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ORCIDGeoffrey E. Hinton
Person information
- affiliation: Google DeepMind, London, UK
- affiliation: University of Toronto, Department of Computer Science, ON, Canada
- award (2018): Turing Award
- award (2016): BBVA Foundation Frontiers of Knowledge Award
- award (2024): Nobel Prize in Physics
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2020 – today
- 2023
[j70]Geoffrey E. Hinton:
How to Represent Part-Whole Hierarchies in a Neural Network. Neural Comput. 35(3): 413-452 (2023)- [c163]Ting Chen, Lala Li, Saurabh Saxena, Geoffrey E. Hinton, David J. Fleet:
A Generalist Framework for Panoptic Segmentation of Images and Videos. ICCV 2023: 909-919 - [c162]Ting Chen, Ruixiang Zhang, Geoffrey E. Hinton:
Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning. ICLR 2023 - [c161]Mengye Ren, Simon Kornblith, Renjie Liao, Geoffrey E. Hinton:
Scaling Forward Gradient With Local Losses. ICLR 2023 - [i53]Yoshua Bengio, Geoffrey E. Hinton, Andrew Yao, Dawn Song, Pieter Abbeel, Yuval Noah Harari, Ya-Qin Zhang, Lan Xue, Shai Shalev-Shwartz, Gillian K. Hadfield, Jeff Clune, Tegan Maharaj, Frank Hutter, Atilim Günes Baydin, Sheila A. McIlraith, Qiqi Gao, Ashwin Acharya, David Krueger, Anca D. Dragan, Philip H. S. Torr, Stuart Russell, Daniel Kahneman, Jan Brauner, Sören Mindermann:
Managing AI Risks in an Era of Rapid Progress. CoRR abs/2310.17688 (2023) - 2022
- [c160]Kevin Clark, Kelvin Guu, Ming-Wei Chang, Panupong Pasupat, Geoffrey E. Hinton, Mohammad Norouzi:
Meta-Learning Fast Weight Language Models. EMNLP 2022: 9751-9757 - [c159]Ting Chen, Saurabh Saxena, Lala Li, David J. Fleet, Geoffrey E. Hinton:
Pix2seq: A Language Modeling Framework for Object Detection. ICLR 2022 - [c158]Ting Chen, Saurabh Saxena, Lala Li, Tsung-Yi Lin, David J. Fleet, Geoffrey E. Hinton:
A Unified Sequence Interface for Vision Tasks. NeurIPS 2022 - [i52]Shekoofeh Azizi
, Laura Culp, Jan Freyberg, Basil Mustafa, Sebastien Baur, Simon Kornblith, Ting Chen, Patricia MacWilliams, S. Sara Mahdavi, Ellery Wulczyn, Boris Babenko, Megan Wilson, Aaron Loh, Po-Hsuan Cameron Chen, Yuan Liu, Pinal Bavishi, Scott Mayer McKinney, Jim Winkens, Abhijit Guha Roy, Zachary Beaver, Fiona Ryan, Justin Krogue, Mozziyar Etemadi, Umesh Telang, Yun Liu, Lily Peng, Gregory S. Corrado, Dale R. Webster, David J. Fleet, Geoffrey E. Hinton, Neil Houlsby, Alan Karthikesalingam, Mohammad Norouzi, Vivek Natarajan:
Robust and Efficient Medical Imaging with Self-Supervision. CoRR abs/2205.09723 (2022) - [i51]Ting Chen, Saurabh Saxena, Lala Li, Tsung-Yi Lin, David J. Fleet, Geoffrey E. Hinton:
A Unified Sequence Interface for Vision Tasks. CoRR abs/2206.07669 (2022) - [i50]Ting Chen, Ruixiang Zhang, Geoffrey E. Hinton:
Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning. CoRR abs/2208.04202 (2022) - [i49]Mengye Ren, Simon Kornblith, Renjie Liao, Geoffrey E. Hinton:
Scaling Forward Gradient With Local Losses. CoRR abs/2210.03310 (2022) - [i48]Ting Chen, Lala Li, Saurabh Saxena, Geoffrey E. Hinton, David J. Fleet:
A Generalist Framework for Panoptic Segmentation of Images and Videos. CoRR abs/2210.06366 (2022) - [i47]Renjie Liao, Simon Kornblith, Mengye Ren, David J. Fleet, Geoffrey E. Hinton:
Gaussian-Bernoulli RBMs Without Tears. CoRR abs/2210.10318 (2022) - [i46]Laura Culp, Sara Sabour, Geoffrey E. Hinton:
Testing GLOM's ability to infer wholes from ambiguous parts. CoRR abs/2211.16564 (2022) - [i45]Kevin Clark, Kelvin Guu, Ming-Wei Chang, Panupong Pasupat, Geoffrey E. Hinton, Mohammad Norouzi:
Meta-Learning Fast Weight Language Models. CoRR abs/2212.02475 (2022) - [i44]Geoffrey E. Hinton:
The Forward-Forward Algorithm: Some Preliminary Investigations. CoRR abs/2212.13345 (2022) - 2021
- [j69]Yoshua Bengio, Yann LeCun, Geoffrey E. Hinton:
Deep learning for AI. Commun. ACM 64(7): 58-65 (2021) - [c157]Aniruddh Raghu, Maithra Raghu, Simon Kornblith, David Duvenaud, Geoffrey E. Hinton:
Teaching with Commentaries. ICLR 2021 - [c156]Sara Sabour, Andrea Tagliasacchi, Soroosh Yazdani, Geoffrey E. Hinton, David J. Fleet:
Unsupervised Part Representation by Flow Capsules. ICML 2021: 9213-9223 - [c155]Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Benjamin J. Lengerich, Rich Caruana, Geoffrey E. Hinton:
Neural Additive Models: Interpretable Machine Learning with Neural Nets. NeurIPS 2021: 4699-4711 - [c154]Weiwei Sun, Andrea Tagliasacchi, Boyang Deng, Sara Sabour, Soroosh Yazdani, Geoffrey E. Hinton, Kwang Moo Yi:
Canonical Capsules: Self-Supervised Capsules in Canonical Pose. NeurIPS 2021: 24993-25005 - [i43]Geoffrey E. Hinton:
How to represent part-whole hierarchies in a neural network. CoRR abs/2102.12627 (2021) - [i42]Ting Chen, Saurabh Saxena, Lala Li, David J. Fleet, Geoffrey E. Hinton:
Pix2seq: A Language Modeling Framework for Object Detection. CoRR abs/2109.10852 (2021) - 2020
- [c153]Boyang Deng, Kyle Genova, Soroosh Yazdani, Sofien Bouaziz, Geoffrey E. Hinton, Andrea Tagliasacchi:
CvxNet: Learnable Convex Decomposition. CVPR 2020: 31-41 - [c152]Boyang Deng, John P. Lewis, Timothy Jeruzalski, Gerard Pons-Moll, Geoffrey E. Hinton, Mohammad Norouzi, Andrea Tagliasacchi:
NASA Neural Articulated Shape Approximation. ECCV (7) 2020: 612-628 - [c151]Yao Qin, Nicholas Frosst, Sara Sabour, Colin Raffel, Garrison W. Cottrell, Geoffrey E. Hinton:
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions. ICLR 2020 - [c150]William Chan, Chitwan Saharia, Geoffrey E. Hinton, Mohammad Norouzi, Navdeep Jaitly:
