120:Archived 11116:25739012 11063:Archived 11033:Archived 11016:27286184 10973:Archived 10936:20 March 10930:Archived 10926:37566823 10879:20 March 10873:Archived 10822:22 March 10816:Archived 10812:14948018 10765:20 March 10759:Archived 10641:Archived 10587:88515435 10550:Silver D 10525:Archived 10488:Archived 10434:(2015). 10285:Archived 10265:41312633 10219:Archived 10215:16095286 10162:Archived 10014:Archived 9982:Archived 9846:Archived 9743:Archived 9624:Archived 9538:Archived 9441:27910748 9380:Archived 9347:22 March 9341:Archived 9308:22 March 9302:Archived 9254:Archived 9210:23140797 9130:10557754 9024:Archived 8954:Archived 8853:17698099 8685:16683347 8656:(1992). 8419:Archived 8346:32334341 8269:Archived 8193:Cvpr2015 8139:Archived 8062:24579167 8010:Archived 7975:Archived 7933:20858131 7847:Archived 7720:Zemel RS 7521:Wikidata 7485:Archived 7445:Archived 7404:18271205 7312:37403768 7114:14542261 7015:20706526 6968:20577468 6882:et al., 6750:Archived 6737:(1982). 6735:Werbos P 6646:(1970). 6573:11715509 6565:25462637 6382:12149203 6241:Archived 6147:(1962). 6080:16786738 6005:12781225 5997:13602029 5777:(1986). 5634:Archived 5549:Archived 5510:Archived 5475:Archived 5450:Archived 5432:Archived 5410:Archived 5391: · 5182:See also 5176:Jukedeck 5139:Medicine 4707:Hardware 4656:emergent 4637:Training 4352:Capacity 4330:rational 4184:Robotics 3723:keepdims 3465:n_hidden 3435:n_hidden 3405:n_hidden 3333:n_hidden 3218:topology 3088:Bayesian 2994:medicine 2552:between 2395:a priori 2220:gradient 2049:Learning 1914:directed 1818:such as 1777:training 1772:(2022). 1766:DALL·E 2 1754:StyleGAN 1585:residual 1491:profiles 1211:Legendre 1145:features 1137:starfish 1101:Training 1047:synapses 310:Boosting 159:Problems 17294:context 17241:Commons 16654:science 16642:History 16637:Outline 16435:Meta AI 16272:AlphaGo 16256:PanGu-Σ 16226:ChatGPT 16201:Granite 16149:Seq2seq 16128:Whisper 16049:WaveNet 16044:AlexNet 16016:Flux.jl 15996:PyTorch 15848:Sigmoid 15843:Softmax 15708:General 15459:Sampled 15289:control 15283:control 15267:control 15164:Entropy 14980:Scaling 14630:2456483 14433:Bibcode 13641:8515002 13578:4268588 13557:Bibcode 13496:5 March 13407:, 2012. 13339:12 July 13091:Bibcode 12901:Bibcode 12752:Cover T 12736:11 June 12528:Bibcode 12449:4417229 12379:Bibcode 12318:Bibcode 12265:Bibcode 12204:Bibcode 11975:8838479 11922:Bibcode 11852:Bibcode 11708:Bibcode 11630:Bibcode 11588:4810736 11474:9274582 11418:Bibcode 11389:16 June 11268:Bibcode 11218:Bibcode 11069:5 March 10979:18 July 10675:Bibcode 10629:. ACM. 10534:27 June 10497:27 June 10448:Bibcode 10416:13 June 10345:1915014 10337:9377276 10158:6233899 9781:8944741 9630:29 July 9544:17 June 9421:Bibcode 9386:28 July 9260:27 June 8880:Bibcode 8617:29 June 8113:2161592 8019:13 June 7984:13 June 7941:1918673 7878:16 June 7823:7761831 7803:Bibcode 7795:Science 7791:Frey BJ 7787:Dayan P 7762:1890561 7754:7584891 7716:Neal RM 7708:Peter D 7684:194–281 7570:1915014 7562:9377276 7491:26 June 7455:, 1991. 