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85-463: The conference is currently a double-track meeting (single-track until 2015) that includes invited talks as well as oral and poster presentations of refereed papers, followed by parallel-track workshops that up to 2013 were held at ski resorts. The NeurIPS meeting was first proposed in 1986 at the annual invitation-only Snowbird Meeting on Neural Networks for Computing organized by The California Institute of Technology and Bell Laboratories . NeurIPS

170-547: A slur against Japanese . The board changed the abbreviation to "NeurIPS" in November 2018. Along with machine learning and neuroscience, other fields represented at NeurIPS include cognitive science , psychology , computer vision , statistical linguistics , and information theory . Over the years, NeurIPS became a premier conference on machine learning and although the 'Neural' in the NeurIPS acronym had become something of

255-411: A 1994 book, did not yet describe the algorithm ). In 1986, David E. Rumelhart et al. popularised backpropagation but did not cite the original work. Kunihiko Fukushima 's convolutional neural network (CNN) architecture of 1979 also introduced max pooling , a popular downsampling procedure for CNNs. CNNs have become an essential tool for computer vision . The time delay neural network (TDNN)

340-471: A CNN named DanNet by Dan Ciresan, Ueli Meier, Jonathan Masci, Luca Maria Gambardella , and Jürgen Schmidhuber 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 max-pooling CNNs on GPU improved performance significantly. In October 2012, AlexNet by Alex Krizhevsky , Ilya Sutskever , and Geoffrey Hinton won

425-411: A CNN was applied to medical image object segmentation and breast cancer detection in mammograms. LeNet -5 (1998), a 7-level CNN by Yann LeCun et al., that classifies digits, was applied by several banks to recognize hand-written numbers on checks digitized in 32×32 pixel images. From 1988 onward, the use of neural networks transformed the field of protein structure prediction , in particular when

510-551: A Hebbian network. Other neural network computational machines were created by Rochester , Holland, Habit and Duda (1956). In 1958, psychologist Frank Rosenblatt described the perceptron, one of the first implemented artificial neural networks, funded by the United States Office of Naval Research . 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

595-409: A complex and seemingly unrelated set of information. Neural networks are typically trained through empirical risk minimization . 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 backpropagation are usually used to estimate

680-461: A constant and the cost C = E [ ( x − f ( x ) ) 2 ] {\displaystyle \textstyle C=E[(x-f(x))^{2}]} . Minimizing this cost produces a value of a {\displaystyle \textstyle a} 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 compression it could be related to

765-444: 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." The first deep learning multilayer perceptron trained by stochastic gradient descent

850-436: A historical relic, the resurgence of deep learning in neural networks since 2012, fueled by faster computers and big data, has led to achievements in speech recognition , object recognition in images, image captioning , language translation and world championship performance in the game of Go, based on neural architectures inspired by the hierarchy of areas in the visual cortex (ConvNet) and reinforcement learning inspired by

935-590: A neural network model of cognition-emotion relation. It was an example of a debate where an AI system, a recurrent neural network, contributed to an issue in the same time addressed by cognitive psychology. Two early influential works were the Jordan network (1986) and the Elman network (1990), which applied RNN to study cognitive psychology . In the 1980s, backpropagation did not work well for deep RNNs. To overcome this problem, in 1991, Jürgen Schmidhuber proposed

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1020-572: A particular learning task. Supervised learning 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 mean-squared error , which tries to minimize the average squared error between the network's output and the desired output. Tasks suited for supervised learning are pattern recognition (also known as classification) and regression (also known as function approximation). Supervised learning

1105-413: 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 mean squared errors 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

1190-418: 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 output neurons of the neural net accomplish the task, such as recognizing an object in an image. To find the output of the neuron we take the weighted sum of all the inputs, weighted by

1275-569: A working learning algorithm for hidden units, i.e., deep learning . Fundamental research was conducted on ANNs in the 1960s and 1970s. The first working deep learning algorithm was the Group method of data handling , a method to train arbitrarily deep neural networks, published by Alexey Ivakhnenko and Lapa in Ukraine (1965). They regarded it as a form of polynomial regression, or a generalization of Rosenblatt's perceptron. A 1971 paper described

1360-447: Is a model inspired by the structure and function of biological neural networks in animal brains . An ANN consists of connected units or nodes called artificial neurons , which loosely model the neurons in the brain. These are connected by edges , which model the synapses in the brain. Each artificial neuron receives signals from connected neurons, then processes them and sends a signal to other connected neurons. The "signal"

1445-412: Is a real number , and the output of each neuron is computed by some non-linear function of the sum of its inputs, called the activation function . The strength of the signal at each connection is determined by a weight , which adjusts during the learning process. Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from

1530-541: Is a Computer Science Assistant Professor at the University of California, Berkeley . She was previously a Junior Fellow at Harvard's Society of Fellows . She was the first Black woman to receive a Ph.D. in computer science at Cornell University . She "designs and analyzes algorithms, discrete optimizations, network-based, [and] computational strategies to increase access to opportunity for historically disadvantaged populations," according to her web bio. Timnit Gebru

1615-409: Is a constant parameter whose value is set before the learning process begins. The values of parameters are derived via learning. Examples of hyperparameters include learning rate , 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. Learning

1700-427: Is also applicable to sequential data (e.g., for handwriting, speech and gesture recognition ). 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. In unsupervised learning , input data is given along with the cost function, some function of the data x {\displaystyle \textstyle x} and

1785-444: 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 examining additional observations does not usefully reduce the error rate. Even after learning, the error rate typically does not reach 0. If after learning,

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1870-455: The Boltzmann machine , restricted Boltzmann machine , Helmholtz machine , and the wake-sleep algorithm . These were designed for unsupervised learning of deep generative models. Between 2009 and 2012, ANNs began winning prizes in image recognition contests, approaching human level performance on various tasks, initially in pattern recognition and handwriting recognition . In 2011,

1955-594: The COVID-19 pandemic ). In 2021, Black in AI, alongside the groups Queer in AI and Widening NLP , released a public statement refusing funding from Google in an act of protest of Google's treatment of Timnit Gebru, Margaret Mitchell , and April Christina Curley in the events that occurred in December 2020. Rediet Abebe is an Ethiopian computer scientist who specializes in algorithms and artificial intelligence. She

2040-574: The ReLU (rectified linear unit) activation function . The rectifier has become the most popular activation function for deep learning. Nevertheless, research stagnated in the United States following the work of Minsky and Papert (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). In 1976 transfer learning

2125-408: The method of least squares or linear regression . It was used as a means of finding a good rough linear fit to a set of points by Legendre (1805) and Gauss (1795) for the prediction of planetary movement. Historically, digital computers such as the von Neumann model operate via the execution of explicit instructions with access to memory by a number of processors. Some neural networks, on

2210-410: The mutual information between x {\displaystyle \textstyle x} and f ( x ) {\displaystyle \textstyle f(x)} , whereas in statistical modeling, it could be related to the posterior probability 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

2295-527: The proceedings from the following conferences have been published by Morgan Kaufmann (1988–1993), MIT Press (1994–2004) and Curran Associates (2005–present) under the name Advances in Neural Information Processing Systems . The conference was originally abbreviated as "NIPS". By 2018 a few commentators were criticizing the abbreviation as encouraging sexism due to its association with the word nipples , and as being

2380-554: The vanishing gradient problem and proposed recurrent residual connections to solve it. He and Schmidhuber introduced long short-term memory (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. During 1985–1995, inspired by statistical mechanics, several architectures and methods were developed by Terry Sejnowski , Peter Dayan , Geoffrey Hinton , etc., including

2465-557: The weights of the connections from the inputs to the neuron. We add a bias term to this sum. This weighted sum is sometimes called the activation . This weighted sum is then passed through a (usually nonlinear) activation function 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. The neurons are typically organized into multiple layers, especially in deep learning . Neurons of one layer connect only to neurons of

2550-478: The "neural sequence chunker" or "neural history compressor" which introduced the important concepts of self-supervised pre-training (the "P" in ChatGPT ) and neural knowledge distillation . In 1993, a neural history compressor system solved a "Very Deep Learning" task that required more than 1000 subsequent layers in an RNN unfolded in time. In 1991, Sepp Hochreiter 's diploma thesis identified and analyzed

2635-518: The 2010s, the seq2seq model was developed, and attention mechanisms were added. It led to the modern Transformer architecture in 2017 in Attention Is All You Need . It requires computation time that is quadratic in the size of the context window. Jürgen Schmidhuber 's fast weight controller (1992) scales linearly and was later shown to be equivalent to the unnormalized linear Transformer. Transformers have increasingly become