Imputer: Sequence Modelling via Imputation and Dynamic Programming. ICML 2020: 1403-1413 - [c149]Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey E. Hinton:
A Simple Framework for Contrastive Learning of Visual Representations. ICML 2020: 1597-1607 - [c148]Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey E. Hinton:
Big Self-Supervised Models are Strong Semi-Supervised Learners. NeurIPS 2020 - [c147]Geoffrey E. Hinton:
The Next Generation of Neural Networks. SIGIR 2020: 1 - [i41]Rafael Müller, Simon Kornblith, Geoffrey E. Hinton:
Subclass Distillation. CoRR abs/2002.03936 (2020) - [i40]Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey E. Hinton:
A Simple Framework for Contrastive Learning of Visual Representations. CoRR abs/2002.05709 (2020) - [i39]Yao Qin, Nicholas Frosst, Colin Raffel, Garrison W. Cottrell, Geoffrey E. Hinton:
Deflecting Adversarial Attacks. CoRR abs/2002.07405 (2020) - [i38]William Chan, Chitwan Saharia, Geoffrey E. Hinton, Mohammad Norouzi, Navdeep Jaitly:
Imputer: Sequence Modelling via Imputation and Dynamic Programming. CoRR abs/2002.08926 (2020) - [i37]Rishabh Agarwal, Nicholas Frosst, Xuezhou Zhang, Rich Caruana, Geoffrey E. Hinton:
Neural Additive Models: Interpretable Machine Learning with Neural Nets. CoRR abs/2004.13912 (2020) - [i36]Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey E. Hinton:
Big Self-Supervised Models are Strong Semi-Supervised Learners. CoRR abs/2006.10029 (2020) - [i35]Aniruddh Raghu, Maithra Raghu, Simon Kornblith, David Duvenaud, Geoffrey E. Hinton:
Teaching with Commentaries. CoRR abs/2011.03037 (2020) - [i34]Sara Sabour, Andrea Tagliasacchi, Soroosh Yazdani, Geoffrey E. Hinton, David J. Fleet:
Unsupervised part representation by Flow Capsules. CoRR abs/2011.13920 (2020) - [i33]Weiwei Sun, Andrea Tagliasacchi, Boyang Deng, Sara Sabour, Soroosh Yazdani, Geoffrey E. Hinton, Kwang Moo Yi:
Canonical Capsules: Unsupervised Capsules in Canonical Pose. CoRR abs/2012.04718 (2020)
2010 – 2019
- 2019
- [c146]Nicholas Frosst, Nicolas Papernot, Geoffrey E. Hinton:
Analyzing and Improving Representations with the Soft Nearest Neighbor Loss. ICML 2019: 2012-2020 - [c145]Simon Kornblith, Mohammad Norouzi, Honglak Lee, Geoffrey E. Hinton:
Similarity of Neural Network Representations Revisited. ICML 2019: 3519-3529 - [c144]Rafael Müller, Simon Kornblith, Geoffrey E. Hinton:
When does label smoothing help? NeurIPS 2019: 4696-4705 - [c143]Michael R. Zhang, James Lucas, Jimmy Ba, Geoffrey E. Hinton:
Lookahead Optimizer: k steps forward, 1 step back. NeurIPS 2019: 9593-9604 - [c142]Adam R. Kosiorek, Sara Sabour, Yee Whye Teh, Geoffrey E. Hinton:
Stacked Capsule Autoencoders. NeurIPS 2019: 15486-15496 - [i32]Nicholas Frosst, Nicolas Papernot, Geoffrey E. Hinton:
Analyzing and Improving Representations with the Soft Nearest Neighbor Loss. CoRR abs/1902.01889 (2019) - [i31]Simon Kornblith, Mohammad Norouzi, Honglak Lee, Geoffrey E. Hinton:
Similarity of Neural Network Representations Revisited. CoRR abs/1905.00414 (2019) - [i30]Boyang Deng, Simon Kornblith, Geoffrey E. Hinton:
Cerberus: A Multi-headed Derenderer. CoRR abs/1905.11940 (2019) - [i29]Aidan N. Gomez, Ivan Zhang, Kevin Swersky, Yarin Gal, Geoffrey E. Hinton:
Learning Sparse Networks Using Targeted Dropout. CoRR abs/1905.13678 (2019) - [i28]Rafael Müller, Simon Kornblith, Geoffrey E. Hinton:
When Does Label Smoothing Help? CoRR abs/1906.02629 (2019) - [i27]Adam R. Kosiorek, Sara Sabour, Yee Whye Teh, Geoffrey E. Hinton:
Stacked Capsule Autoencoders. CoRR abs/1906.06818 (2019) - [i26]Yao Qin, Nicholas Frosst, Sara Sabour, Colin Raffel, Garrison W. Cottrell, Geoffrey E. Hinton:
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions. CoRR abs/1907.02957 (2019) - [i25]Michael R. Zhang, James Lucas, Geoffrey E. Hinton, Jimmy Ba:
Lookahead Optimizer: k steps forward, 1 step back. CoRR abs/1907.08610 (2019) - [i24]Boyang Deng, Kyle Genova, Soroosh Yazdani, Sofien Bouaziz, Geoffrey E. Hinton, Andrea Tagliasacchi:
CvxNets: Learnable Convex Decomposition. CoRR abs/1909.05736 (2019) - [i23]Timothy Jeruzalski, Boyang Deng, Mohammad Norouzi, John P. Lewis, Geoffrey E. Hinton, Andrea Tagliasacchi:
NASA: Neural Articulated Shape Approximation. CoRR abs/1912.03207 (2019) - 2018
- [c141]Melody Y. Guan, Varun Gulshan, Andrew M. Dai, Geoffrey E. Hinton:
Who Said What: Modeling Individual Labelers Improves Classification. AAAI 2018: 3109-3118 - [c140]Jamie Ryan Kiros, William Chan, Geoffrey E. Hinton:
Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search. ACL (1) 2018: 922-933 - [c139]Rohan Anil, Gabriel Pereyra, Alexandre Passos, Róbert Ormándi, George E. Dahl, Geoffrey E. Hinton:
Large scale distributed neural network training through online distillation. ICLR (Poster) 2018 - [c138]Geoffrey E. Hinton, Sara Sabour, Nicholas Frosst:
Matrix capsules with EM routing. ICLR (Poster) 2018 - [c137]Sergey Bartunov, Adam Santoro, Blake A. Richards, Luke Marris, Geoffrey E. Hinton, Timothy P. Lillicrap:
Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures. NeurIPS 2018: 9390-9400 - [i22]Rohan Anil, Gabriel Pereyra, Alexandre Passos, Róbert Ormándi, George E. Dahl, Geoffrey E. Hinton:
Large scale distributed neural network training through online distillation. CoRR abs/1804.03235 (2018) - [i21]Sergey Bartunov, Adam Santoro, Blake A. Richards, Geoffrey E. Hinton, Timothy P. Lillicrap:
Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures. CoRR abs/1807.04587 (2018) - [i20]Nicholas Frosst, Sara Sabour, Geoffrey E. Hinton:
DARCCC: Detecting Adversaries by Reconstruction from Class Conditional Capsules. CoRR abs/1811.06969 (2018) - 2017
- [j68]Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton:
ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6): 84-90 (2017) - [c136]Nicholas Frosst, Geoffrey E. Hinton:
Distilling a Neural Network Into a Soft Decision Tree. CEx@AI*IA 2017 - [c135]Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, Geoffrey E. Hinton:
Regularizing Neural Networks by Penalizing Confident Output Distributions. ICLR (Workshop) 2017 - [c134]Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc V. Le, Geoffrey E. Hinton, Jeff Dean:
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. ICLR (Poster) 2017 - [c133]Sara Sabour, Nicholas Frosst, Geoffrey E. Hinton:
Dynamic Routing Between Capsules. NIPS 2017: 3856-3866 - [r4]Geoffrey E. Hinton:
Boltzmann Machines. Encyclopedia of Machine Learning and Data Mining 2017: 164-168 - [r3]Geoffrey E. Hinton:
Deep Belief Nets. Encyclopedia of Machine Learning and Data Mining 2017: 335-338 - [i19]Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc V. Le, Geoffrey E. Hinton, Jeff Dean:
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. CoRR abs/1701.06538 (2017) - [i18]Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, Geoffrey E. Hinton:
Regularizing Neural Networks by Penalizing Confident Output Distributions. CoRR abs/1701.06548 (2017) - [i17]Melody Y. Guan, Varun Gulshan, Andrew M. Dai, Geoffrey E. Hinton:
Who Said What: Modeling Individual Labelers Improves Classification. CoRR abs/1703.08774 (2017) - [i16]Sara Sabour, Nicholas Frosst, Geoffrey E. Hinton:
Dynamic Routing Between Capsules. CoRR abs/1710.09829 (2017) - [i15]Nicholas Frosst, Geoffrey E. Hinton:
Distilling a Neural Network Into a Soft Decision Tree. CoRR abs/1711.09784 (2017) - 2016
- [c132]S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Koray Kavukcuoglu, Geoffrey E. Hinton:
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models. NIPS 2016: 3225-3233 - [c131]Jimmy Ba, Geoffrey E. Hinton, Volodymyr Mnih, Joel Z. Leibo, Catalin Ionescu:
Using Fast Weights to Attend to the Recent Past. NIPS 2016: 4331-4339 - [i14]S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, Koray Kavukcuoglu, Geoffrey E. Hinton:
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models. CoRR abs/1603.08575 (2016) - [i13]Lei Jimmy Ba, Jamie Ryan Kiros, Geoffrey E. Hinton:
Layer Normalization. CoRR abs/1607.06450 (2016) - [i12]Jimmy Ba, Geoffrey E. Hinton, Volodymyr Mnih, Joel Z. Leibo, Catalin Ionescu:
Using Fast Weights to Attend to the Recent Past. CoRR abs/1610.06258 (2016) - 2015
- [j67]Marc'Aurelio Ranzato, Geoffrey E. Hinton, Yann LeCun:
Guest Editorial: Deep Learning. Int. J. Comput. Vis. 113(1): 1-2 (2015) - [j66]Yann LeCun, Yoshua Bengio, Geoffrey E. Hinton:
Deep learning. Nat. 521(7553): 436-444 (2015) - [c130]Oriol Vinyals, Lukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, Geoffrey E. Hinton:
Grammar as a Foreign Language. NIPS 2015: 2773-2781 - [i11]Geoffrey E. Hinton, Oriol Vinyals, Jeffrey Dean:
Distilling the Knowledge in a Neural Network. CoRR abs/1503.02531 (2015) - [i10]Quoc V. Le, Navdeep Jaitly, Geoffrey E. Hinton:
A Simple Way to Initialize Recurrent Networks of Rectified Linear Units. CoRR abs/1504.00941 (2015) - 2014
- [j65]Geoffrey E. Hinton:
Where Do Features Come From? Cogn. Sci. 38(6): 1078-1101 (2014) - [j64]Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov:
Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1): 1929-1958 (2014) - [j63]Ruhi Sarikaya, Geoffrey E. Hinton, Anoop Deoras:
Application of Deep Belief Networks for Natural Language Understanding. IEEE ACM Trans. Audio Speech Lang. Process. 22(4): 778-784 (2014) - [c129]Navdeep Jaitly, Vincent Vanhoucke, Geoffrey E. Hinton:
Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models. INTERSPEECH 2014: 1905-1909 - [i9]Oriol Vinyals, Lukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, Geoffrey E. Hinton:
Grammar as a Foreign Language. CoRR abs/1412.7449 (2014) - 2013
- [j62]Marc'Aurelio Ranzato, Volodymyr Mnih, Joshua M. Susskind, Geoffrey E. Hinton:
Modeling Natural Images Using Gated MRFs. IEEE Trans. Pattern Anal. Mach. Intell. 35(9): 2206-2222 (2013) - [c128]Matthew D. Zeiler, Marc'Aurelio Ranzato, Rajat Monga, Mark Z. Mao, K. Yang, Quoc Viet Le, Patrick Nguyen, Andrew W. Senior, Vincent Vanhoucke, Jeffrey Dean, Geoffrey E. Hinton:
On rectified linear units for speech processing. ICASSP 2013: 3517-3521 - [c127]Alex Graves, Abdel-rahman Mohamed, Geoffrey E. Hinton:
Speech recognition with deep recurrent neural networks. ICASSP 2013: 6645-6649 - [c126]Li Deng, Geoffrey E. Hinton, Brian Kingsbury:
New types of deep neural network learning for speech recognition and related applications: an overview. ICASSP 2013: 8599-8603 - [c125]George E. Dahl, Tara N. Sainath, Geoffrey E. Hinton:
Improving deep neural networks for LVCSR using rectified linear units and dropout. ICASSP 2013: 8609-8613 - [c124]Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton:
Tensor Analyzers. ICML (3) 2013: 163-171 - [c123]Ilya Sutskever, James Martens, George E. Dahl, Geoffrey E. Hinton:
On the importance of initialization and momentum in deep learning. ICML (3) 2013: 1139-1147 - [c122]Navdeep Jaitly, Geoffrey E. Hinton:
Using an autoencoder with deformable templates to discover features for automated speech recognition. INTERSPEECH 2013: 1737-1740 - [c121]Nitish Srivastava, Ruslan Salakhutdinov, Geoffrey E. Hinton:
