Geoffrey Hinton | Academic Influence

Why Is Geoffrey Hinton Influential?

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By Erik J. Larson, PhD

Areas of Specialization: Artifical Intelligence, Deep Learning
Hinton has been called one of the “Godfathers of Artificial Intelligence” by media sources for his work on a neural network system known as “Deep Learning.” He divides his year between working for Google Brain, the influential AI group at Google, and as a professor of computer science at the University of Toronto in Canada.

Hinton, along with researchers David Rumelhart and Ronald Wilson, designed one of the key features in modern neural networks, a type of machine learning algorithm that learns from experience. In 1986, he published a description of using backpropagation to train neural networks on data, and this technique has become a lynchpin for all neural network successes to date. Hinton truly is one of the “godfathers” of AI, an honorific especially relevant today as major Web companies like Google, Facebook, Twitter and many others now use neural networks ubiquitously. At Google and the University of Toronto, Hinton focuses on Deep Learning systems, a type of neural networks that involves stacking multiple networks together to create powerful results, like learning to recognize faces and other objects in online photos. Self-driving cars also use Deep Learning systems for autonomous navigation.

Hinton was elected a Fellow of the Royal Society in 1998. With Yann LeCun and Yoshua Bengio, Hinton received the top prize in computer science, the Turing Award in 2018.

Academic Website

Featured in Top Influential Computer Scientists Today

According to Wikipedia, Geoffrey Everest Hinton is a British-Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks. Since 2013, he has divided his time working for Google and the University of Toronto. In 2017, he co-founded and became the Chief Scientific Advisor of the Vector Institute in Toronto.

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Geoffrey Hinton's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Papers

ImageNet classification with deep convolutional neural networks (2012) (91450)
Deep Learning (2015) (61305)
Dropout: a simple way to prevent neural networks from overfitting (2014) (31274)
Visualizing Data using t-SNE (2008) (25983)
Learning representations by back-propagating errors (1986) (21686)
Learning internal representations by error propagation (1986) (19896)
Reducing the Dimensionality of Data with Neural Networks (2006) (15940)
Rectified Linear Units Improve Restricted Boltzmann Machines (2010) (14326)
A Fast Learning Algorithm for Deep Belief Nets (2006) (14280)
Distilling the Knowledge in a Neural Network (2015) (10650)
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups (2012) (7904)
Speech recognition with deep recurrent neural networks (2013) (7409)
A Simple Framework for Contrastive Learning of Visual Representations (2020) (6935)
Improving neural networks by preventing co-adaptation of feature detectors (2012) (6642)
Training Products of Experts by Minimizing Contrastive Divergence (2002) (4819)
Adaptive Mixtures of Local Experts (1991) (4189)
Layer Normalization (2016) (3914)
On the importance of initialization and momentum in deep learning (2013) (3902)
A Learning Algorithm for Boltzmann Machines (1985) (3613)
Dynamic Routing Between Capsules (2017) (3264)
Phoneme recognition using time-delay neural networks (1989) (2884)
A Practical Guide to Training Restricted Boltzmann Machines (2012) (2881)
A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants (1998) (2662)
Deep Neural Networks for Acoustic Modeling in Speech Recognition (2012) (2329)
Deep Boltzmann Machines (2009) (2142)
Restricted Boltzmann machines for collaborative filtering (2007) (1917)
Neighbourhood Components Analysis (2004) (1750)
Acoustic Modeling Using Deep Belief Networks (2012) (1725)
Connectionist Learning Procedures (1989) (1629)
Stochastic Neighbor Embedding (2002) (1557)
Learning representations by back-propagation errors, nature (1986) (1384)
Learning and relearning in Boltzmann machines (1986) (1365)
Generating Text with Recurrent Neural Networks (2011) (1337)