7335:27 July 7259:6953413 7227:Bibcode 7062:8058017 7042:Bibcode 6995:Bibcode 6948:Bibcode 6788:Bibcode 6507:7370364 6464:7370364 6302:Amari S 5860:17 June 5555:28 July 5479:YouTube 5378: ( 5349:minutes 5189:ADALINE 5101:Finance 4808:Gallery 4438:improve 4348:power. 4205:ex-ante 4163:gesture 3579:sigmoid 3399:n_input 3357:n_input 3255:slowly. 3162:improve 2284:improve 2089:improve 2001:pooling 1959:of the 1957:weights 1946:neurons 1853:may be 1820:ChatGPT 1798:seq2seq 1724:(GAN) ( 1659:AlexNet 1562:ChatGPT 1319:Ukraine 1174:History 1115:labeled 1039:neurons 1016:) is a 892:NeurIPS 709:(ECRAM) 663:AlexNet 305:Bagging 45:neurons 17793:People 17764:Navlab 17731:People 17534:(2021) 17528:(2017) 17522:(2015) 17516:(2015) 17500:(2011) 17494:(2010) 17488:(2007) 17482:(1994) 16977:fields 16450:Huawei 16430:OpenAI 16332:People 16302:MuZero 16164:Gemini 16159:Claude 16094:DALL-E 16006:Theano 15407:theory 15287:Neural 15265:Hybrid 14970:Motifs 14699: 14682: 14672: 14655: 14645: 14628: 14591: 14569: 14559: 14534: 14524: 14507: 14497: 14480: 14470: 14453: 14443: 14419: 14409: 14392: 14382: 14365: 14355: 14288: 14267: 14257: 14240: 14230: 14192: 14182: 14165: 14155: 14100: 14090: 14059: 14028: 13955: 13911: 13861: 13794: 13784: 13736: 13692: 13648: 13638: 13585: 13575: 13528:6 July 13440: 13403: 13292: 13258: 13250: 13187: 13179: 13109: 13056:4 June 13005: 12995: 12954:4 June 12919: 12836: 12826: 12719: 12671: 12644:11 May 12564: 12554: 12546: 12520:Nature 12456: 12446: 12405: 12397: 12344: 12336: 12283: 12230: 12222: 12107:12 May 12076: 12066: 12034: 11973: 11963: 11897:5 July 11773:17 May 11738: 11648: 11595: 11585: 11534: 11480: 11472: 11454:Lancet 11377: 11336: 11296:515925 11294: 11286: 11260:Nature 11236: 11192: 11114: 11104: 11014: 10924: 10914: 10867: 10857: 10810: 10800: 10753: 10743: 10706: 10585: 10343: 10335: 10327: 10282:Online 10263: 10213: 10156: 10097: 9974: 9925: 9917: 9875: 9779: 9725: 9704: 9686: 9661: 9616: 9571: 9530: 9476: 9439: 9372: 9333: 9294: 9208: 9175:116858 9173: 9155: 9128: 9118: 9066: 9056: 8990: 8963:7 July 8851: 8814: 8792: 8782: 8752: 8683: 8589: 8352: 8344: 8238: 8148:24 May 8111: 8101: 8060: 8050: 7939: 7931: 7923: 7831:871473 7829: 7821: 7760: 7752: 7690: 7640: 7599: 7568: 7560: 7523: 7477: 7402: 7310: 7302: 7257: 7250:346238 7247: 7198: 7112: 7094: 7060: 7013: 6966: 6918:et al. 6916:LeCun 6845: 6806: 6780:Nature 6759:2 July 6717: 6684: 6596: 6571: 6563: 6513: 6505: 6470: 6462: 6426: 6380: 6172: 6124: 6078: 6072:285702 6070: 6003: 5995: 5977: 5885: 5833: 5789: 5663: 5640:1 June 5606: 5578: 5541: 5516:2 June 5167:DALL-E 5160:) and 4797:Amazon 4776:hybrid 4732:GPGPUs 4721:memory 4650:Theory 4270:, and 4169:, and 4167:speech 4136:, and 4077:return 4008:square 3525:n_iter 3453:random 3387:random 3345:n_iter 3306:Python 3280:AutoML 2435:where 2193:ad hoc 2131:, and 1834:Models 1826:, and 1750:Nvidia 1669:, and 1646:, and 1640:DanNet 1570:layers 1368:Papert 1364:Minsky 1062:weight 1026:brains 1000:(also 685:Vision 541:RANSAC 419:OPTICS 414:DBSCAN 398:-means 205:AutoML 17703:rFpro 17673:LIDAR 17668:Laser 17663:Radar 17504:Waymo 17444:(V2V) 16652:Basic 16516:Mamba 16287:SARSA 16251:LLaMA 16246:BLOOM 16231:GPT-J 16221:GPT-4 16216:GPT-3 16211:GPT-2 16206:GPT-1 16169:LaMDA 16001:Keras 15386:ethod 15043:Chaos 14626:S2CID 14567:S2CID 14319:(PDF) 14312:(PDF) 14124:(PDF) 14098:S2CID 13803:(PDF) 13792:S2CID 13770:(PDF) 13522:(PDF) 13515:(PDF) 13486:Wired 13438:S2CID 13228:arXiv 13185:S2CID 13159:arXiv 13126:arXiv 13107:S2CID 13081:arXiv 13050:(PDF) 13037:arXiv 13025:(PDF) 13003:S2CID 12975:arXiv 12948:(PDF) 12941:(PDF) 12917:S2CID 12891:arXiv 12834:S2CID 12782:(PDF) 12759:(PDF) 12730:(PDF) 12709:(PDF) 12607:(PDF) 12584:(PDF) 12403:S2CID 12369:arXiv 12342:S2CID 12308:arXiv 12281:S2CID 12255:arXiv 12228:S2CID 12194:arXiv 12157:InfoQ 12074:S2CID 11971:S2CID 11646:S2CID 11620:arXiv 11495:2 May 11489:(PDF) 11478:S2CID 11450:(PDF) 11303:(PDF) 11292:S2CID 11256:(PDF) 11190:S2CID 11112:S2CID 11061:(3). 10922:S2CID 10865:S2CID 10808:S2CID 10751:S2CID 10664:arXiv 10631:arXiv 10602:arXiv 10583:S2CID 10554:arXiv 10528:(PDF) 10517:(PDF) 10491:(PDF) 10480:(PDF) 10385:arXiv 10370:(PDF) 10363:(PDF) 10341:S2CID 10261:S2CID 10222:(PDF) 10211:S2CID 10191:(PDF) 10165:(PDF) 10154:S2CID 10134:(PDF) 10017:(PDF) 10010:(PDF) 9923:S2CID 9897:arXiv 9818:arXiv 9796:arXiv 9777:S2CID 9437:S2CID 9411:arXiv 9225:arXiv 9171:S2CID 9126:S2CID 9083:arXiv 9027:(PDF) 9022:(1). 9012:(PDF) 8982:[ 8957:(PDF) 8942:(PDF) 8774:[ 8750:S2CID 8681:S2CID 8661:(PDF) 8634:arXiv 8569:arXiv 8538:arXiv 8517:arXiv 8492:arXiv 8472:arXiv 8445:(PDF) 8395:arXiv 8350:S2CID 8324:arXiv 8272:(PDF) 8265:(PDF) 8208:arXiv 8189:(PDF) 8165:arXiv 8142:(PDF) 8131:(PDF) 8109:S2CID 8081:arXiv 8013:(PDF) 8006:(PDF) 7978:(PDF) 7959:(PDF) 7937:S2CID 7903:arXiv 7827:S2CID 7758:S2CID 7674:(PDF) 7566:S2CID 7424:(PDF) 7400:S2CID 7380:(PDF) 7358:(PDF) 7110:S2CID 7080:(PDF) 6867:(PDF) 6753:(PDF) 6742:(PDF) 6682:S2CID 6569:S2CID 6543:arXiv 6511:S2CID 6468:S2CID 6397:arXiv 6378:S2CID 6360:arXiv 6244:(PDF) 6225:(PDF) 6118:(PDF) 6076:S2CID 6068:JSTOR 6001:S2CID 5755:arXiv 5487:Notes 4748:or a 4740:FPGAs 4713:graph 4423:, or 4317:is a 4100:, or 4080:model 4020:model 3966:print 3519:range 3489:zeros 3459:randn 3423:zeros 3393:randn 3369:shape 3315:train 3292:Keras 3205:Types 3147:, or 3122:Modes 3086:In a 3082:Other 3002:games 2269:, or 2074:, or 1929:links 1925:nodes 1824:GPT-4 1527:Cajal 1476:LeNet 1464:LeNet 1268:Clark 1215:Gauss 1043:edges 1018:model 907:IJCAI 733:SARSA 692:Mamba 658:LeNet 653:U-Net 479:t-SNE 403:Fuzzy 380:BIRCH 49:brain 47:in a 17754:DAVI 17573:taxi 17512:with 17480:VaMP 17473:Cars 16440:Mila 16241:PaLM 16174:Bard 16154:BERT 16137:Text 16116:Sora 15461:data 14848:and 14697:ISBN 14680:OCLC 14670:ISBN 14653:OCLC 14643:ISBN 14589:ISBN 14557:ISBN 14532:OCLC 14522:ISBN 14505:OCLC 14495:ISBN 14478:OCLC 14468:ISBN 14451:OCLC 14441:ISBN 14417:OCLC 14407:ISBN 14390:OCLC 14380:ISBN 14363:OCLC 14353:ISBN 14327:2006 14265:OCLC 14255:ISBN 14238:OCLC 14228:ISBN 14190:OCLC 14180:ISBN 14163:OCLC 14153:ISBN 14088:ISBN 14057:ISSN 14026:ISSN 13989:2023 13976:IEEE 13953:ISSN 13909:ISSN 13859:ISSN 13811:2023 13782:ISBN 13750:2023 13734:ISSN 13690:ISSN 13646:PMID 13583:PMID 13530:2022 13498:2017 13467:2024 13401:ISBN 13341:2010 13290:ISBN 13256:PMID 13248:ISSN 13201:2021 13177:ISSN 13058:2022 12993:ISBN 12956:2022 12887:2020 12868:2020 12824:ISBN 12790:2020 12767:IEEE 12738:2016 12717:ISBN 12646:2023 12615:2017 12562:PMID 12544:ISSN 12501:2024 12454:PMID 12395:PMID 12334:PMID 12220:PMID 12169:2021 12139:2020 12109:2021 12064:ISBN 12032:ISBN 12005:2019 11961:ISBN 11899:2017 11775:2023 11736:ISSN 11681:2018 11593:PMID 11548:2017 11532:PMID 11497:2012 11470:PMID 11391:2017 11375:ISSN 11350:2023 11334:ISSN 11311:2019 11284:PMID 11234:PMID 11159:2021 11128:2021 11102:ISBN 11071:2013 11012:PMID 10981:2019 10938:2023 10912:ISBN 10881:2023 10855:ISBN 10824:2023 10798:ISBN 10767:2023 10741:ISBN 10704:ISBN 10649:2019 10536:2017 10499:2017 10418:2017 10333:PMID 10325:ISSN 10230:2019 10173:2019 10113:2012 10095:ISBN 10064:2011 10025:2012 9990:2011 9972:ISBN 9915:ISSN 9873:ISBN 9854:2018 9723:ISBN 9702:ISBN 9659:ISBN 9632:2013 9614:ISBN 9546:2017 9528:ISBN 9474:ISBN 9388:2022 9370:ISBN 9349:2023 9331:ISBN 9310:2023 9292:ISBN 9262:2017 9206:PMID 9116:ISBN 9064:OCLC 9054:ISBN 9035:2019 8988:ISBN 8965:2022 8924:2009 8849:PMID 8812:ISBN 8790:OCLC 8780:ISBN 8619:2024 8587:ISBN 8427:2020 8380:2019 8342:PMID 8280:2019 8236:ISBN 8150:2017 8099:ISBN 8058:PMID 8048:ISBN 8021:2017 7986:2017 7929:PMID 7921:ISSN 7880:2017 7819:PMID 7750:PMID 7688:ISBN 7656:link 7638:ISSN 7597:ISBN 7558:PMID 7493:2017 7475:ISBN 7337:2024 7308:PMID 7300:ISSN 7255:PMID 7196:ISSN 7184:C-21 7121:2016 7058:PMID 7011:PMID 6964:PMID 6843:ISSN 6804:ISSN 6761:2017 6715:ISBN 6594:ISBN 6561:PMID 6503:PMID 6460:PMID 6424:ISBN 6252:2019 6170:ISBN 6122:ISBN 5993:PMID 5883:ISBN 5862:2017 5831:ISSN 5787:ISBN 5661:ISBN 5642:2016 5604:ISBN 5576:ISBN 5557:2022 5539:ISBN 5518:2022 5174:and 5172:AIVA 5115:and 4736:GPUs 4734:(on 4644:CMAC 4313:The 3996:mean 3954:1000 3846:axis 3729:True 3711:axis 3304:The 3110:and 2992:and 2626:and 2574:and 2326:and 1965:bias 1927:via 1828:BERT 1704:and 1702:GPUs 1698:Dean 1696:and 1681:and 1634:and 1535:Hebb 1466:for 1450:The 1366:and 1353:ReLU 1235:and 1139:and 996:, a 917:JMLR 902:ICLR 897:ICML 783:RLHF 599:LSTM 385:CURE 71:and 16181:NMT 16064:OCR 16059:HWR 16011:JAX 15965:VPU 15960:TPU 15955:IPU 15779:SGD 14616:doi 14612:131 14549:doi 14296:doi 14217:PDF 14136:doi 14080:doi 14049:doi 14016:doi 13943:doi 13899:doi 13849:doi 13774:doi 13724:doi 13680:doi 13636:PMC 13626:doi 13573:PMC 13565:doi 13430:doi 13375:," 13238:doi 13224:113 13169:doi 13099:doi 12985:doi 12909:doi 12816:doi 12812:ACM 12771:doi 12679:doi 12596:doi 12552:PMC 12536:doi 12524:624 12485:doi 12444:PMC 12434:doi 12387:doi 12365:122 12326:doi 12304:122 12273:doi 12212:doi 12190:122 12056:doi 12024:doi 11953:doi 11930:doi 11860:doi 11825:doi 11798:doi 11726:hdl 11716:doi 11638:doi 11583:PMC 11575:doi 11524:doi 11462:doi 11458:350 11426:doi 11276:doi 11264:529 11226:doi 11214:131 11182:doi 11094:doi 11004:doi 10965:doi 10904:doi 10847:doi 10790:doi 10733:doi 10696:doi 10456:doi 10317:doi 10253:doi 10203:doi 10146:doi 10087:doi 10048:doi 9962:hdl 9954:doi 9907:doi 9769:doi 9694:doi 9651:doi 9606:doi 9579:doi 9503:doi 9466:doi 9429:doi 9198:doi 9163:doi 9108:doi 8888:doi 8841:doi 8837:178 8742:doi 8673:doi 8579:doi 8334:doi 8320:127 8091:doi 8040:doi 7967:doi 7913:doi 7811:doi 7799:268 7740:hdl 7732:doi 7630:doi 7589:doi 7550:doi 7451:," 7392:doi 7290:hdl 7282:doi 7245:PMC 7235:doi 7188:doi 7102:doi 7050:doi 7003:doi 6956:doi 6835:doi 6796:doi 6784:323 6707:doi 6674:doi 6626:doi 6553:doi 6495:doi 6452:doi 6370:doi 6333:doi 6282:doi 6233:doi 6203:doi 6060:doi 5985:doi 5939:doi 5912:doi 5823:doi 5703:doi 5158:GAN 5119:: 4725:CPU 4017:))) 4014:dz3 3936:db2 3915:db1 3894:dw2 3873:dw1 3840:dz2 3834:sum 3822:db1 3816:dz2 3798:dot 3786:dw1 3753:dz3 3747:dot 3735:dz2 3705:dz3 3699:sum 3687:db2 3681:dz3 3663:dot 3651:dw2 3636:dz3 3603:dot 3549:dot 3510:for 3312:def 3290:or 2366:In 1760:. 1752:'s 1661:by 1553:. 1517:by 1435:'s 1205:or 1012:or 1010:ANN 1004:or 992:In 643:SOM 633:GAN 609:ESN 604:GRU 549:-NN 484:SDL 474:PGD 469:PCA 464:NMF 459:LDA 454:ICA 449:CCA 325:-NN 17831:: 14678:. 14651:. 14624:. 14610:. 14606:. 14565:. 14555:. 14530:. 14503:. 14476:. 14449:. 14439:. 14415:. 14388:. 14361:. 14294:. 14282:35 14280:. 14263:. 14236:. 14188:. 14161:. 14132:39 14130:. 14126:. 14096:. 14086:. 14055:. 14047:. 14024:. 14012:33 14010:. 14006:. 13979:. 13973:. 13951:. 13939:25 13937:. 13933:. 13921:^ 13907:. 13895:13 13893:. 13889:. 13871:^ 13857:. 13843:. 13839:. 13819:^ 13798:. 13790:. 13780:. 13740:. 13732:. 13720:23 13718:. 13714:. 13702:^ 13688:. 13674:. 13670:. 13658:^ 13644:. 13634:. 13620:. 13616:. 13604:^ 13581:. 13571:. 13563:. 13553:42 13551:. 13547:. 13517:. 13488:. 13484:. 13458:. 13436:. 13426:58 13424:. 13412:^ 13357:. 13254:. 13246:. 13236:. 13222:. 13218:. 13191:. 13183:. 13175:. 13167:. 13155:35 13153:. 13149:. 13105:. 13097:. 13089:. 13077:28 13075:. 13045:. 13033:97 13031:. 13027:. 13001:. 12991:. 12983:. 12915:. 12907:. 12899:. 12885:. 12854:. 12832:. 12822:. 12810:. 12777:. 12761:. 12725:. 12715:. 12711:. 12691:^ 12677:. 12665:43 12663:. 12636:. 12632:. 12602:. 12590:. 12586:. 12560:. 12550:. 12542:. 12534:. 12522:. 12518:. 12491:. 12481:36 12479:. 12475:. 12452:. 12442:. 12430:16 12428:. 12424:. 12401:. 12393:. 12385:. 12377:. 12363:. 12340:. 12332:. 12324:. 12316:. 12302:. 12279:. 12271:. 12263:. 12251:99 12249:. 12226:. 12218:. 12210:. 12202:. 12188:. 12159:. 12155:. 12129:. 12125:. 12099:. 12095:. 12072:. 12062:. 12030:, 11991:. 11969:. 11959:. 11928:. 11918:66 11885:. 11881:. 11858:. 11848:94 11846:. 11819:. 11792:. 11763:24 11761:. 11757:. 11734:. 11724:. 11714:. 11704:58 11702:. 11698:. 11671:. 11667:. 11644:. 11636:. 11628:. 11616:33 11614:. 11591:. 11581:. 11569:. 11565:. 11538:. 11530:. 11518:. 11514:. 11476:. 11468:. 11456:. 11452:. 11424:. 11412:. 11408:. 11381:. 11373:. 11367:. 11340:. 11332:. 11328:. 11298:. 11290:. 11282:. 11274:. 11262:. 11258:. 11232:. 11224:. 11188:. 11178:46 11176:. 11145:. 11118:. 11110:. 11100:. 11088:. 11059:12 11057:. 11053:. 11010:. 11000:75 10998:. 10971:. 10961:35 10959:. 10955:. 10928:. 10920:. 10910:. 10898:. 10871:. 10863:. 10853:. 10845:. 10841:. 10814:. 10806:. 10796:. 10784:. 10757:. 10749:. 10739:. 10727:. 10702:. 10639:. 10625:. 10579:20 10577:. 10523:. 10519:. 10482:. 10454:. 10444:10 10442:. 10438:. 10408:. 10339:. 10331:. 10323:. 10311:. 10303:, 10259:. 10247:. 10217:. 10209:. 10199:22 10197:. 10193:. 10160:. 10152:. 10142:19 10140:. 10136:. 10121:^ 10093:. 9980:. 9970:. 9960:. 9948:. 9921:. 9913:. 9905:. 9840:. 9775:. 9765:32 9763:. 9700:. 9692:. 9657:. 9622:. 9612:. 9600:. 9577:. 9565:27 9563:. 9536:. 9499:16 9497:. 9472:. 9460:. 9435:. 9427:. 9419:. 9407:60 9405:. 9378:. 9339:. 9300:. 9252:. 9248:. 9204:. 9194:37 9192:. 9169:. 9161:. 9149:70 9147:. 9124:. 9114:. 9062:. 9018:. 9014:. 8948:. 8944:. 8910:. 8886:. 8876:43 8874:. 8870:. 8847:. 8835:. 8788:. 8762:^ 8748:. 8702:. 8679:. 8667:. 8663:. 8610:. 8585:. 8577:. 8563:. 8515:. 8470:, 8453:37 8451:. 8447:. 8370:. 8348:. 8340:. 8332:. 8318:. 8250:^ 8222:^ 8191:. 8137:. 8133:. 8107:. 8097:. 8089:. 8056:. 8046:. 7973:. 7965:. 7961:. 7935:. 7927:. 7919:. 7911:. 7899:22 7897:. 7866:. 7825:. 7817:. 7809:. 7797:. 7789:, 7785:, 7756:. 7748:. 7738:. 7726:. 7718:, 7714:, 7710:, 7686:. 7652:}} 7648:{{ 7624:. 7620:. 7595:. 7564:. 7556:. 7544:. 7519:, 7509:, 7483:. 7433:^ 7398:. 7386:. 7382:. 7360:. 7328:. 7306:. 7298:. 7288:. 7280:. 7276:. 7253:. 7243:. 7233:. 7223:79 7221:. 7217:. 7194:. 7182:. 7178:. 7108:. 7100:. 7088:86 7086:. 7082:. 7056:. 7048:. 7038:21 7036:. 7032:. 7009:. 7001:. 6991:30 6989:. 6985:. 6962:. 6954:. 6944:29 6942:. 6938:. 6902:. 6841:. 6831:15 6829:. 6825:. 6802:. 6794:. 6782:. 6778:. 6744:. 6713:. 6680:. 6670:16 6668:. 6622:30 6620:. 6567:. 6559:. 6551:. 6539:61 6537:. 6523:^ 6509:. 6501:. 6491:36 6489:. 6466:. 6458:. 6438:^ 6376:. 6368:. 6356:43 6354:. 6327:. 6310:EC 6308:. 6278:22 6276:. 6272:. 6239:. 6227:. 6197:. 6193:. 6136:^ 6088:^ 6074:. 6066:. 6056:26 6054:. 5999:. 5991:. 5983:. 5971:65 5969:. 5933:. 5906:. 5852:. 5829:. 5817:. 5813:. 5801:^ 5732:^ 5697:. 5693:. 5618:^ 5590:^ 5547:. 5473:. 5467:. 5463:is 5347:31 5135:. 4673:. 4671:Go 4427:, 4419:, 4262:: 4165:, 4154:, 4132:, 4108:, 4071:b2 4059:w2 4047:b1 4035:w1 4002:np 3990:np 3957:== 3945:if 3927:-= 3924:b2 3906:-= 3903:b1 3885:-= 3882:w2 3864:-= 3861:w1 3828:np 3792:np 3780:z2 3759:w2 3741:np 3693:np 3669:a2 3657:np 3642:a3 3633:z3 3627:a3 3624:b2 3615:w2 3609:a2 3597:np 3591:z3 3585:z2 3573:a2 3570:b1 3561:w1 3543:np 3537:z2 3534:): 3516:in 3504:)) 3492:(( 3483:np 3477:b2 3447:np 3441:w2 3438:)) 3426:(( 3417:np 3411:b1 3381:np 3375:w1 3348:): 3294:. 3151:, 3143:, 3106:, 3102:, 3098:, 3094:, 3078:. 2630:. 2622:, 2322:, 2273:, 2265:, 2240:. 2155:. 2127:, 2078:, 2070:, 2033:A 2019:. 1920:. 1916:, 1822:, 1694:Ng 1689:. 1665:, 1614:, 1610:, 1602:, 1598:, 1533:. 1509:. 1497:. 1447:. 1297:. 1243:. 1089:, 1028:. 1014:NN 912:ML 17276:e 17269:t 17262:v 16618:e 16611:t 16604:v 15694:e 15687:t 15680:v 15218:e 15211:t 15204:v 14734:e 14727:t 14720:v 14705:. 14686:. 14659:. 14632:. 14618:: 14597:. 14573:. 14551:: 14538:. 14511:. 14484:. 14457:. 14435:: 14423:. 14396:. 14369:. 14329:. 14302:. 14298:: 14271:. 14244:. 14196:. 14169:. 14142:. 14138:: 14104:. 14082:: 14063:. 14051:: 14032:. 14018:: 13991:. 13959:. 13945:: 13915:. 13901:: 13865:. 13851:: 13845:2 13813:. 13776:: 13752:. 13726:: 13696:. 13682:: 13676:4 13652:. 13628:: 13622:2 13589:. 13567:: 13559:: 13532:. 13500:. 13469:. 13444:. 13432:: 13361:. 13343:. 13298:. 13262:. 13240:: 13230:: 13203:. 13171:: 13161:: 13134:. 13128:: 13113:. 13101:: 13093:: 13083:: 13060:. 13039:: 13009:. 12987:: 12977:: 12958:. 12923:. 12911:: 12903:: 12893:: 12870:. 12840:. 12818:: 12792:. 12773:: 12740:. 12685:. 12681:: 12648:. 12617:. 12598:: 12592:4 12568:. 12538:: 12530:: 12503:. 12487:: 12460:. 12436:: 12409:. 12389:: 12381:: 12371:: 12348:. 12328:: 12320:: 12310:: 12287:. 12275:: 12267:: 12257:: 12234:. 12214:: 12206:: 12196:: 12171:. 12141:. 12111:. 12080:. 12058:: 12026:: 12007:. 11977:. 11955:: 11936:. 11932:: 11924:: 11901:. 11887:5 11866:. 11862:: 11854:: 11831:. 11827:: 11821:5 11804:. 11800:: 11794:5 11777:. 11742:. 11728:: 11718:: 11710:: 11683:. 11652:. 11640:: 11632:: 11622:: 11599:. 11577:: 11571:5 11550:. 11526:: 11520:8 11499:. 11464:: 11434:. 11428:: 11420:: 11414:1 11393:. 11352:. 11313:. 11278:: 11270:: 11240:. 11228:: 11220:: 11196:. 11184:: 11161:. 11130:. 11096:: 11073:. 11018:. 11006:: 10983:. 10967:: 10940:. 10906:: 10883:. 10849:: 10826:. 10792:: 10769:. 10735:: 10712:. 10698:: 10677:: 10672:. 10666:: 10633:: 10610:. 10604:: 10589:. 10562:. 10556:: 10538:. 10501:. 10464:. 10458:: 10450:: 10420:. 10393:. 10387:: 10347:. 10319:: 10313:9 10267:. 10255:: 10249:1 10232:. 10205:: 10175:. 10148:: 10115:. 10089:: 10066:. 10050:: 10027:. 9992:. 9964:: 9956:: 9929:. 9909:: 9899:: 9856:. 9826:. 9820:: 9804:. 9798:: 9783:. 9771:: 9729:. 9710:. 9696:: 9667:. 9653:: 9634:. 9608:: 9585:. 9581:: 9548:. 9509:. 9505:: 9482:. 9468:: 9443:. 9431:: 9423:: 9413:: 9390:. 9351:. 9312:. 9264:. 9233:. 9227:: 9212:. 9200:: 9177:. 9165:: 9132:. 9110:: 9091:. 9085:: 9070:. 9037:. 9020:3 8996:. 8967:. 8950:2 8926:. 8896:. 8890:: 8882:: 8855:. 8843:: 8820:. 8796:. 8756:. 8744:: 8687:. 8675:: 8669:4 8642:. 8636:: 8621:. 8595:. 8581:: 8571:: 8546:. 8540:: 8525:. 8519:: 8500:. 8494:: 8474:: 8429:. 8403:. 8397:: 8382:. 8356:. 8336:: 8326:: 8282:. 8244:. 8216:. 8210:: 8195:. 8173:. 8167:: 8152:. 8115:. 8093:: 8083:: 8064:. 8042:: 8023:. 7988:. 7969:: 7943:. 7915:: 7905:: 7882:. 7833:. 7813:: 7805:: 7764:. 7742:: 7734:: 7728:7 7696:. 7658:) 7644:. 7632:: 7626:9 7605:. 7591:: 7572:. 7552:: 7546:9 7495:. 7426:. 7406:. 7394:: 7388:4 7364:. 7339:. 7314:. 7292:: 7284:: 7261:. 7237:: 7229:: 7202:. 7190:: 7123:. 7104:: 7064:. 7052:: 7044:: 7017:. 7005:: 6997:: 6970:. 6958:: 6950:: 6906:. 6849:. 6837:: 6810:. 6798:: 6790:: 6763:. 6723:. 6709:: 6688:. 6676:: 6632:. 6628:: 6602:. 6575:. 6555:: 6545:: 6517:. 6497:: 6474:. 6454:: 6432:. 6405:. 6399:: 6384:. 6372:: 6362:: 6339:. 6335:: 6329:5 6290:. 6284:: 6254:. 6235:: 6209:. 6205:: 6199:6 6178:. 6130:. 6100:. 6082:. 6062:: 6024:. 6007:. 5987:: 5945:. 5941:: 5935:2 5918:. 5914:: 5908:4 5891:. 5864:. 5837:. 5825:: 5819:5 5795:. 5763:. 5757:: 5711:. 5705:: 5699:9 5669:. 5644:. 5612:. 5584:. 5559:. 5520:. 5481:. 5395:) 5387:( 5382:) 5351:) 5344:( 4966:. 4963:) 4958:q 4954:b 4947:) 4942:q 4939:i 4935:w 4926:i 4922:x 4918:( 4913:i 4905:( 4899:K 4896:= 4891:q 4887:y 4863:q 4859:y 4835:2 4831:x 4606:j 4602:x 4597:e 4591:c 4586:1 4583:= 4580:j 4567:i 4563:x 4558:e 4552:= 4547:i 4543:y 4463:) 4457:( 4452:) 4448:( 4434:. 4211:) 4200:) 4190:) 4180:) 4140:) 4074:} 4068:: 4062:, 4056:: 4050:, 4044:: 4038:, 4032:: 4026:{ 4023:= 4011:( 4005:. 3999:( 3993:. 3987:, 3981:, 3978:i 3975:, 3969:( 3963:: 3960:0 3951:% 3948:i 3942:m 3939:/ 3933:* 3921:m 3918:/ 3912:* 3900:m 3897:/ 3891:* 3879:m 3876:/ 3870:* 3855:) 3852:0 3849:= 3843:, 3837:( 3831:. 3825:= 3819:) 3813:, 3810:T 3807:. 3804:X 3801:( 3795:. 3789:= 3783:) 3777:( 3771:* 3768:) 3765:T 3762:. 3756:, 3750:( 3744:. 3738:= 3732:) 3726:= 3720:, 3717:0 3714:= 3708:, 3702:( 3696:. 3690:= 3684:) 3678:, 3675:T 3672:. 3666:( 3660:. 3654:= 3648:y 3645:- 3639:= 3630:= 3621:+ 3618:) 3612:, 3606:( 3600:. 3594:= 3588:) 3582:( 3576:= 3567:+ 3564:) 3558:, 3555:X 3552:( 3546:. 3540:= 3531:1 3528:+ 3522:( 3513:i 3501:1 3498:, 3495:1 3486:. 3480:= 3474:) 3471:1 3468:, 3462:( 3456:. 3450:. 3444:= 3432:, 3429:1 3420:. 3414:= 3408:) 3402:, 3396:( 3390:. 3384:. 3378:= 3366:. 3363:X 3360:= 3354:, 3351:m 3342:, 3336:, 3330:, 3327:y 3324:, 3321:X 3318:( 3187:) 3181:( 3176:) 3172:( 3158:. 2956:) 2951:t 2947:a 2943:, 2938:t 2934:s 2929:| 2923:1 2920:+ 2917:t 2913:s 2909:( 2906:P 2884:) 2879:t 2875:s 2870:| 2864:t 2860:x 2856:( 2853:P 2831:) 2826:t 2822:s 2817:| 2811:t 2807:c 2803:( 2800:P 2778:A 2769:m 2765:a 2761:, 2758:. 2755:. 2752:. 2749:, 2744:1 2740:a 2716:S 2707:n 2703:s 2699:, 2696:. 2693:. 2690:. 2687:, 2682:1 2678:s 2592:) 2589:x 2586:( 2583:f 2561:x 2531:a 2509:] 2504:2 2500:) 2496:) 2493:x 2490:( 2487:f 2481:x 2478:( 2475:[ 2472:E 2469:= 2466:C 2444:a 2422:a 2419:= 2416:) 2413:x 2410:( 2407:f 2379:x 2309:) 2303:( 2298:) 2294:( 2280:. 2114:) 2108:( 2103:) 2099:( 2085:. 1888:) 1882:( 1877:) 1873:( 1869:. 1859:. 981:e 974:t 967:v 547:k 396:k 323:k 281:) 269:( 34:. 20:)

Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.

Index