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Author Information
Mark M Palatucci (Apple)
Dean Pomerleau (Intel Labs)
Geoffrey E Hinton (Google & University of Toronto)
Geoffrey Hinton received his PhD in Artificial Intelligence from Edinburgh in 1978 and spent five years as a faculty member at Carnegie-Mellon where he pioneered back-propagation, Boltzmann machines and distributed representations of words. In 1987 he became a fellow of the Canadian Institute for Advanced Research and moved to the University of Toronto. In 1998 he founded the Gatsby Computational Neuroscience Unit at University College London, returning to the University of Toronto in 2001. His group at the University of Toronto then used deep learning to change the way speech recognition and object recognition are done. He currently splits his time between the University of Toronto and Google. In 2010 he received the NSERC Herzberg Gold Medal, Canada's top award in Science and Engineering.
Tom Mitchell (Carnegie Mellon University)
More from the Same Authors
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2021 Spotlight: Neural Additive Models: Interpretable Machine Learning with Neural Nets »
Rishabh Agarwal · Levi Melnick · Nicholas Frosst · Xuezhou Zhang · Ben Lengerich · Rich Caruana · Geoffrey Hinton -
2022 Invited Talk: The Forward-Forward Algorithm for Training Deep Neural Networks »
Geoffrey Hinton -
2022 Poster: A Unified Sequence Interface for Vision Tasks »
Ting Chen · Saurabh Saxena · Lala Li · Tsung-Yi Lin · David Fleet · Geoffrey Hinton -
2021 Poster: Canonical Capsules: Self-Supervised Capsules in Canonical Pose »
Weiwei Sun · Andrea Tagliasacchi · Boyang Deng · Sara Sabour · Soroosh Yazdani · Geoffrey Hinton · Kwang Moo Yi -
2021 Poster: Neural Additive Models: Interpretable Machine Learning with Neural Nets »
Rishabh Agarwal · Levi Melnick · Nicholas Frosst · Xuezhou Zhang · Ben Lengerich · Rich Caruana · Geoffrey Hinton -
2020 : Q & A and Panel Session with Tom Mitchell, Jenn Wortman Vaughan, Sanjoy Dasgupta, and Finale Doshi-Velez »
Tom Mitchell · Jennifer Wortman Vaughan · Sanjoy Dasgupta · Finale Doshi-Velez · Zachary Lipton -
2020 Poster: Big Self-Supervised Models are Strong Semi-Supervised Learners »
Ting Chen · Simon Kornblith · Kevin Swersky · Mohammad Norouzi · Geoffrey E Hinton -
2019 : Tom Mitchell - Understanding Neural Processes: Getting Beyond Where and When, to How »
Tom Mitchell -
2019 : Tom Mitchell »
Tom M Mitchell -
2019 Poster: Lookahead Optimizer: k steps forward, 1 step back »
Michael Zhang · James Lucas · Jimmy Ba · Geoffrey E Hinton -
2019 Poster: Stacked Capsule Autoencoders »
Adam Kosiorek · Sara Sabour · Yee Whye Teh · Geoffrey E Hinton -
2019 Poster: Learning Data Manipulation for Augmentation and Weighting »
Zhiting Hu · Bowen Tan · Russ Salakhutdinov · Tom Mitchell · Eric Xing -
2019 Poster: When does label smoothing help? »
Rafael Müller · Simon Kornblith · Geoffrey E Hinton -
2019 Spotlight: When does label smoothing help? »
Rafael Müller · Simon Kornblith · Geoffrey E Hinton -
2019 Poster: Game Design for Eliciting Distinguishable Behavior »
Fan Yang · Liu Leqi · Yifan Wu · Zachary Lipton · Pradeep Ravikumar · Tom M Mitchell · William Cohen -
2018 Workshop: Learning by Instruction »
Shashank Srivastava · Igor Labutov · Bishan Yang · Amos Azaria · Tom Mitchell -
2018 Poster: Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures »
Sergey Bartunov · Adam Santoro · Blake Richards · Luke Marris · Geoffrey E Hinton · Timothy Lillicrap -
2018 Poster: Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems »
Mrinmaya Sachan · Kumar Avinava Dubey · Tom Mitchell · Dan Roth · Eric Xing -
2017 : Invited Talk: Learning from Limited Labeled Data (But a Lot of Unlabeled Data) »