Semantic hashing (2009) (1314)
Distributed Representations (1986) (1283)
Improving deep neural networks for LVCSR using rectified linear units and dropout (2013) (1264)
Unsupervised Learning (1999) (1245)
The Helmholtz Machine (1995) (1222)
How Learning Can Guide Evolution (1996) (1199)
Big Self-Supervised Models are Strong Semi-Supervised Learners (2020) (1098)
Autoencoders, Minimum Description Length and Helmholtz Free Energy (1993) (1072)
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer (2017) (1038)
The "wake-sleep" algorithm for unsupervised neural networks. (1995) (1035)
Keeping the neural networks simple by minimizing the description length of the weights (1993) (1016)
New types of deep neural network learning for speech recognition and related applications: an overview (2013) (972)
A Scalable Hierarchical Distributed Language Model (2008) (969)
Learning multiple layers of representation (2007) (950)
Learning distributed representations of concepts. (1989) (929)
Transforming Auto-Encoders (2011) (928)
When Does Label Smoothing Help? (2019) (894)
Zero-shot Learning with Semantic Output Codes (2009) (887)
Grammar as a Foreign Language (2014) (875)
Parallel Models of Associative Memory (1989) (868)
A general framework for parallel distributed processing (1986) (851)
Regularizing Neural Networks by Penalizing Confident Output Distributions (2017) (809)
Matrix capsules with EM routing (2018) (805)
Schemata and Sequential Thought Processes in PDP Models (1986) (804)
On Contrastive Divergence Learning (2005) (744)
The EM algorithm for mixtures of factor analyzers (1996) (724)
Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems (1991) (695)
Feudal Reinforcement Learning (1992) (681)
Simplifying Neural Networks by Soft Weight-Sharing (1992) (677)
A time-delay neural network architecture for isolated word recognition (1990) (655)
Three new graphical models for statistical language modelling (2007) (626)
A Simple Way to Initialize Recurrent Networks of Rectified Linear Units (2015) (619)
Modeling Human Motion Using Binary Latent Variables (2006) (614)
The appeal of parallel distributed processing (1986) (602)
Parameter estimation for linear dynamical systems (1996) (596)
OPTIMAL PERCEPTUAL INFERENCE (1983) (571)
Learning representations of back-propagation errors (1986) (541)
Replicated Softmax: an Undirected Topic Model (2009) (533)
Learning to Detect Roads in High-Resolution Aerial Images (2010) (519)
Similarity of Neural Network Representations Revisited (2019) (514)
Exponential Family Harmoniums with an Application to Information Retrieval (2004) (510)
On rectified linear units for speech processing (2013) (502)
Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure (2007) (501)
How neural networks learn from experience. (1992) (477)
Lookahead Optimizer: k steps forward, 1 step back (2019) (466)
Machine Learning for Aerial Image Labeling (2013) (452)
SMEM Algorithm for Mixture Models (1998) (448)
Distilling a Neural Network Into a Soft Decision Tree (2017) (443)
Lesioning an attractor network: investigations of acquired dyslexia (1991) (442)
Self-organizing neural network that discovers surfaces in random-dot stereograms (1992) (437)
Glove-Talk: a neural network interface between a data-glove and a speech synthesizer (1993) (436)
Deep Belief Networks for phone recognition (2009) (432)
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models (2016) (431)
Modeling the manifolds of images of handwritten digits (1997) (422)
An Efficient Learning Procedure for Deep Boltzmann Machines (2012) (418)
Deep belief networks (2009) (414)
The Recurrent Temporal Restricted Boltzmann Machine (2008) (409)
Factored conditional restricted Boltzmann Machines for modeling motion style (2009) (402)
Application of Deep Belief Networks for Natural Language Understanding (2014) (400)
Variational Learning for Switching State-Space Models (2000) (398)
Using very deep autoencoders for content-based image retrieval (2011) (397)
Experiments on Learning by Back Propagation. (1986) (395)
Learning to combine foveal glimpses with a third-order Boltzmann machine (2010) (391)
Deep Learning-A Technology With the Potential to Transform Health Care. (2018) (376)
Mapping Part-Whole Hierarchies into Connectionist Networks (1990) (372)
3D Object Recognition with Deep Belief Nets (2009) (369)
Learning to Label Aerial Images from Noisy Data (2012) (363)
Binary coding of speech spectrograms using a deep auto-encoder (2010) (359)
NeuroAnimator: fast neural network emulation and control of physics-based models (1998) (351)
Backpropagation and the brain (2020) (348)
Parallel visual computation (1983) (346)
A Distributed Connectionist Production System (1988) (329)
Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine (2010) (328)
Deep Belief Networks using discriminative features for phone recognition (2011) (316)
To recognize shapes, first learn to generate images. (2007) (311)
Lesioning an attractor network: investigations of acquired dyslexia. (1991) (307)
Using fast weights to improve persistent contrastive divergence (2009) (305)
Understanding how Deep Belief Networks perform acoustic modelling (2012) (303)
Reducing the Dimensionality of Data with Neural (2008) (293)
Large scale distributed neural network training through online distillation (2018) (282)
Training Recurrent Neural Networks (2013) (281)
Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines (2010) (280)
Learning Generative Texture Models with extended Fields-of-Experts (2009) (275)
An Alternative Model for Mixtures of Experts (1994) (273)
Generative models for discovering sparse distributed representations. (1997) (263)
Modeling pixel means and covariances using factorized third-order boltzmann machines (2010) (261)
Learning Multilevel Distributed Representations for High-Dimensional Sequences (2007) (246)
Global Coordination of Local Linear Models (2001) (244)
Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images (2010) (241)
Proceedings of the 1988 Connectionist Models Summer School (1989) (235)
Using fast weights to deblur old memories (1987) (230)
Visualizing non-metric similarities in multiple maps (2011) (225)
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes (2007) (225)
Massively Parallel Architectures for AI: NETL, Thistle, and Boltzmann Machines (1983) (221)
Some Demonstrations of the Effects of Structural Descriptions in Mental Imagery (1979) (220)
Unsupervised Learning of Image Transformations (2007) (219)
On deep generative models with applications to recognition (2011) (218)
A Parallel Computation that Assigns Canonical Object-Based Frames of Reference (1981) (217)
Learning Translation Invariant Recognition in Massively Parallel Networks (1987) (213)
Learning a better representation of speech soundwaves using restricted boltzmann machines (2011) (213)
Transforming Autoencoders (2011) (210)
Using Generative Models for Handwritten Digit Recognition (1996) (207)
Symbols Among the Neurons: Details of a Connectionist Inference Architecture (1985) (207)
Using Expectation-Maximization for Reinforcement Learning (1997) (199)
Distributed representations and nested compositional structure (1994) (195)
Stacked Capsule Autoencoders (2019) (190)
Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space (1989) (183)
Unsupervised learning : foundations of neural computation (1999) (181)
Using Fast Weights to Attend to the Recent Past (2016) (180)
Energy-Based Models for Sparse Overcomplete Representations (2003) (180)
Deep learning for AI (2021) (176)
Phoneme recognition: neural networks vs. hidden Markov models vs. hidden Markov models (1988) (175)
THE EFFECTS OF METHYLPHENIDATE (RITALIN) ON THE MOTOR SKILLS AND BEHAVIOR OF CHILDREN WITH LEARNING PROBLEMS (1969) (172)
Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures (2018) (172)
Products of experts (1999) (167)
Robust Boltzmann Machines for recognition and denoising (2012) (165)
CvxNet: Learnable Convex Decomposition (2019) (160)
Rate-coded Restricted Boltzmann Machines for Face Recognition (2000) (160)
Who Said What: Modeling Individual Labelers Improves Classification (2017) (159)
Dynamical binary latent variable models for 3D human pose tracking (2010) (158)
Learning Representations by Recirculation (1987) (156)
Learning Sparse Topographic Representations with Products of Student-t Distributions (2002) (156)
20 – CONNECTIONIST LEARNING PROCEDURES1 (1990) (154)
Parallel computations for controlling an arm. (1984) (154)
Reinforcement Learning with Factored States and Actions (2004) (154)
Deep, Narrow Sigmoid Belief Networks Are Universal Approximators (2008) (147)
Glove-TalkII-a neural-network interface which maps gestures to parallel formant speech synthesizer controls (1997) (147)
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition-' Washington , D . C . , June , 1983 OPTIMAL PERCEPTUAL INFERENCE (2011) (147)
Connectionist Architectures for Artificial Intelligence (1990) (145)
A New Learning Algorithm for Mean Field Boltzmann Machines (2002) (142)
Modeling image patches with a directed hierarchy of Markov random fields (2007) (142)
Neural Additive Models: Interpretable Machine Learning with Neural Nets (2020) (139)
Preface to the Special Issue on Connectionist Symbol Processing (1990) (135)
Deep belief nets for natural language call-routing (2011) (134)
Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation (2006) (134)
Varieties of Helmholtz Machine (1996) (134)
Learning sets of filters using back-propagation (1987) (131)
Recognizing Handwritten Digits Using Mixtures of Linear Models (1994) (130)
Acoustic and surface EMG diagnosis of pediatric muscle disease (1990) (127)
Hyperpipecolatemia: A new metabolic disorder associated with neuropathy and hepatomegaly: A case study. (1968) (126)
Topographic Product Models Applied to Natural Scene Statistics (2006) (126)
A Better Way to Pretrain Deep Boltzmann Machines (2012) (125)
Adaptive Elastic Models for Hand-Printed Character Recognition (1991) (124)
Separating Figure from Ground with a Parallel Network (1986) (122)
Two Distributed-State Models For Generating High-Dimensional Time Series (2011) (121)
Conditional Restricted Boltzmann Machines for Structured Output Prediction (2011) (121)
Modeling Documents with Deep Boltzmann Machines (2013) (119)
Learning Distributed Representations of Concepts Using Linear Relational Embedding (2001) (118)
Phone recognition using Restricted Boltzmann Machines (2010) (117)
Modeling documents with a Deep Boltzmann Machine (2013) (113)
Evaluation of Adaptive Mixtures of Competing Experts (1990) (112)
Scene-based and viewer-centered representations for comparing shapes (1988) (111)
Discovering Binary Codes for Documents by Learning Deep Generative Models (2011) (111)
Learning symmetry groups with hidden units: beyond the perceptron (1986) (109)
Shape Representation in Parallel Systems (1981) (108)
The shared views of four research groups ) (2012) (106)
Generating Facial Expressions with Deep Belief Nets (2008) (103)
Visualizing Similarity Data with a Mixture of Maps (2007) (100)
Gated Softmax Classification (2010) (99)
Learning to represent visual input (2010) (95)
Switching State-Space Models (1996) (94)
A comparison of statistical learning methods on the Gusto database. (1998) (90)
Implicit Mixtures of Restricted Boltzmann Machines (2008) (90)
Simulating brain damage. (1993) (89)
How to represent part-whole hierarchies in a neural network (2021) (88)
Learning and Applying Contextual Constraints in Sentence Comprehension (1991) (86)
A Mobile Robot That Learns Its Place (1997) (85)
Relaxation and its role in vision (1977) (83)
Generative versus discriminative training of RBMs for classification of fMRI images (2008) (83)
Analyzing and Improving Representations with the Soft Nearest Neighbor Loss (2019) (83)
Variational Learning in Nonlinear Gaussian Belief Networks (1999) (82)
Recognizing Hand-written Digits Using Hierarchical Products of Experts (2002) (82)
Deep Lambertian Networks (2012) (81)
Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates (1998) (80)
The delve manual (1996) (78)
Mental simulation (1990) (77)
Imputer: Sequence Modelling via Imputation and Dynamic Programming (2020) (76)
Generating more realistic images using gated MRF's (2010) (76)
Where Do Features Come From? (2014) (75)
A Desktop Input Device and Interface for Interactive 3D Character Animation (2002) (75)
Shape Recognition and Illusory Conjunctions (1985) (74)
Deep Learning in Natural Language Processing (2018) (74)
Introduction to the Special Section on Deep Learning for Speech and Language Processing (2012) (73)
A soft decision-directed LMS algorithm for blind equalization (1993) (71)
Learning Sparse Networks Using Targeted Dropout (2019) (68)
Rectiﬁed Linear Units Improve Restricted Boltzmann Machines (67)
Glove-TalkII: an adaptive gesture-to-formant interface (1995) (67)
Deep Mixtures of Factor Analysers (2012) (66)
Does the Wake-sleep Algorithm Produce Good Density Estimators? (1995) (62)
GTM through time (1997) (62)
Modeling the joint density of two images under a variety of transformations (2011) (61)
Glove-talk II - a neural-network interface which maps gestures to parallel formant speech synthesizer controls (1997) (60)
Learning to Parse Images (1999) (60)
Separating figure from ground with a Boltzmann machine (1990) (59)
Building adaptive interfaces with neural networks: The glove-talk pilot study (1990) (58)
G-maximization: An unsupervised learning procedure for discovering regularities (1987) (57)
What kind of graphical model is the brain? (2005) (57)
Modeling Natural Images Using Gated MRFs (2013) (57)
Glove-TalkII: Mapping Hand Gestures to Speech Using Neural Networks (1994) (57)
Tensor Analyzers (2013) (56)
Cerebellar astrocytoma with benign histology and malignant clinical course. Case report. (1981) (56)
Learning Mixture Models of Spatial Coherence (1993) (55)
A variant form of metachromatic leukodystrophy without arylsulfatase deficiency (1982) (55)
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions (2019) (53)
Keeping Neural Networks Simple (1993) (52)
Mean field networks that learn to discriminate temporally distorted strings (1991) (50)
Self Supervised Boosting (2002) (49)
Inferring Motor Programs from Images of Handwritten Digits (2005) (49)
A Hierarchical Community of Experts (1999) (48)
Learning Population Codes by Minimizing Description Length (1993) (47)
Implementing Semantic Networks in Parallel Hardware (2014) (47)
Mundane Reasoning by Parallel Constraint Satisfaction (1990) (47)
The Bootstrap Widrow-Hoff Rule as a Cluster-Formation Algorithm (1990) (46)
Multiple Relational Embedding (2004) (46)
Comparing Classification Methods for Longitudinal fMRI Studies (2010) (45)
Temporal-Kernel Recurrent Neural Networks (2010) (45)
Analysis-by-Synthesis by Learning to Invert Generative Black Boxes (2008) (43)
Deep Belief Nets (2017) (43)
Untimed and Misrepresented: Connectionism and the Computer Metaphor Untimed and Misrepresented: Connectionism and the Computer Metaphor (1992) (41)
Spiking Boltzmann Machines (1999) (41)
Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search (2018) (41)
Dimensionality Reduction and Prior Knowledge in E-Set Recognition (1989) (40)
DARCCC: Detecting Adversaries by Reconstruction from Class Conditional Capsules (2018) (40)
Discovering Viewpoint-Invariant Relationships That Characterize Objects (1990) (39)
Bayesian networks for pattern classification, data compression, and channel coding (1997) (39)
Products of Hidden Markov Models (2001) (38)
Connectionist Symbol Processing (1991) (38)
Local Physical Models for Interactive Character Animation (2002) (38)
Reinforcement learning for factored Markov decision processes (2002) (36)
Learning Causally Linked Markov Random Fields (2005) (35)
Adaptive Soft Weight Tying using Gaussian Mixtures (1991) (35)
Hierarchical Non-linear Factor Analysis and Topographic Maps (1997) (34)
Developing Population Codes by Minimizing Description Length (1993) (33)
Successful Treatment of Hereditary Trembling Chin With Botulinum Toxin (1993) (31)
Discovering Multiple Constraints that are Frequently Approximately Satisfied (2001) (31)
Using Free Energies to Represent Q-values in a Multiagent Reinforcement Learning Task (2000) (30)
Using EM for Reinforcement Learning (2000) (29)
A Mode-Hopping MCMC sampler (2003) (28)
Unsupervised part representation by Flow Capsules (2020) (25)
GEMINI: Gradient Estimation Through Matrix Inversion After Noise Injection (1988) (25)
Guest Editorial: Deep Learning (2015) (25)
Using matrices to model symbolic relationship (2008) (24)
TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations (1989) (23)
Learning Hierarchical Structures with Linear Relational Embedding (2001) (22)
BoltzCONS: Dynamic Symbol Structures in a Connectionist Network (1991) (22)
The ups and downs of Hebb synapses. (2003) (22)
Efficient Stochastic Source Coding and an Application to a Bayesian Network Source Model (1997) (22)
A New View of ICA (2001) (22)
Boltzmann machine (2007) (21)
Coaching variables for regression and classification (1998) (21)
Using Relaxation to find a Puppet (1976) (21)
A simple algorithm that discovers efficient perceptual codes (1997) (21)
Combining deformable models and neural networks for handprinted digit recognition (1994) (20)
Improving a statistical language model through non-linear prediction (2009) (20)
Children with Learning Problems: Academic History, Academic Prediction, and Adjustment Three Years After Assessment (1971) (19)
Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models (2014) (19)
Canonical Capsules: Unsupervised Capsules in Canonical Pose (2020) (19)
Subclass Distillation (2020) (19)
Spatial coherence as an internal teacher for a neural network (1995) (19)
Using an autoencoder with deformable templates to discover features for automated speech recognition (2013) (19)
Deterministic Boltzmann Learning in Networks with Asymmetric Connectivity (1991) (19)
Extracting distributed representations of concepts and relations from positive and negative propositions (2000) (18)
Wormholes Improve Contrastive Divergence (2003) (17)
Instantiating Deformable Models with a Neural Net (1997) (16)
A better way to learn features (2011) (16)
Free energy coding (1996) (16)
A Practical Guide to Training (2010) (16)
Using Pairs of Data-Points to Define Splits for Decision Trees (1995) (16)
Teaching with Commentaries (2020) (16)
Why the Islands Move (1984) (15)
Improving dimensionality reduction with spectral gradient descent (2005) (15)
Checklist for Diagnosis of Brain Death (1991) (14)
Deflecting Adversarial Attacks (2019) (14)
Tay-Sachs disease: B1 variant. (1988) (14)
Modeling pigeon behavior using a Conditional Restricted Boltzmann Machine (2009) (14)
Probabilistic sequential independent components analysis (2004) (13)
Imagery without arrays (1979) (13)
A new way to learn acoustic events (2011) (13)
“Dark Knowledge” (2020) (13)
Hand-printed digit recognition using deformable models (1994) (13)
Chapter IVb Some Computational Solutions to Bernstein's Problems (1984) (12)
Childhood Psychosis or Mental Retardation: A Diagnostic Dilemma. (1964) (11)
Minimizing Description Length in an Unsupervised Neural Network (2000) (11)
Bethe free energy and contrastive divergence approximations for undirected graphical models (2003) (11)
Computation by neural networks (2000) (11)
Dual Control (2010) (10)
Learning in parallel networks: simulating learning in a probabilistic system (1985) (10)
Speech recognition using time‐delay neural networks (1988) (10)
Minimal Brain Dysfunction: Clinical and Psychological Test Characteristics (1969) (9)
Discovering High Order Features with Mean Field Modules (1989) (9)
Fast Neural Network Emulation of Dynamical Systems for Computer Animation (1998) (9)
Using a neural net to instantiate a deformable model (1994) (9)
Aspartylglucosaminuria in a Canadian family. (1998) (8)
Products of Hidden Markov Models: It Takes N>1 to Tango (2009) (8)
An Efficient Learning Procedure for Deep (2010) (8)
Representation and Control in Vision (1978) (8)
Redes neuronales que aprenden de la experiencia (1992) (7)
Combining two methods of recognizing hand-printed digits (1992) (7)
Machine learning for neuroscience (2011) (7)
Modeling High-Dimensional Data by Combining Simple Experts (2000) (7)
Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space (2000) (7)
Intradural spinal hematoma in an infant with cystic fibrosis. (1986) (7)
Inferring the meaning of direct perception (1980) (7)
LEARNING SEMANTIC FEATURES (1984) (7)
Learning in massively parallel nets (1986) (7)
Une nouvelle approche de la cognition : le connexionnisme (1987) (6)
Efficient Parametric Projection Pursuit Density Estimation (2002) (6)
Heme‐Derived Bilins (2019) (6)
Training Products of Experts by Maximizing Contrastive Likelihood (1999) (6)
The AB-variant of metachromatic leukodystrophy (Postulated activator protein deficiency) (1981) (6)
The effect of hypothalamic lesions on growth. (1962) (5)
Ultrastructure and peroxidase of leucocytes in five patients with juvenile form of ceroid lipofuscinoses. (1976) (5)
Postnatal organic causes of mental retardation. (1962) (5)
Cerberus: A Multi-headed Derenderer (2019) (5)
The Horizontal—Vertical Delusion (1987) (5)
Learnable Convex Decomposition (2020) (5)
Bone mineral density and computer tomographic measurements in correlation with failure strength of equine metacarpal bones (2014) (5)
Using Mixtures of Factor Analyzers for Segmentation and Pose Estimation (1997) (5)
Unsupervised Learning: Foundations of Neural Computation--A Review (2001) (5)
Learning Distributed Representations of Relational Data using Linear Relational Embedding (2001) (5)
How to generate realistic images using gated MRF ’ s (2010) (5)
Neural network architectures for artificial intelligence (1988) (5)
Automated motif discovery in protein structure prediction (1997) (5)
TRAINING MANY SMALL HIDDEN MARKOV MODELS (2001) (4)
Learning to Make Coherent Predictions in Domains with Discontinuities (1991) (4)
The Next Generation of Neural Networks (2020) (4)
Connectionist Models: Proceedings of the Summer School Held in San Diego, California on 1990 (1990) (3)
Relative Density Nets: A New Way to Combine Backpropagation with HMM's (2001) (3)
Number 20 (1998) (3)
Decision Stump (2010) (3)
CHILDHOOD PSYCHOSIS OR MENTAL RETARDATION: A DIAGNOSTIC DILEMMA. II. PEDIATRIC AND NEUROLOGICAL ASPECTS. (1963) (3)
Improving a statistical language model by modulating the effects of context words (2008) (3)
Turn that frown upside-down! Inferring facial actions from pairs of images in a neurally plausible computational model (2010) (3)
The Behaviorial Significance of Differing EEG Abnormalities in Children With Learning and/or Behavior Problems (1970) (3)
ATRIX CAPSULES WITH EM ROUTING (2018) (3)
Artificial Intelligence: Neural Networks (2005) (3)
Cascaded redundancy reduction. (1998) (2)
Task and Object Learning in Visual Recognition (1991) (2)
DIGITAL MARIONETTE: augmenting kinematics with physics for multi-track desktop performance animation (2002) (2)
Non-linear dimensionality reduction using neural networks (2006) (2)
Fast Inference and Learning for Modeling Documents with a Deep Boltzmann Machine (2013) (2)
Workshop summary: Workshop on learning feature hierarchies (2009) (2)
Connectionist Symbol Processing - Preface (1990) (2)
Scaling in a hierarchical unsupervised network (1999) (2)
Bias Variance Decomposition (2010) (2)
Learning Pigeon Behaviour Using Binary Latent Variables (2009) (1)
Using matrices to model symbolic relationships (2008) (1)
COOPERATIVE : COMPUTATION (1)
Pattern classification using a mixture of factor analyzers (1999) (1)
Bellman Equation (2010) (1)
Developing a Mind: Learning in Humans, Animals, and Machines (2020) (1)
Mundane Reasoning by Settling on a Plausible Model (1991) (1)
Cascaded redundancy reduction (1998) (1)
The Development of the Time-Delayed Neural Network Architecture (1990) (1)
Models of human inference (1987) (1)
Developing Population Codes For Object Instantiation Parameters (2001) (1)
Recursive Distributed Representations (1991) (1)
Reviews: A Primer of Infant Development, Systems Neuroscience (1978) (1)
Unsupervised Object Discovery via Capsule Decoders (2019) (1)
LEARNING IN BOLTZMANN MACHINES ' APPRENTISSAGE DANS LES MACHINES DE BOLTZMANN (1985) (1)
Approximate Contrastive Free Energies for Learning in Undirected Graphical Models (2001) (1)
Using neural networks to learn intractable generative models (1994) (1)
Three frames suffice (1985) (1)
0 Speeding up Backpropagation Algorithms (1997) (1)
Paralysis after oral poliomyelitis vaccine. (1962) (1)
Learning fast neural network emulators for physics-based models (1997) (1)
Intrauterine growth retardation and its impact on the neurological status of the newborn (1981) (1)
Learning in Massively Parallel Nets (Panel) (1986) (1)
Atypical severe muscular dystrophy in a male: genetic implications for female relatives. (1985) (1)
Recluse Oil Field (1968) (0)