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2720-510: The 2018 meeting in Montreal. The AAAI announced the first Black in AI lunch, which was held in conjunction with AAAI-19. The lunch was hosted on Tuesday, January 29, 2019. This event was intended to promote networking, discussion of various AI career options, and the exchange of ideas in order to boost the number of Black researchers in the area. The fourth Black in AI workshop, which was held in conjunction with NeurIPS 2020, took place

2805-568: The AI community to have focus on these issues. Black in AI has strived to continue the progress of improving the presence of people of color in the field of artificial intelligence. In 2018 and 2019, the Black in AI workshop had many immigration visa issues to Canada , which spurred the conference to be planned for 2020 in Addis Ababa , Ethiopia. On December 7, 2020, Black in AI held its fourth annual workshop and first virtual workshop (due to

2890-579: The NeurIPS Board introduced the Breiman Lectureship to highlight work in statistics relevant to conference topics. The lectureship was named for statistician Leo Breiman , who served on the NeurIPS Board from 1994 to 2005. Past lecturers have included: In NIPS 2014, the program chairs duplicated 10% of all submissions and sent them through separate reviewers to evaluate randomness in the reviewing process. Several researchers interpreted

2975-606: The NeurIPS Foundation, established by Ed Posner . Terrence Sejnowski has been the president of the NeurIPS Foundation since Posner's death in 1993. The board of trustees consists of previous general chairs of the NeurIPS Conference. The first proceedings was published in book form by the American Institute of Physics in 1987, and was entitled Neural Information Processing Systems , then

3060-428: The ability to learn 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 directed , weighted graph . An artificial neural network consists of simulated neurons. Each neuron is connected to other nodes via links like a biological axon-synapse-dendrite connection. All

3145-605: The agent decides whether to explore new actions to uncover their costs or to exploit prior learning to proceed more quickly. Black in AI Black in AI , formally called the Black in AI Workshop , is a technology research organization and affinity group, founded by computer scientists Timnit Gebru and Rediet Abebe in 2017. It started as a conference workshop, later pivoting into an organization. Black in AI increases

3230-590: The application process. They also undertake research projects to investigate and highlight the difficulties that Black young researchers face, as well as push for structural reforms to eliminate these barriers and build equitable research settings. Moses Namara is a Facebook Research Fellow at Clemson University and a PhD candidate in Human-Centered Computing (HCC). He is the mentor for the new Black in AI Academic Program. During

3315-466: The art in generative modeling during 2014–2018 period. The GAN principle was originally published in 1991 by Jürgen Schmidhuber who called it "artificial curiosity": two neural networks contest with each other in the form of a zero-sum game , where one network's gain is the other network's loss. The first network is a generative model that models a probability distribution over output patterns. The second network learns by gradient descent to predict

3400-433: 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 emphasizes the last change. While it is possible to define a cost function ad hoc , frequently the choice is determined by the function's desirable properties (such as convexity ) or because it arises from

3485-600: The basal ganglia (Temporal difference learning). Notable affinity groups have emerged from the NeurIPS conference and displayed diversity, including Black in AI (in 2017), Queer in AI (in 2016), and others. In addition to invited talks and symposia, NeurIPS also organizes two named lectureships to recognize distinguished researchers. The NeurIPS Board introduced the Posner Lectureship in honor of NeurIPS founder Ed Posner ; two Posner Lectures were given each year up to 2015. Past lecturers have included: In 2015,

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3570-673: The conference was held in Montreal , Canada, in Barcelona, Spain in 2016, in Long Beach, United States in 2017, in Montreal, Canada in 2018 and Vancouver, Canada in 2019. Reflecting its origins at Snowbird, Utah , the meeting was accompanied by workshops organized at a nearby ski resort up until 2013, when it outgrew ski resorts. The first NeurIPS Conference was sponsored by the IEEE . The following NeurIPS Conferences have been organized by

3655-894: 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. The first perceptrons did not have adaptive hidden units. However, Joseph (1960) also discussed multilayer perceptrons 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

3740-597: The development of signal-processing algorithms for the original iPad. Black in AI received grants and support from private foundations like MacArthur Foundation and Rockefeller Foundation . The organization received $ 10,000 in 2018 for its annual workshop and $ 150,000 in 2019 for its long-term organizational planning. In 2020, during the pandemic, the organization received a grant of $ 300,000 by MacArthur Foundation in order to provide broad organizational support. In 2022, Rockefeller Foundation announced $ 300,000 to fight prejudice in artificial intelligence (AI) across