Modeling Documents with Deep Boltzmann Machines. UAI 2013 - [i8]Geoffrey E. Hinton, Yee Whye Teh:
Discovering Multiple Constraints that are Frequently Approximately Satisfied. CoRR abs/1301.2278 (2013) - [i7]Alex Graves, Abdel-rahman Mohamed, Geoffrey E. Hinton:
Speech Recognition with Deep Recurrent Neural Networks. CoRR abs/1303.5778 (2013) - [i6]Nitish Srivastava, Ruslan Salakhutdinov, Geoffrey E. Hinton:
Modeling Documents with Deep Boltzmann Machines. CoRR abs/1309.6865 (2013) - 2012
- [j61]Laurens van der Maaten, Geoffrey E. Hinton:
Visualizing non-metric similarities in multiple maps. Mach. Learn. 87(1): 33-55 (2012) - [j60]Ruslan Salakhutdinov, Geoffrey E. Hinton:
An Efficient Learning Procedure for Deep Boltzmann Machines. Neural Comput. 24(8): 1967-2006 (2012) - [j59]Dong Yu, Geoffrey E. Hinton, Nelson Morgan, Jen-Tzung Chien, Shigeki Sagayama:
Introduction to the Special Section on Deep Learning for Speech and Language Processing. IEEE Trans. Speech Audio Process. 20(1): 4-6 (2012) - [j58]Abdel-rahman Mohamed, George E. Dahl, Geoffrey E. Hinton:
Acoustic Modeling Using Deep Belief Networks. IEEE Trans. Speech Audio Process. 20(1): 14-22 (2012) - [c120]Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton:
Robust Boltzmann Machines for recognition and denoising. CVPR 2012: 2264-2271 - [c119]Abdel-rahman Mohamed, Geoffrey E. Hinton, Gerald Penn:
Understanding how Deep Belief Networks perform acoustic modelling. ICASSP 2012: 4273-4276 - [c118]Volodymyr Mnih, Geoffrey E. Hinton:
Learning to Label Aerial Images from Noisy Data. ICML 2012 - [c117]Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton:
Deep Mixtures of Factor Analysers. ICML 2012 - [c116]Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton:
Deep Lambertian Networks. ICML 2012 - [c115]Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton:
ImageNet Classification with Deep Convolutional Neural Networks. NIPS 2012: 1106-1114 - [c114]Ruslan Salakhutdinov, Geoffrey E. Hinton:
A Better Way to Pretrain Deep Boltzmann Machines. NIPS 2012: 2456-2464 - [p4]Geoffrey E. Hinton:
A Practical Guide to Training Restricted Boltzmann Machines. Neural Networks: Tricks of the Trade (2nd ed.) 2012: 599-619 - [i5]Volodymyr Mnih, Hugo Larochelle, Geoffrey E. Hinton:
Conditional Restricted Boltzmann Machines for Structured Output Prediction. CoRR abs/1202.3748 (2012) - [i4]Graham W. Taylor, Geoffrey E. Hinton:
Products of Hidden Markov Models: It Takes N>1 to Tango. CoRR abs/1205.2614 (2012) - [i3]Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton:
Deep Mixtures of Factor Analysers. CoRR abs/1206.4635 (2012) - [i2]Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov:
Improving neural networks by preventing co-adaptation of feature detectors. CoRR abs/1207.0580 (2012) - [i1]Max Welling, Richard S. Zemel, Geoffrey E. Hinton:
Efficient Parametric Projection Pursuit Density Estimation. CoRR abs/1212.2513 (2012) - 2011
- [j57]Geoffrey E. Hinton:
A better way to learn features: technical perspective. Commun. ACM 54(10): 94 (2011) - [j56]Graham W. Taylor, Geoffrey E. Hinton, Sam T. Roweis:
Two Distributed-State Models For Generating High-Dimensional Time Series. J. Mach. Learn. Res. 12: 1025-1068 (2011) - [j55]Geoffrey E. Hinton, Ruslan Salakhutdinov:
Discovering Binary Codes for Documents by Learning Deep Generative Models. Top. Cogn. Sci. 3(1): 74-91 (2011) - [c113]Joshua M. Susskind, Geoffrey E. Hinton, Roland Memisevic, Marc Pollefeys:
Modeling the joint density of two images under a variety of transformations. CVPR 2011: 2793-2800 - [c112]Marc'Aurelio Ranzato, Joshua M. Susskind, Volodymyr Mnih, Geoffrey E. Hinton:
On deep generative models with applications to recognition. CVPR 2011: 2857-2864 - [c111]Alex Krizhevsky, Geoffrey E. Hinton:
Using very deep autoencoders for content-based image retrieval. ESANN 2011 - [c110]Geoffrey E. Hinton, Alex Krizhevsky, Sida D. Wang:
Transforming Auto-Encoders. ICANN (1) 2011: 44-51 - [c109]Abdel-rahman Mohamed, Tara N. Sainath, George E. Dahl, Bhuvana Ramabhadran, Geoffrey E. Hinton, Michael A. Picheny:
Deep Belief Networks using discriminative features for phone recognition. ICASSP 2011: 5060-5063 - [c108]Ruhi Sarikaya, Geoffrey E. Hinton, Bhuvana Ramabhadran:
Deep belief nets for natural language call-routing. ICASSP 2011: 5680-5683 - [c107]Navdeep Jaitly, Geoffrey E. Hinton:
Learning a better representation of speech soundwaves using restricted boltzmann machines. ICASSP 2011: 5884-5887 - [c106]Ilya Sutskever, James Martens, Geoffrey E. Hinton:
Generating Text with Recurrent Neural Networks. ICML 2011: 1017-1024 - [c105]Volodymyr Mnih, Hugo Larochelle, Geoffrey E. Hinton:
Conditional Restricted Boltzmann Machines for Structured Output Prediction. UAI 2011: 514-522 - 2010
- [j54]Roland Memisevic, Geoffrey E. Hinton:
Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines. Neural Comput. 22(6): 1473-1492 (2010) - [j53]Tanya Schmah, Grigori Yourganov, Richard S. Zemel, Geoffrey E. Hinton, Steven L. Small, Stephen C. Strother:
Comparing Classification Methods for Longitudinal fMRI Studies. Neural Comput. 22(11): 2729-2762 (2010) - [j52]Ilya Sutskever, Geoffrey E. Hinton:
Temporal-Kernel Recurrent Neural Networks. Neural Networks 23(2): 239-243 (2010) - [c104]Graham W. Taylor, Leonid Sigal, David J. Fleet, Geoffrey E. Hinton:
Dynamical binary latent variable models for 3D human pose tracking. CVPR 2010: 631-638 - [c103]Marc'Aurelio Ranzato, Geoffrey E. Hinton:
Modeling pixel means and covariances using factorized third-order boltzmann machines. CVPR 2010: 2551-2558 - [c102]Volodymyr Mnih, Geoffrey E. Hinton:
Learning to Detect Roads in High-Resolution Aerial Images. ECCV (6) 2010: 210-223 - [c101]Abdel-rahman Mohamed, Geoffrey E. Hinton:
Phone recognition using Restricted Boltzmann Machines. ICASSP 2010: 4354-4357 - [c100]Vinod Nair, Geoffrey E. Hinton:
Rectified Linear Units Improve Restricted Boltzmann Machines. ICML 2010: 807-814 - [c99]Li Deng, Michael L. Seltzer, Dong Yu, Alex Acero, Abdel-rahman Mohamed, Geoffrey E. Hinton:
Binary coding of speech spectrograms using a deep auto-encoder. INTERSPEECH 2010: 1692-1695 - [c98]George E. Dahl, Marc'Aurelio Ranzato, Abdel-rahman Mohamed, Geoffrey E. Hinton:
Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine. NIPS 2010: 469-477 - [c97]Hugo Larochelle, Geoffrey E. Hinton:
Learning to combine foveal glimpses with a third-order Boltzmann machine. NIPS 2010: 1243-1251 - [c96]Roland Memisevic, Christopher Zach, Geoffrey E. Hinton, Marc Pollefeys:
Gated Softmax Classification. NIPS 2010: 1603-1611 - [c95]Marc'Aurelio Ranzato, Volodymyr Mnih, Geoffrey E. Hinton:
Generating more realistic images using gated MRF's. NIPS 2010: 2002-2010 - [c94]Marc'Aurelio Ranzato, Alex Krizhevsky, Geoffrey E. Hinton:
Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images. AISTATS 2010: 621-628 - [r2]Geoffrey E. Hinton:
Boltzmann Machines. Encyclopedia of Machine Learning 2010: 132-136 - [r1]Geoffrey E. Hinton:
Deep Belief Nets. Encyclopedia of Machine Learning 2010: 267-269
2000 – 2009
- 2009
- [j51]Ruslan Salakhutdinov, Geoffrey E. Hinton:
Semantic hashing. Int. J. Approx. Reason. 50(7): 969-978 (2009) - [j50]Andriy Mnih, Zhang Yuecheng, Geoffrey E. Hinton:
Improving a statistical language model through non-linear prediction. Neurocomputing 72(7-9): 1414-1418 (2009) - [j49]Geoffrey E. Hinton:
Deep belief networks. Scholarpedia 4(5): 5947 (2009) - [c93]Nicolas Heess, Christopher K. I. Williams, Geoffrey E. Hinton:
Learning Generative Texture Models with extended Fields-of-Experts. BMVC 2009: 1-11 - [c92]Matthew D. Zeiler, Graham W. Taylor, Nikolaus F. Troje, Geoffrey E. Hinton:
Modeling pigeon behavior using a Conditional Restricted Boltzmann Machine. ESANN 2009 - [c91]Kai Yu, Ruslan Salakhutdinov, Yann LeCun, Geoffrey E. Hinton, Yoshua Bengio:
Workshop summary: Workshop on learning feature hierarchies. ICML 2009: 5 - [c90]Graham W. Taylor, Geoffrey E. Hinton:
Factored conditional restricted Boltzmann Machines for modeling motion style. ICML 2009: 1025-1032 - [c89]Tijmen Tieleman, Geoffrey E. Hinton:
Using fast weights to improve persistent contrastive divergence. ICML 2009: 1033-1040 - [c88]Vinod Nair, Geoffrey E. Hinton:
3D Object Recognition with Deep Belief Nets. NIPS 2009: 1339-1347 - [c87]Mark Palatucci, Dean Pomerleau, Geoffrey E. Hinton, Tom M. Mitchell:
Zero-shot Learning with Semantic Output Codes. NIPS 2009: 1410-1418 - [c86]Ruslan Salakhutdinov, Geoffrey E. Hinton:
Replicated Softmax: an Undirected Topic Model. NIPS 2009: 1607-1614 - [c85]Graham W. Taylor, Geoffrey E. Hinton:
Products of Hidden Markov Models: It Takes N>1 to Tango. UAI 2009: 522-529 - [c84]Ruslan Salakhutdinov, Geoffrey E. Hinton:
Deep Boltzmann Machines. AISTATS 2009: 448-455 - 2008
- [j48]Ilya Sutskever, Geoffrey E. Hinton:
Deep, Narrow Sigmoid Belief Networks Are Universal Approximators. Neural Comput. 20(11): 2629-2636 (2008) - [c83]Zhang Yuecheng, Andriy Mnih, Geoffrey E. Hinton:
Improving a statistical language model by modulating the effects of context words. ESANN 2008: 493-498 - [c82]Vinod Nair, Joshua M. Susskind, Geoffrey E. Hinton:
Analysis-by-Synthesis by Learning to Invert Generative Black Boxes. ICANN (1) 2008: 971-981 - [c81]Andriy Mnih, Geoffrey E. Hinton:
A Scalable Hierarchical Distributed Language Model. NIPS 2008: 1081-1088 - [c80]Vinod Nair, Geoffrey E. Hinton:
Implicit Mixtures of Restricted Boltzmann Machines. NIPS 2008: 1145-1152 - [c79]Tanya Schmah, Geoffrey E. Hinton, Richard S. Zemel, Steven L. Small, Stephen C. Strother:
Generative versus discriminative training of RBMs for classification of fMRI images. NIPS 2008: 1409-1416 - [c78]Ilya Sutskever, Geoffrey E. Hinton:
Using matrices to model symbolic relationship. NIPS 2008: 1593-1600 - [c77]Ilya Sutskever, Geoffrey E. Hinton, Graham W. Taylor:
The Recurrent Temporal Restricted Boltzmann Machine. NIPS 2008: 1601-1608 - 2007
- [j47]Geoffrey E. Hinton:
Boltzmann machine. Scholarpedia 2(5): 1668 (2007) - [c76]Roland Memisevic, Geoffrey E. Hinton:
Unsupervised Learning of Image Transformations. CVPR 2007 - [c75]Andriy Mnih, Geoffrey E. Hinton:
Three new graphical models for statistical language modelling. ICML 2007: 641-648 - [c74]Ruslan Salakhutdinov, Andriy Mnih, Geoffrey E. Hinton:
Restricted Boltzmann machines for collaborative filtering. ICML 2007: 791-798 - [c73]Simon Osindero, Geoffrey E. Hinton:
Modeling image patches with a directed hierarchy of Markov random fields. NIPS 2007: 1121-1128 - [c72]Ruslan Salakhutdinov, Geoffrey E. Hinton:
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes. NIPS 2007: 1249-1256 - [c71]James Cook, Ilya Sutskever, Andriy Mnih, Geoffrey E. Hinton:
Visualizing Similarity Data with a Mixture of Maps. AISTATS 2007: 67-74 - [c70]Ruslan Salakhutdinov, Geoffrey E. Hinton:
Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure. AISTATS 2007: 412-419 - [c69]Ilya Sutskever, Geoffrey E. Hinton:
Learning Multilevel Distributed Representations for High-Dimensional Sequences. AISTATS 2007: 548-555 - 2006
- [j46]Geoffrey E. Hinton, Simon Osindero, Max Welling, Yee Whye Teh:
Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation. Cogn. Sci. 30(4): 725-731 (2006) - [j45]Simon Osindero, Max Welling, Geoffrey E. Hinton:
Topographic Product Models Applied to Natural Scene Statistics. Neural Comput. 18(2): 381-414 (2006) - [j44]Geoffrey E. Hinton, Simon Osindero, Yee Whye Teh:
A Fast Learning Algorithm for Deep Belief Nets. Neural Comput. 18(7): 1527-1554 (2006) - [c68]Graham W. Taylor, Geoffrey E. Hinton, Sam T. Roweis:
Modeling Human Motion Using Binary Latent Variables. NIPS 2006: 1345-1352 - 2005
- [j43]Roland Memisevic, Geoffrey E. Hinton:
Improving dimensionality reduction with spectral gradient descent. Neural Networks 18(5-6): 702-710 (2005) - [c67]Miguel Á. Carreira-Perpiñán, Geoffrey E. Hinton:
On Contrastive Divergence Learning. AISTATS 2005: 33-40 - [c66]Geoffrey E. Hinton, Simon Osindero, Kejie Bao:
Learning Causally Linked Markov Random Fields. AISTATS 2005: 128-135 - [c65]Geoffrey E. Hinton:
What kind of graphical model is the brain? IJCAI 2005: 1765- - [c64]Geoffrey E. Hinton, Vinod Nair:
Inferring Motor Programs from Images of Handwritten Digits. NIPS 2005: 515-522 - 2004
- [j42]Brian Sallans, Geoffrey E. Hinton:
Reinforcement Learning with Factored States and Actions. J. Mach. Learn. Res. 5: 1063-1088 (2004) - [j41]Max Welling, Richard S. Zemel, Geoffrey E. Hinton:
Probabilistic sequential independent components analysis. IEEE Trans. Neural Networks 15(4): 838-849 (2004) - [c63]Christopher M. Bishop, Markus Svensén, Geoffrey E. Hinton:
Distinguishing text from graphics in on-line handwritten ink. IWFHR 2004: 142-147 - [c62]Jacob Goldberger, Sam T. Roweis, Geoffrey E. Hinton, Ruslan Salakhutdinov:
Neighbourhood Components Analysis. NIPS 2004: 513-520 - [c61]Roland Memisevic, Geoffrey E. Hinton:
Multiple Relational Embedding. NIPS 2004: 913-920 - [c60]Max Welling, Michal Rosen-Zvi, Geoffrey E. Hinton:
Exponential Family Harmoniums with an Application to Information Retrieval. NIPS 2004: 1481-1488 - 2003
- [j40]Yee Whye Teh, Max Welling, Simon Osindero, Geoffrey E. Hinton:
Energy-Based Models for Sparse Overcomplete Representations. J. Mach. Learn. Res. 4: 1235-1260 (2003) - [c59]Geoffrey E. Hinton, Max Welling, Andriy Mnih:
Wormholes Improve Contrastive Divergence. NIPS 2003: 417-424 - [c58]Max Welling, Richard S. Zemel, Geoffrey E. Hinton:
Efficient Parametric Projection Pursuit Density Estimation. UAI 2003: 575-582 - 2002
- [j39]Fiora Pirri, Geoffrey E. Hinton, Hector J. Levesque:
In Memory of Ray Reiter (1939-2002). AI Mag. 23(4): 93 (2002) - [j38]Sageev Oore, Demetri Terzopoulos, Geoffrey E. Hinton:
Local Physical Models for Interactive Character Animation. Comput. Graph. Forum 21(3): 337-346 (2002) - [j37]Geoffrey E. Hinton:
Training Products of Experts by Minimizing Contrastive Divergence. Neural Comput. 14(8): 1771-1800 (2002) - [j36]Karl J. Friston, William D. Penny, Christophe Phillips, Stefan J. Kiebel, Geoffrey E. Hinton, John Ashburner:
Classical and Bayesian Inference in Neuroimaging: Theory. NeuroImage 16(2): 465-483 (2002) - [j35]Guy Mayraz, Geoffrey E. Hinton:
Recognizing Handwritten Digits Using Hierarchical Products of Experts. IEEE Trans. Pattern Anal. Mach. Intell. 24(2): 189-197 (2002) - [c57]Sageev Oore, Demetri Terzopoulos, Geoffrey E. Hinton:
A Desktop Input Device and Interface for Interactive 3D Character Animation. Graphics Interface 2002: 133-140 - [c56]Max Welling, Geoffrey E. Hinton:
A New Learning Algorithm for Mean Field Boltzmann Machines. ICANN 2002: 351-357 - [c55]Max Welling, Richard S. Zemel, Geoffrey E. Hinton:
Self Supervised Boosting. NIPS 2002: 665-672 - [c54]Geoffrey E. Hinton, Sam T. Roweis:
Stochastic Neighbor Embedding. NIPS 2002: 833-840 - [c53]Max Welling, Geoffrey E. Hinton, Simon Osindero:
Learning Sparse Topographic Representations with Products of Student-t Distributions. NIPS 2002: 1359-1366 - 2001
- [j34]Alberto Paccanaro, Geoffrey E. Hinton:
Learning Distributed Representations of Concepts Using Linear Relational Embedding. IEEE Trans. Knowl. Data Eng. 13(2): 232-244 (2001) - [c52]Andrew D. Brown, Geoffrey E. Hinton:
Products of Hidden Markov Models. AISTATS 2001: 21-28 - [c51]Alberto Paccanaro, Geoffrey E. Hinton:
Learning Hierarchical Structures with Linear Relational Embedding. NIPS 2001: 857-864 - [c50]Sam T. Roweis, Lawrence K. Saul, Geoffrey E. Hinton:
Global Coordination of Local Linear Models. NIPS 2001: 889-896 - [c49]Andrew D. Brown, Geoffrey E. Hinton:
Relative Density Nets: A New Way to Combine Backpropagation with HMM's. NIPS 2001: 1149-1156 - [c48]Geoffrey E. Hinton, Yee Whye Teh:
Discovering Multiple Constraints that are Frequently Approximately Satisfied. UAI 2001: 227-234 - [c47]Alberto Paccanaro, Geoffrey E. Hinton:
Learning Distributed Representations of Relational Data using Linear Relational Embedding. WIRN 2001: 134-143 - 2000
- [j33]Zoubin Ghahramani, Geoffrey E. Hinton:
Variational Learning for Switching State-Space Models. Neural Comput. 12(4): 831-864 (2000) - [j32]Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton:
SMEM Algorithm for Mixture Models. Neural Comput. 12(9): 2109-2128 (2000) - [j31]Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton:
Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates. J. VLSI Signal Process. 26(1-2): 133-140 (2000) - [c46]Geoffrey E. Hinton:
Modeling High-Dimensional Data by Combining Simple Experts. AAAI/IAAI 2000: 1159-1164 - [c45]Alberto Paccanaro, Geoffrey E. Hinton:
Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space. ICML 2000: 711-718 - [c44]Alberto Paccanaro, Geoffrey E. Hinton:
Extracting Distributed Representations of Concepts and Relations from Positive and Negative Propositions. IJCNN (2) 2000: 259-264 - [c43]Yee Whye Teh, Geoffrey E. Hinton:
Rate-coded Restricted Boltzmann Machines for Face Recognition. NIPS 2000: 908-914 - [c42]Guy Mayraz, Geoffrey E. Hinton:
Recognizing Hand-written Digits Using Hierarchical Products of Experts. NIPS 2000: 953-959 - [c41]Brian Sallans, Geoffrey E. Hinton:
Using Free Energies to Represent Q-values in a Multiagent Reinforcement Learning Task. NIPS 2000: 1075-1081
1990 – 1999
- 1999
- [j30]Brendan J. Frey, Geoffrey E. Hinton:
Variational Learning in Nonlinear Gaussian Belief Networks. Neural Comput. 11(1): 193-213 (1999) - [c40]Geoffrey E. Hinton, Andrew D. Brown:
Spiking Boltzmann Machines. NIPS 1999: 122-128 - [c39]Geoffrey E. Hinton, Zoubin Ghahramani, Yee Whye Teh:
Learning to Parse Images. NIPS 1999: 463-469 - 1998
- [j29]Robert Tibshirani, Geoffrey E. Hinton:
Coaching variables for regression and classification. Stat. Comput. 8(1): 25-33 (1998) - [j28]Sidney S. Fels, Geoffrey E. Hinton:
Glove-TalkII-a neural-network interface which maps gestures to parallel formant speech synthesizer controls. IEEE Trans. Neural Networks 9(1): 205-212 (1998) - [c38]Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton:
SMEM Algorithm for Mixture Models. NIPS 1998: 599-605 - [c37]Radek Grzeszczuk, Demetri Terzopoulos, Geoffrey E. Hinton:
Fast Neural Network Emulation of Dynamical Systems for Computer Animation. NIPS 1998: 882-888 - [c36]Radek Grzeszczuk, Demetri Terzopoulos, Geoffrey E. Hinton:
NeuroAnimator: Fast Neural Network Emulation and Control of Physics-based Models. SIGGRAPH 1998: 9-20 - [p3]Radford M. Neal, Geoffrey E. Hinton:
A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants. Learning in Graphical Models 1998: 355-368 - [p2]Geoffrey E. Hinton, Brian Sallans, Zoubin Ghahramani:
A Hierarchical Community of Experts. Learning in Graphical Models 1998: 479-494 - 1997
- [j27]Brendan J. Frey, Geoffrey E. Hinton:
Efficient Stochastic Source Coding and an Application to a Bayesian Network Source Model. Comput. J. 40(2/3): 157-165 (1997) - [j26]Christopher K. I. Williams, Michael Revow, Geoffrey E. Hinton:
Instantiating Deformable Models with a Neural Net. Comput. Vis. Image Underst. 68(1): 120-126 (1997) - [j25]Peter Dayan, Geoffrey E. Hinton:
Using Expectation-Maximization for Reinforcement Learning. Neural Comput. 9(2): 271-278 (1997) - [j24]Sageev Oore, Geoffrey E. Hinton, Gregory Dudek:
A Mobile Robot that Learns its Place. Neural Comput. 9(3): 683-699 (1997) - [j23]Geoffrey E. Hinton, Peter Dayan, Michael Revow:
Modeling the manifolds of images of handwritten digits. IEEE Trans. Neural Networks 8(1): 65-74 (1997) - [j22]Sidney S. Fels, Geoffrey E. Hinton:
Glove-talk II - a neural-network interface which maps gestures to parallel formant speech synthesizer controls. IEEE Trans. Neural Networks 8(5): 977-984 (1997) - [c35]Zoubin Ghahramani, Geoffrey E. Hinton:
Hierarchical Non-linear Factor Analysis and Topographic Maps. NIPS 1997: 486-492 - [c34]Radek Grzeszczuk, Demetri Terzopoulos, Geoffrey E. Hinton:
Learning fast neural network emulators for physics-based models. SIGGRAPH Visual Proceedings 1997: 167 - 1996
- [j21]Peter Dayan, Geoffrey E. Hinton:
Varieties of Helmholtz Machine. Neural Networks 9(8): 1385-1403 (1996) - [j20]Michael Revow, Christopher K. I. Williams, Geoffrey E. Hinton:
Using Generative Models for Handwritten Digit Recognition. IEEE Trans. Pattern Anal. Mach. Intell. 18(6): 592-606 (1996) - [c33]Brendan J. Frey, Geoffrey E. Hinton:
Free Energy Coding. Data Compression Conference 1996: 73-81 - 1995
- [j19]Richard S. Zemel, Geoffrey E. Hinton:
Learning Population Codes by Minimizing Description Length. Neural Comput. 7(3): 549-564 (1995) - [j18]Peter Dayan, Geoffrey E. Hinton, Radford M. Neal, Richard S. Zemel:
The Helmholtz machine. Neural Comput. 7(5): 889-904 (1995) - [c32]Sidney S. Fels, Geoffrey E. Hinton:
GloveTalkII: An Adaptive Gesture-to-Formant Interface. CHI 1995: 456-463 - [c31]Geoffrey E. Hinton, Michael Revow:
Using Pairs of Data-Points to Define Splits for Decision Trees. NIPS 1995: 507-513 - [c30]Brendan J. Frey, Geoffrey E. Hinton, Peter Dayan:
Does the Wake-sleep Algorithm Produce Good Density Estimators? NIPS 1995: 661-667 - 1994
- [c29]Lei Xu, Michael I. Jordan, Geoffrey E. Hinton:
An Alternative Model for Mixtures of Experts. NIPS 1994: 633-640 - [c28]Sidney S. Fels, Geoffrey E. Hinton:
Glove-TalkII: Mapping Hand Gestures to Speech Using Neural Networks. NIPS 1994: 843-850 - [c27]Christopher K. I. Williams, Michael Revow, Geoffrey E. Hinton:
Using a neural net to instantiate a deformable model. NIPS 1994: 965-972 - [c26]Geoffrey E. Hinton, Michael Revow, Peter Dayan:
Recognizing Handwritten Digits Using Mixtures of Linear Models. NIPS 1994: 1015-1022 - 1993
- [j17]Suzanna Becker, Geoffrey E. Hinton:
Learning Mixture Models of Spatial Coherence. Neural Comput. 5(2): 267-277 (1993) - [j16]Steven J. Nowlan, Geoffrey E. Hinton:
A soft decision-directed LMS algorithm for blind equalization. IEEE Trans. Commun. 41(2): 275-279 (1993) - [j15]Sidney S. Fels, Geoffrey E. Hinton:
Glove-Talk: a neural network interface between a data-glove and a speech synthesizer. IEEE Trans. Neural Networks 4(1): 2-8 (1993) - [c25]Geoffrey E. Hinton, Drew van Camp:
Keeping the Neural Networks Simple by Minimizing the Description Length of the Weights. COLT 1993: 5-13 - [c24]Geoffrey E. Hinton, Richard S. Zemel:
Autoencoders, Minimum Description Length and Helmholtz Free Energy. NIPS 1993: 3-10 - [c23]Richard S. Zemel, Geoffrey E. Hinton:
Developing Population Codes by Minimizing Description Length. NIPS 1993: 11-18 - 1992
- [j14]Steven J. Nowlan, Geoffrey E. Hinton:
Simplifying Neural Networks by Soft Weight-Sharing. Neural Comput. 4(4): 473-493 (1992) - [c22]Peter Dayan, Geoffrey E. Hinton:
Feudal Reinforcement Learning. NIPS 1992: 271-278 - 1991
- [j13]Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan, Geoffrey E. Hinton:
Adaptive Mixtures of Local Experts. Neural Comput. 3(1): 79-87 (1991) - [c21]Suzanna Becker, Geoffrey E. Hinton:
Learning to Make Coherent Predictions in Domains with Discontinuities. NIPS 1991: 372-379 - [c20]Geoffrey E. Hinton, Christopher K. I. Williams, Michael Revow:
Adaptive Elastic Models for Hand-Printed Character Recognition. NIPS 1991: 512-519 - [c19]Steven J. Nowlan, Geoffrey E. Hinton:
Adaptive Soft Weight Tying using Gaussian Mixtures. NIPS 1991: 993-1000 - 1990
- [j12]Geoffrey E. Hinton:
Connectionist Symbol Processing - Preface. Artif. Intell. 46(1-2): 1-4 (1990) - [j11]Geoffrey E. Hinton:
Mapping Part-Whole Hierarchies into Connectionist Networks. Artif. Intell. 46(1-2): 47-75 (1990) - [j10]Geoffrey E. Hinton, Steven J. Nowlan:
The Bootstrap Widrow-Hoff Rule as a Cluster-Formation Algorithm. Neural Comput. 2(3): 355-362 (1990) - [j9]Kevin J. Lang, Alex Waibel, Geoffrey E. Hinton:
A time-delay neural network architecture for isolated word recognition. Neural Networks 3(1): 23-43 (1990) - [c18]Sidney S. Fels, Geoffrey E. Hinton:
Building adaptive interfaces with neural networks: The glove-talk pilot study. INTERACT 1990: 683-688 - [c17]Richard S. Zemel, Geoffrey E. Hinton:
Discovering Viewpoint-Invariant Relationships That Characterize Objects. NIPS 1990: 299-305 - [c16]Steven J. Nowlan, Geoffrey E. Hinton:
Evaluation of Adaptive Mixtures of Competing Experts. NIPS 1990: 774-780 - [p1]Geoffrey E. Hinton, James L. McClelland, David E. Rumelhart:
Distributed Representations. The Philosophy of Artificial Intelligence 1990: 248-280
1980 – 1989
- 1989
- [j8]Geoffrey E. Hinton:
Connectionist Learning Procedures. Artif. Intell. 40(1-3): 185-234 (1989) - [j7]Geoffrey E. Hinton:
Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space. Neural Comput. 1(1): 143-150 (1989) - [j6]Alexander Waibel, Toshiyuki Hanazawa, Geoffrey E. Hinton, Kiyohiro Shikano, Kevin J. Lang:
Phoneme recognition using time-delay neural networks. IEEE Trans. Acoust. Speech Signal Process. 37(3): 328-339 (1989) - [c15]Kevin J. Lang, Geoffrey E. Hinton:
Dimensionality Reduction and Prior Knowledge in E-Set Recognition. NIPS 1989: 178-185 - [c14]Richard S. Zemel, Michael Mozer, Geoffrey E. Hinton:
TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations. NIPS 1989: 266-273 - [c13]Conrad C. Galland, Geoffrey E. Hinton:
Discovering High Order Features with Mean Field Modules. NIPS 1989: 509-515 - 1988
- [j5]David S. Touretzky, Geoffrey E. Hinton:
A Distributed Connectionist Production System. Cogn. Sci. 12(3): 423-466 (1988) - [c12]Alex Waibel, Toshiyuki Hanazawa, Geoffrey E. Hinton, Kiyohiro Shikano, Kevin J. Lang:
Phoneme recognition: neural networks vs. hidden Markov models. ICASSP 1988: 107-110 - [c11]Yann LeCun, Conrad C. Galland, Geoffrey E. Hinton:
GEMINI: Gradient Estimation Through Matrix Inversion After Noise Injection. NIPS 1988: 141-148 - 1987
- [j4]Geoffrey E. Hinton, Steven J. Nowlan:
How Learning Can Guide Evolution. Complex Syst. 1(3) (1987) - [j3]Scott E. Fahlman, Geoffrey E. Hinton:
Connectionist Architectures for Artificial Intelligence. Computer 20(1): 100-109 (1987) - [c10]Geoffrey E. Hinton, James L. McClelland:
Learning Representations by Recirculation. NIPS 1987: 358-366 - [c9]Geoffrey E. Hinton:
Learning Translation Invariant Recognition in Massively Parallel Networks. PARLE (1) 1987: 1-13 - 1986
- [c8]Drew V. McDermott, Geoffrey E. Hinton:
Learning in Massively Parallel Nets (Panel). AAAI 1986: 1149 - 1985
- [j2]David H. Ackley, Geoffrey E. Hinton, Terrence J. Sejnowski:
A Learning Algorithm for Boltzmann Machines. Cogn. Sci. 9(1): 147-169 (1985) - [c7]David S. Touretzky, Geoffrey E. Hinton:
Symbols Among the Neurons: Details of a Connectionist Inference Architecture. IJCAI 1985: 238-243 - [c6]Geoffrey E. Hinton, Kevin J. Lang:
Shape Recognition and Illusory Conjunctions. IJCAI 1985: 252-259 - 1983
- [c5]Scott E. Fahlman, Geoffrey E. Hinton, Terrence J. Sejnowski:
Massively Parallel Architectures for AI: NETL, Thistle, and Boltzmann Machines. AAAI 1983: 109-113 - 1981
- [c4]Geoffrey E. Hinton:
A Parallel Computation that Assigns Canonical Object-Based Frames of Reference. IJCAI 1981: 683-685 - [c3]Geoffrey E. Hinton:
Shape Representation in Parallel Systems. IJCAI 1981: 1088-1096
1970 – 1979
- 1979
- [j1]Geoffrey E. Hinton:
Some Demonstrations of the Effects of Structural Descriptions in Mental Imagery. Cogn. Sci. 3(3): 231-250 (1979) - 1978
- [c2]Aaron Sloman, David Owen, Geoffrey E. Hinton, Frank Birch, Frank O'Gorman:
Representation and Control in Vision. AISB/GI (ECAI) 1978: 309-314 - 1977
- [b1]Geoffrey E. Hinton:
Relaxation and its role in vision. University of Edinburgh, UK, 1977 - 1976
- [c1]Geoffrey E. Hinton:
Using Relaxation to find a Puppet. AISB (ECAI) 1976: 148-157
Coauthor Index
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