ML Anthology

Hinton, Geoffrey E.

121 publications

NeurIPS 2022 A Unified Sequence Interface for Vision Tasks Ting Chen, Saurabh Saxena, Lala Li, Tsung-Yi Lin, David J Fleet, Geoffrey E. Hinton
NeurIPS 2021 Canonical Capsules: Self-Supervised Capsules in Canonical Pose Weiwei Sun, Andrea Tagliasacchi, Boyang Deng, Sara Sabour, Soroosh Yazdani, Geoffrey E. Hinton, Kwang Moo Yi
NeurIPS 2021 Neural Additive Models: Interpretable Machine Learning with Neural Nets Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Ben Lengerich, Rich Caruana, Geoffrey E. Hinton
NeurIPS 2020 Big Self-Supervised Models Are Strong Semi-Supervised Learners Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey E. Hinton
NeurIPS 2019 Lookahead Optimizer: K Steps Forward, 1 Step Back Michael Zhang, James Lucas, Jimmy Ba, Geoffrey E. Hinton
NeurIPS 2019 Stacked Capsule Autoencoders Adam Kosiorek, Sara Sabour, Yee Whye Teh, Geoffrey E. Hinton
NeurIPS 2019 When Does Label Smoothing Help? Rafael Müller, Simon Kornblith, Geoffrey E. Hinton
NeurIPS 2018 Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures Sergey Bartunov, Adam Santoro, Blake Richards, Luke Marris, Geoffrey E. Hinton, Timothy Lillicrap
ICLR 2018 Large Scale Distributed Neural Network Training Through Online Distillation Rohan Anil, Gabriel Pereyra, Alexandre Passos, Robert Ormandi, George E. Dahl, Geoffrey E. Hinton
ICLR 2018 Matrix Capsules with EM Routing Geoffrey E Hinton, Sara Sabour, Nicholas Frosst
AAAI 2018 Who Said What: Modeling Individual Labelers Improves Classification Melody Y. Guan, Varun Gulshan, Andrew M. Dai, Geoffrey E. Hinton
NeurIPS 2017 Dynamic Routing Between Capsules Sara Sabour, Nicholas Frosst, Geoffrey E. Hinton
ICLR 2017 Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc V. Le, Geoffrey E. Hinton, Jeff Dean
ICLR 2017 Regularizing Neural Networks by Penalizing Confident Output Distributions Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, Geoffrey E. Hinton
NeurIPS 2016 Attend, Infer, Repeat: Fast Scene Understanding with Generative Models S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Koray Kavukcuoglu, Geoffrey E. Hinton
NeurIPS 2016 Using Fast Weights to Attend to the Recent past Jimmy Ba, Geoffrey E. Hinton, Volodymyr Mnih, Joel Z. Leibo, Catalin Ionescu
UAI 2013 Modeling Documents with Deep Boltzmann Machines Nitish Srivastava, Ruslan Salakhutdinov, Geoffrey E. Hinton
NeurIPS 2012 A Better Way to Pretrain Deep Boltzmann Machines Geoffrey E. Hinton, Ruslan Salakhutdinov
ICML 2012 Deep Lambertian Networks Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton
ICML 2012 Deep Mixtures of Factor Analysers Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton
NeurIPS 2012 ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton
ICML 2012 Learning to Label Aerial Images from Noisy Data Volodymyr Mnih, Geoffrey E. Hinton
CVPR 2012 Robust Boltzmann Machines for Recognition and Denoising Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton
MLJ 2012 Visualizing Non-Metric Similarities in Multiple Maps Laurens van der Maaten, Geoffrey E. Hinton
UAI 2011 Conditional Restricted Boltzmann Machines for Structured Output Prediction Volodymyr Mnih, Hugo Larochelle, Geoffrey E. Hinton
ICML 2011 Generating Text with Recurrent Neural Networks Ilya Sutskever, James Martens, Geoffrey E. Hinton
CVPR 2011 Modeling the Joint Density of Two Images Under a Variety of Transformations Joshua M. Susskind, Geoffrey E. Hinton, Roland Memisevic, Marc Pollefeys
CVPR 2011 On Deep Generative Models with Applications to Recognition Marc'Aurelio Ranzato, Joshua M. Susskind, Volodymyr Mnih, Geoffrey E. Hinton
JMLR 2011 Two Distributed-State Models for Generating High-Dimensional Time Series Graham W. Taylor, Geoffrey E. Hinton, Sam T. Roweis
CVPR 2010 Dynamical Binary Latent Variable Models for 3D Human Pose Tracking Graham W. Taylor, Leonid Sigal, David J. Fleet, Geoffrey E. Hinton
NeurIPS 2010 Gated SoftMax Classification Roland Memisevic, Christopher Zach, Marc Pollefeys, Geoffrey E. Hinton
NeurIPS 2010 Generating More Realistic Images Using Gated MRF's Marc'aurelio Ranzato, Volodymyr Mnih, Geoffrey E. Hinton
NeurIPS 2010 Learning to Combine Foveal Glimpses with a Third-Order Boltzmann Machine Hugo Larochelle, Geoffrey E. Hinton
ECCV 2010 Learning to Detect Roads in High-Resolution Aerial Images Volodymyr Mnih, Geoffrey E. Hinton
CVPR 2010 Modeling Pixel Means and Covariances Using Factorized Third-Order Boltzmann Machines Marc'Aurelio Ranzato, Geoffrey E. Hinton
NeurIPS 2010 Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine George Dahl, Marc'aurelio Ranzato, Abdel-rahman Mohamed, Geoffrey E. Hinton
ICML 2010 Rectified Linear Units Improve Restricted Boltzmann Machines Vinod Nair, Geoffrey E. Hinton
NeurIPS 2009 3D Object Recognition with Deep Belief Nets Vinod Nair, Geoffrey E. Hinton
ICML 2009 Factored Conditional Restricted Boltzmann Machines for Modeling Motion Style Graham W. Taylor, Geoffrey E. Hinton
UAI 2009 Products of Hidden Markov Models: It Takes N>1 to Tango Graham W. Taylor, Geoffrey E. Hinton
NeurIPS 2009 Replicated SoftMax: An Undirected Topic Model Geoffrey E. Hinton, Ruslan Salakhutdinov
ICML 2009 Using Fast Weights to Improve Persistent Contrastive Divergence Tijmen Tieleman, Geoffrey E. Hinton
ICML 2009 Workshop Summary: Workshop on Learning Feature Hierarchies Kai Yu, Ruslan Salakhutdinov, Yann LeCun, Geoffrey E. Hinton, Yoshua Bengio
NeurIPS 2009 Zero-Shot Learning with Semantic Output Codes Mark Palatucci, Dean Pomerleau, Geoffrey E. Hinton, Tom M. Mitchell
NeurIPS 2008 A Scalable Hierarchical Distributed Language Model Andriy Mnih, Geoffrey E. Hinton
NeurIPS 2008 Generative Versus Discriminative Training of RBMs for Classification of fMRI Images Tanya Schmah, Geoffrey E. Hinton, Steven L. Small, Stephen Strother, Richard S. Zemel
NeurIPS 2008 Implicit Mixtures of Restricted Boltzmann Machines Vinod Nair, Geoffrey E. Hinton
NeurIPS 2008 The Recurrent Temporal Restricted Boltzmann Machine Ilya Sutskever, Geoffrey E. Hinton, Graham W. Taylor
NeurIPS 2008 Using Matrices to Model Symbolic Relationship Ilya Sutskever, Geoffrey E. Hinton
NeurIPS 2007 Modeling Image Patches with a Directed Hierarchy of Markov Random Fields Simon Osindero, Geoffrey E. Hinton
ICML 2007 Restricted Boltzmann Machines for Collaborative Filtering Ruslan Salakhutdinov, Andriy Mnih, Geoffrey E. Hinton
ICML 2007 Three New Graphical Models for Statistical Language Modelling Andriy Mnih, Geoffrey E. Hinton
CVPR 2007 Unsupervised Learning of Image Transformations Roland Memisevic, Geoffrey E. Hinton
NeurIPS 2007 Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes Geoffrey E. Hinton, Ruslan Salakhutdinov
NeurIPS 2006 Modeling Human Motion Using Binary Latent Variables Graham W. Taylor, Geoffrey E. Hinton, Sam T. Roweis
NeurIPS 2005 Inferring Motor Programs from Images of Handwritten Digits Vinod Nair, Geoffrey E. Hinton
IJCAI 2005 What Kind of Graphical Model Is the Brain? Geoffrey E. Hinton