nerative Models for andwritten Digit Recognition (1996) (0)
Cerberus : A Multiheaded Derenderer (2019) (0)
Who Said What: Modelling Individual Labels Improves Classification (2017) (0)
Detecting Handwritten Text from Forms using Deep Learning (2020) (0)
Bias toward Higher Performance for the Decomposed Network. Whether a Decomposed Network Would Learn (1991) (0)
Embedding viaclustering: Using spectral information toguidedimensionality reduction (2005) (0)
In Memory of Ray Reiter (1939-2002) (2002) (0)
A Bayesian Unsupervised Learning Algorithm that Scales (2007) (0)
Performance analysis of Neural Network Classifier for the Different Number of Hidden Units (2014) (0)
Inverting Generative Black Boxes with Breeder Learning (2006) (0)
Système et méthode de résolution du problème de surapprentissage dans un réseau de neurones (2013) (0)
Networks Reducing the Dimensionality of Data with Neural (2008) (0)
learning can then be used to fine-tune the CD solution . 1 ON CONTRASTIVE DIVERGENCE LEARNING (1998) (0)
R M R S E N T a T I O N and Control in Vision ($5 ) (2012) (0)
Boltzmann Machines (2010) (0)
Sustainability Attitudes of College Students as Future Business Leaders (2018) (0)
23 Generating Facial Expressions with Deep Belief Nets (2008) (0)
is achieved. Prior to stabilization, neural networks do not jump around between points in activation space. Stabiliza-tion is the process whereby a network first generates a de-terminate activation pattern, and thereby arrives at a point in activation space (2004) (0)
Learning spatially coherent properties of the visual world in connectionist networks (1991) (0)
Thanks to our guest reviewers (1985) (0)
Artificial Intelligence Cytometer in Blood (2019) (0)
CSC 2535 2011 ASSIGNMENT 2 (2011) (0)
Detergent compositions containing percarbonate (1998) (0)
Preventive psychotherapeutic measures for use with non-vocal clients. A case study. (1982) (0)
Awards and Distinguished Papers (2011) (0)
APPRENTISSAGE DANS LES MACHINES DE BOLTZMANN (1986) (0)
Learning and Evaluaing Deep Bolztmann Machines (2009) (0)
Système et procédé permettant de paralléliser des réseaux neuronaux classiques (2013) (0)
Simulación de lesiones cerebrales (1993) (0)
Multifacility Location Problem using Scaled Conjugate Gradient Algorithm under Triangular Area Constraints (2017) (0)
Activity in Cognitive Elements 48 (1991) (0)
Report from Dagstuhl Seminar 14381 Neural-Symbolic Learning and Reasoning (2015) (0)
Fast Neural Network Emulation and Control of Dynamical Systems (1999) (0)
The way things ought to be (2003) (0)
November 21 , 2000 GCNU TR 2000 – 008 Products of Hidden Markov Models (2001) (0)
Scaling in a Hierarchical Unsupervised Network 1 (2000) (0)
Scalingin a Hierar chical UnsupervisedNetwork (1999) (0)
A Neurodegenerative Disorder in a 10 Year Old Boy (1987) (0)
NEUROMUSCULAR: ACOUSTIC AND SURFACE EMG DIAGNOSIS OF PEDIATRIC MUSCLE DISEASE (1990) (0)
The assessment and management of recurrent headaches in children (1987) (0)
JUVENILE FORM OF CEROID LIPOFUSCINOSES (1976) (0)
Modeling Semantic Similarities in Multiple Maps (2009) (0)
Artificial Intelligence, Linguistics, Neuroscience, Philosophy, Psychology (1996) (0)
Proceedings of the Connectionists Models Summer School Held in Pittsburgh, Pennsylvania on June 17-26, 1988 (1989) (0)
Proceedings of the 1990 Summer School on Connectionist models (1991) (0)
7. Conclusion and Future Work Novel Objective Function for Improved Phoneme Recognition Using Time-delay Neural Networks. 5. Simulation Results (0)
認知・学習のコネクショニストモデル (機械は考えるか ) (1987) (0)
Developing Population Codes For (1993) (0)
Who’s Who in the Zoo: Defining Roles and Responsibilities of a Collaborative Health Care Team Abstract (2018) (0)
P IX 2 SEQ : A L ANGUAGE M ODELING F RAMEWORK FOR O BJECT D ETECTION (2022) (0)
Shape Rfprfsfntatton in Parallel Systems (0)
Maximizing Mutual Information (0)
198 1 Iters in the Brain (0)
Simulating Brain Damage Adults with brain damage make some bizarre errors when reading words . If a network of simulated neurons is trained to read and then is damaged , it produces strikingly similar behavior (1999) (0)

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