3825-405: The error rate is too high, the network typically must be redesigned. Practically this is done by defining a cost function that is evaluated periodically during learning. As long as its output continues to decline, learning continues. The cost is frequently defined as a statistic whose value can only be approximated. The outputs are actually numbers, so when the error is low, the difference between

3910-485: The first Black in AI event took place in December 8, 2017 in Long Beach, California . The goal was to bring together experts in the area to share ideas and debate efforts aimed at increasing the participation of Black people in artificial intelligence, both for diversity and to avoid data bias. Black AI researchers had the opportunity to share their work at the workshop's oral and poster sessions. The second workshop

3995-488: The first cascading networks were trained on profiles (matrices) produced by multiple sequence alignments . One origin of RNN was statistical mechanics . In 1972, Shun'ichi Amari proposed to modify the weights of an Ising model by Hebbian learning rule as a model of associative memory, adding in the component of learning. This was popularized as the Hopfield network by John Hopfield (1982). Another origin of RNN

4080-466: The first layer (the input layer ) to the last layer (the output layer ), possibly passing through multiple intermediate layers ( hidden layers ). A network is typically called a deep neural network if it has at least two hidden layers. Artificial neural networks are used for various tasks, including predictive modeling , adaptive control , and solving problems in artificial intelligence . They can learn from experience, and can derive conclusions from

4165-561: The globe and incorporate equity into this rapidly expanding field. "Black in AI works in academics, advocacy, entrepreneurship, financial support, and summer research programs." The Black in AI Academic Program is a resource for Black junior researchers applying to graduate schools, navigating graduate school, and transitioning into the postgraduate employment market. They provide online education sessions, offer scholarships to cover application fees, pair participants with peer and senior mentors, and distribute crowdsourced papers that simplify

4250-461: The graduate school admissions season in 2021, Black in AI served more than 200 potential graduate program candidates in some capacity. Furthermore, the organization's study identified greater problems encountered by Black graduate school candidates, such as the high cost of graduate school admissions examinations ( GREs ), which are known to be biased against those from low-income backgrounds. Black in AI's attempts to encourage institutions to eliminate

4335-464: The help of Black in AI. Twenty-four academics from Africa and South America were denied visas to attend this session during the conference, according to Victor Silva, the workshop organizer. He noted that, less than a month before the conference, 40 applicants from both continents had been given visas but that more than 70 applications were still waiting. For the second year in a row, visa restrictions have stopped several African scholars from attending

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4420-442: The immediately preceding and immediately following layers. The layer that receives external data is the input layer . The layer that produces the ultimate result is the output layer . In between them are zero or more hidden layers . 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

4505-538: The large-scale ImageNet competition by a significant margin over shallow machine learning methods. Further incremental improvements included the VGG-16 network by Karen Simonyan and Andrew Zisserman and Google's Inceptionv3 . In 2012, Ng and Dean 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 GPUs and distributed computing allowed

4590-427: The model (e.g. in a probabilistic model the model's posterior probability can be used as an inverse cost). 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 gradient (the derivative) of the cost function associated with a given state with respect to

4675-433: The model of choice for natural language processing . Many modern large language models such as ChatGPT , GPT-4 , and BERT use this architecture. 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

4760-442: The most positive (lowest cost) responses. In reinforcement learning , 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 instantaneous cost, according to some (usually unknown) rules. The rules and the long-term cost usually only can be estimated. At any juncture,

4845-401: The network's output. The cost function is dependent on the task (the model domain) and any a priori assumptions (the implicit properties of the model, its parameters and the observed variables). As a trivial example, consider the model f ( x ) = a {\displaystyle \textstyle f(x)=a} where a {\displaystyle \textstyle a} is

4930-441: The next layer. They can be pooling , 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 directed acyclic graph and are known as feedforward networks . Alternatively, networks that allow connections between neurons in the same or previous layers are known as recurrent networks . A hyperparameter

5015-401: 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 neurons. ANNs are composed of artificial neurons which are conceptually derived from biological neurons . Each artificial neuron has inputs and produces

5100-569: The obstacles were supported by the findings. Black in AI is also developing a program to help and connect Black tech startups with investors. Black in AI also mentors early-career Black AI academics and is forming relationships with Historically Black Colleges and Universities to extend its academic program. In 2021, Black in AI launched two summer research programs, one for undergraduate internships and another for unconstrained research mentorship, including one aimed explicitly at empowering Black women's AI research projects. At NeurIPS 2017,

5185-412: The other focused on the application of neural networks to artificial intelligence . In the late 1940s, D. O. Hebb proposed a learning hypothesis based on the mechanism of neural plasticity that became known as Hebbian learning . It was used in many early neural networks, such as Rosenblatt's perceptron and the Hopfield network . Farley and Clark (1954) used computational machines to simulate

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5270-465: The other hand, originated from efforts to model information processing in biological systems through the framework of connectionism . Unlike the von Neumann model, connectionist computing does not separate memory and processing. Warren McCulloch and Walter Pitts (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

5355-436: 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 can be viewed as a straightforward application of optimization theory and statistical estimation . 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

5440-401: The paradigm of unsupervised learning are in general estimation problems; the applications include clustering , the estimation of statistical distributions , compression and filtering . 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

5525-426: The parameters of the network. During the training phase, ANNs learn from labeled training data by iteratively updating their parameters to minimize a defined loss function . This method allows the network to generalize to unseen data. Today's deep neural networks are based on early work in statistics over 200 years ago. The simplest kind of feedforward neural network (FNN) is a linear network, which consists of

5610-427: The past. In 1982 a recurrent neural network, with an array architecture (rather than a multilayer perceptron architecture), named Crossbar Adaptive Array used direct recurrent connections from the output to the supervisor (teaching ) inputs. In addition of computing actions (decisions), it computed internal state evaluations (emotions) of the consequence situations. Eliminating the external supervisor, it introduced

5695-604: The presence and inclusion of Black people in the field of artificial intelligence (AI) by creating space for sharing ideas, fostering collaborations, mentorship, and advocacy. Black in AI was created in 2017 to address issues of lack of diversity in AI workshops, and was started as its own workshop within the Conference on Neural Information Processing Systems (NeurIPS) conference. Because of algorithmic bias , ethical issues , and underrepresentation of Black people in AI roles; there has been an ongoing need for unity within

5780-625: The reactions of the environment to these patterns. Excellent image quality is achieved by Nvidia 's StyleGAN (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 deepfakes . Diffusion models (2015) eclipsed GANs in generative modeling since then, with systems such as DALL·E 2 (2022) and Stable Diffusion (2022). In 2014,

5865-533: The result. Regarding whether the decision in NIPS is completely random or not, John Langford writes: "Clearly not—a purely random decision would have arbitrariness of ~78%. It is, however, quite notable that 60% is much closer to 78% than 0%." He concludes that the result of the reviewing process is mostly arbitrary. Artificial neural network In machine learning , a neural network (also artificial neural network or neural net , abbreviated ANN or NN )

5950-490: The self-learning method in neural networks. In cognitive psychology, the journal American Psychologist in early 1980's carried out a debate on relation between cognition and emotion. Zajonc in 1980 stated that emotion is computed first and is independent from cognition, while Lazarus in 1982 stated that cognition is computed first and is inseparable from emotion. In 1982 the Crossbar Adaptive Array gave

6035-482: 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 training accuracy, known as the "degradation" problem. In 2015, two techniques were developed to train very deep networks: the highway network was published in May 2015, and the residual neural network (ResNet) in December 2015. ResNet behaves like an open-gated Highway Net. During

6120-540: The training time, but with lower ultimate accuracy, while a lower learning rate takes longer, but with the potential for greater accuracy. Optimizations such as Quickprop are primarily aimed at speeding up error minimization, while other improvements mainly try to increase reliability. In order to avoid oscillation inside the network such as alternating connection weights, and to improve the rate of convergence, refinements use an adaptive learning rate that increases or decreases as appropriate. The concept of momentum allows

6205-629: The use of computer models as a tool for understanding biological nervous systems. Since then, the biological and artificial systems research streams have diverged, and recent NeurIPS proceedings have been dominated by papers on machine learning , artificial intelligence and statistics . From 1987 until 2000 NeurIPS was held in Denver , United States. Since then, the conference was held in Vancouver , Canada (2001–2010), Granada , Spain (2011), and Lake Tahoe , United States (2012–2013). In 2014 and 2015,

6290-435: The use of larger networks, particularly in image and visual recognition problems, which became known as "deep learning". Radial basis function and wavelet networks were introduced in 2013. These can be shown to offer best approximation properties and have been applied in nonlinear system identification and classification applications. Generative adversarial network (GAN) ( Ian Goodfellow et al., 2014) became state of

6375-525: The week of December 7, 2020. The workshop was scheduled to take place in Vancouver, British Columbia. Due to the pandemic, the session was held for the first time in a virtual format. Victor Silva, an AI4Society student, served as the event's chair. The fifth annual Black in AI workshop was also held virtually in 2021. Oral presentations, guest keynote speakers, a combined poster session with other affinity groups, sponsored sessions, and startup showcases

6460-432: The weights. The weight updates can be done via stochastic gradient descent or other methods, such as extreme learning machines , "no-prop" networks, training without backtracking, "weightless" networks, and non-connectionist neural networks . Machine learning is commonly separated into three main learning paradigms, supervised learning , unsupervised learning and reinforcement learning . Each corresponds to

6545-465: Was actually introduced in 1962 by Rosenblatt, but he did not know how to implement this, although Henry J. Kelley had a continuous precursor of backpropagation in 1960 in the context of control theory . In 1970, Seppo Linnainmaa published the modern form of backpropagation in his master thesis (1970). G.M. Ostrovski et al. republished it in 1971. Paul Werbos applied backpropagation to neural networks in 1982 (his 1974 PhD thesis, reprinted in

6630-732: Was born in Ethiopia and moved to the United States at the age of fifteen. She got her B.S. and M.S. in electrical engineering from Stanford University , as well as a PhD from the Stanford Artificial Intelligence Laboratory, where she studied computer vision under Fei-Fei Li . She formerly worked as a postdoctoral researcher at Microsoft Research in the Fairness Accountability Transparency, and Ethics (FATE) division. She's also worked with Apple, where she assisted in

6715-464: Was designed as a complementary open interdisciplinary meeting for researchers exploring biological and artificial Neural Networks . Reflecting this multidisciplinary approach, NeurIPS began in 1987 with information theorist Ed Posner as the conference president and learning theorist Yaser Abu-Mostafa as program chairman. Research presented in the early NeurIPS meetings included a wide range of topics from efforts to solve purely engineering problems to

6800-552: Was held in NeurIPS 2019, one of the premier machine learning conferences Vancouver , Canada. The workshop was able to give travel scholarships and visa support to hundreds of academics who would not have been able to attend NeurIPS without the help of sponsors. For instance, Ramon Vilarino of the University of Sao Paulo , who presented a poster at the conference on his study of geographical and racial prejudice in credit scoring in Brazil, would not have been able to attend NeurIPS without

6885-517: Was hosted in Montréal , Canada, on December 7, 2018. According to AI experts, visa issues stymie efforts to make their area more inclusive, making technology that discriminates or disadvantages individuals who aren't white or Western less likely. Hundreds of participants who were supposed to attend or present work at the Black in AI session on Friday were unable to fly to Canada; many of the participants were from African countries. The third workshop

6970-434: Was introduced in 1987 by Alex Waibel 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, Yann LeCun et al. created a CNN called LeNet for recognizing handwritten ZIP codes on mail. Training required 3 days. In 1990, Wei Zhang implemented a CNN on optical computing hardware. In 1991,

7055-564: Was introduced in neural networks learning. Deep learning architectures for convolutional neural networks (CNNs) with convolutional layers and downsampling layers and weight replication began with the Neocognitron introduced by Kunihiko Fukushima in 1979, though not trained by backpropagation. Backpropagation is an efficient application of the chain rule derived by Gottfried Wilhelm Leibniz in 1673 to networks of differentiable nodes. The terminology "back-propagating errors"

7140-441: Was neuroscience. The word "recurrent" is used to describe loop-like structures in anatomy. In 1901, Cajal observed "recurrent semicircles" in the cerebellar cortex . Hebb 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

7225-437: Was published in 1967 by Shun'ichi Amari . In computer experiments conducted by Amari's student Saito, a five layer MLP with two modifiable layers learned internal representations to classify non-linearily separable pattern classes. Subsequent developments in hardware and hyperparameter tunings have made end-to-end stochastic gradient descent the currently dominant training technique. In 1969, Kunihiko Fukushima introduced

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