NeurIPS 2004 Exponential Family Harmoniums with an Application to Information Retrieval Max Welling, Michal Rosen-zvi, Geoffrey E. Hinton
NeurIPS 2004 Multiple Relational Embedding Roland Memisevic, Geoffrey E. Hinton
NeurIPS 2004 Neighbourhood Components Analysis Jacob Goldberger, Geoffrey E. Hinton, Sam T. Roweis, Ruslan Salakhutdinov
JMLR 2004 Reinforcement Learning with Factored States and Actions Brian Sallans, Geoffrey E. Hinton
UAI 2003 Efficient Parametric Projection Pursuit Density Estimation Max Welling, Richard S. Zemel, Geoffrey E. Hinton
JMLR 2003 Energy-Based Models for Sparse Overcomplete Representations Yee Whye Teh, Max Welling, Simon Osindero, Geoffrey E. Hinton
NeurIPS 2003 Wormholes Improve Contrastive Divergence Max Welling, Andriy Mnih, Geoffrey E. Hinton
NeurIPS 2002 Learning Sparse Topographic Representations with Products of Student-T Distributions Max Welling, Simon Osindero, Geoffrey E. Hinton
NeurIPS 2002 Self Supervised Boosting Max Welling, Richard S. Zemel, Geoffrey E. Hinton
NeurIPS 2002 Stochastic Neighbor Embedding Geoffrey E. Hinton, Sam T. Roweis
NeCo 2002 Training Products of Experts by Minimizing Contrastive Divergence Geoffrey E. Hinton
UAI 2001 Discovering Multiple Constraints That Are Frequently Approximately Satisfied Geoffrey E. Hinton, Yee Whye Teh
NeurIPS 2001 Global Coordination of Local Linear Models Sam T. Roweis, Lawrence K. Saul, Geoffrey E. Hinton
NeurIPS 2001 Learning Hierarchical Structures with Linear Relational Embedding Alberto Paccanaro, Geoffrey E. Hinton
AISTATS 2001 Products of Hidden Markov Models Andrew D. Brown, Geoffrey E. Hinton
NeurIPS 2001 Relative Density Nets: A New Way to Combine Backpropagation with HMM's Andrew D. Brown, Geoffrey E. Hinton
ICML 2000 Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space Alberto Paccanaro, Geoffrey E. Hinton
AAAI 2000 Modeling High-Dimensional Data by Combining Simple Experts Geoffrey E. Hinton
NeurIPS 2000 Rate-Coded Restricted Boltzmann Machines for Face Recognition Yee Whye Teh, Geoffrey E. Hinton
NeurIPS 2000 Recognizing Hand-Written Digits Using Hierarchical Products of Experts Guy Mayraz, Geoffrey E. Hinton
NeCo 2000 SMEM Algorithm for Mixture Models Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton
NeurIPS 2000 Using Free Energies to Represent Q-Values in a Multiagent Reinforcement Learning Task Brian Sallans, Geoffrey E. Hinton
NeCo 2000 Variational Learning for Switching State-Space Models Zoubin Ghahramani, Geoffrey E. Hinton
NeurIPS 1999 Learning to Parse Images Geoffrey E. Hinton, Zoubin Ghahramani, Yee Whye Teh
NeurIPS 1999 Spiking Boltzmann Machines Geoffrey E. Hinton, Andrew D. Brown
NeCo 1999 Variational Learning in Nonlinear Gaussian Belief Networks Brendan J. Frey, Geoffrey E. Hinton
NeurIPS 1998 Fast Neural Network Emulation of Dynamical Systems for Computer Animation Radek Grzeszczuk, Demetri Terzopoulos, Geoffrey E. Hinton
NeurIPS 1998 SMEM Algorithm for Mixture Models Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton
NeCo 1997 A Mobile Robot That Learns Its Place Sageev Oore, Geoffrey E. Hinton, Gregory Dudek
NeurIPS 1997 Hierarchical Non-Linear Factor Analysis and Topographic Maps Zoubin Ghahramani, Geoffrey E. Hinton
NeCo 1997 Using Expectation-Maximization for Reinforcement Learning Peter Dayan, Geoffrey E. Hinton
NeurIPS 1995 Does the Wake-Sleep Algorithm Produce Good Density Estimators? Brendan J. Frey, Geoffrey E. Hinton, Peter Dayan