Tom Mitchell -
2017 : NELL: Lessons and Future Directions »
Tom Mitchell -
2017 Poster: Dynamic Routing Between Capsules »
Sara Sabour · Nicholas Frosst · Geoffrey E Hinton -
2017 Spotlight: Dynamic Routing Between Capsules »
Sara Sabour · Nicholas Frosst · Geoffrey E Hinton -
2017 Poster: Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach »
Emmanouil Platanios · Hoifung Poon · Tom M Mitchell · Eric Horvitz -
2016 Poster: 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 -
2016 Poster: Using Fast Weights to Attend to the Recent Past »
Jimmy Ba · Geoffrey E Hinton · Volodymyr Mnih · Joel Leibo · Catalin Ionescu -
2016 Oral: Using Fast Weights to Attend to the Recent Past »
Jimmy Ba · Geoffrey E Hinton · Volodymyr Mnih · Joel Leibo · Catalin Ionescu -
2015 Poster: Grammar as a Foreign Language »
Oriol Vinyals · Łukasz Kaiser · Terry Koo · Slav Petrov · Ilya Sutskever · Geoffrey Hinton -
2015 Tutorial: Deep Learning »
Geoffrey E Hinton · Yoshua Bengio · Yann LeCun -
2014 Workshop: 4th Workshop on Automated Knowledge Base Construction (AKBC) »
Sameer Singh · Fabian M Suchanek · Sebastian Riedel · Partha Pratim Talukdar · Kevin Murphy · Christopher Ré · William Cohen · Tom Mitchell · Andrew McCallum · Jason E Weston · Ramanathan Guha · Boyan Onyshkevych · Hoifung Poon · Oren Etzioni · Ari Kobren · Arvind Neelakantan · Peter Clark -
2014 Workshop: Deep Learning and Representation Learning »
Andrew Y Ng · Yoshua Bengio · Adam Coates · Roland Memisevic · Sharanyan Chetlur · Geoffrey E Hinton · Shamim Nemati · Bryan Catanzaro · Surya Ganguli · Herbert Jaeger · Phil Blunsom · Leon Bottou · Volodymyr Mnih · Chen-Yu Lee · Rich M Schwartz -
2012 Poster: ImageNet Classification with Deep Convolutional Neural Networks »
Alex Krizhevsky · Ilya Sutskever · Geoffrey E Hinton -
2012 Invited Talk: Dropout: A simple and effective way to improve neural networks »
Geoffrey E Hinton · George Dahl -
2012 Poster: A Better Way to Pre-Train Deep Boltzmann Machines »
Russ Salakhutdinov · Geoffrey E Hinton -
2012 Spotlight: ImageNet Classification with Deep Convolutional Neural Networks »
Alex Krizhevsky · Ilya Sutskever · Geoffrey E Hinton -
2010 Workshop: Deep Learning and Unsupervised Feature Learning »
Honglak Lee · Marc'Aurelio Ranzato · Yoshua Bengio · Geoffrey E Hinton · Yann LeCun · Andrew Y Ng -
2010 Talk: A Probabilistic Approach to Data Visualization »
Geoffrey E Hinton -
2010 Oral: Learning to combine foveal glimpses with a third-order Boltzmann machine »
Hugo Larochelle · Geoffrey E Hinton -
2010 Poster: Learning to combine foveal glimpses with a third-order Boltzmann machine »
Hugo Larochelle · Geoffrey E Hinton -
2010 Poster: Generating more realistic images using gated MRF's »
Marc'Aurelio Ranzato · Volodymyr Mnih · Geoffrey E Hinton -
2010 Poster: Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine »
George Dahl · Marc'Aurelio Ranzato · Abdel-rahman Mohamed · Geoffrey E Hinton -
2010 Poster: Gated Softmax Classification »
Roland Memisevic · Christopher Zach · Geoffrey E Hinton · Marc Pollefeys -
2009 Workshop: Deep Learning for Speech Recognition and Related Applications »
Li Deng · Dong Yu · Geoffrey E Hinton -
2009 Poster: Replicated Softmax: an Undirected Topic Model »
Russ Salakhutdinov · Geoffrey E Hinton -
2009 Poster: 3D Object Recognition with Deep Belief Nets »
Vinod Nair · Geoffrey E Hinton -
2009 Spotlight: 3D Object Recognition with Deep Belief Nets »
Vinod Nair · Geoffrey E Hinton -
2009 Invited Talk: Deep Learning with Multiplicative Interactions »
Geoffrey E Hinton -
2008 Workshop: Parallel Implementations of Learning Algorithms: What have you done for me lately? »
Robert Thibadeau · Dan Hammerstrom · David S Touretzky · