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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
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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)- [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 - 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]