NeCo 1995 Learning Population Codes by Minimizing Description Length Richard S. Zemel, Geoffrey E. Hinton
NeCo 1995 The Helmholtz Machine Peter Dayan, Geoffrey E. Hinton, Radford M. Neal, Richard S. Zemel
NeurIPS 1995 Using Pairs of Data-Points to Define Splits for Decision Trees Geoffrey E. Hinton, Michael Revow
NeurIPS 1994 An Alternative Model for Mixtures of Experts Lei Xu, Michael I. Jordan, Geoffrey E. Hinton
NeurIPS 1994 Glove-TalkII: Mapping Hand Gestures to Speech Using Neural Networks Sidney Fels, Geoffrey E. Hinton
NeurIPS 1994 Recognizing Handwritten Digits Using Mixtures of Linear Models Geoffrey E. Hinton, Michael Revow, Peter Dayan
NeurIPS 1994 Using a Neural Net to Instantiate a Deformable Model Christopher K. I. Williams, Michael Revow, Geoffrey E. Hinton
NeurIPS 1993 Autoencoders, Minimum Description Length and Helmholtz Free Energy Geoffrey E. Hinton, Richard S. Zemel
NeurIPS 1993 Developing Population Codes by Minimizing Description Length Richard S. Zemel, Geoffrey E. Hinton
COLT 1993 Keeping the Neural Networks Simple by Minimizing the Description Length of the Weights Geoffrey E. Hinton, Drew van Camp
NeCo 1993 Learning Mixture Models of Spatial Coherence Suzanna Becker, Geoffrey E. Hinton
NeurIPS 1992 Feudal Reinforcement Learning Peter Dayan, Geoffrey E. Hinton
NeCo 1992 Simplifying Neural Networks by Soft Weight-Sharing Steven J. Nowlan, Geoffrey E. Hinton
NeurIPS 1991 Adaptive Elastic Models for Hand-Printed Character Recognition Geoffrey E. Hinton, Christopher K. I. Williams, Michael D. Revow
NeCo 1991 Adaptive Mixtures of Local Experts Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan, Geoffrey E. Hinton
NeurIPS 1991 Adaptive Soft Weight Tying Using Gaussian Mixtures Steven J. Nowlan, Geoffrey E. Hinton
NeurIPS 1991 Learning to Make Coherent Predictions in Domains with Discontinuities Suzanna Becker, Geoffrey E. Hinton
NeurIPS 1990 Discovering Viewpoint-Invariant Relationships That Characterize Objects Richard S. Zemel, Geoffrey E. Hinton
NeurIPS 1990 Evaluation of Adaptive Mixtures of Competing Experts Steven J. Nowlan, Geoffrey E. Hinton
NeCo 1990 The Bootstrap Widrow-Hoff Rule as a Cluster-Formation Algorithm Geoffrey E. Hinton, Steven J. Nowlan
NeCo 1989 Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space Geoffrey E. Hinton
NeurIPS 1989 Dimensionality Reduction and Prior Knowledge in E-Set Recognition Kevin J. Lang, Geoffrey E. Hinton
NeurIPS 1989 Discovering High Order Features with Mean Field Modules Conrad C. Galland, Geoffrey E. Hinton
NeurIPS 1989 TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations Richard S. Zemel, Michael Mozer, Geoffrey E. Hinton
NeurIPS 1988 GEMINI: Gradient Estimation Through Matrix Inversion After Noise Injection Yann Le Cun, Conrad C. Galland, Geoffrey E. Hinton
NeurIPS 1987 Learning Representations by Recirculation Geoffrey E. Hinton, James L. McClelland
AAAI 1986 Learning in Massively Parallel Nets (Panel) Drew V. McDermott, Geoffrey E. Hinton
IJCAI 1985 Shape Recognition and Illusory Conjunctions Geoffrey E. Hinton, Kevin J. Lang
IJCAI 1985 Symbols Among the Neurons: Details of a Connectionist Inference Architecture David S. Touretzky, Geoffrey E. Hinton
AAAI 1983 Massively Parallel Architectures for AI: NETL, Thistle, and Boltzmann Machines Scott E. Fahlman, Geoffrey E. Hinton, Terrence J. Sejnowski
IJCAI 1981 A Parallel Computation That Assigns Canonical Object-Based Frames of Reference Geoffrey E. Hinton
IJCAI 1981 Shape Representation in Parallel Systems Geoffrey E. Hinton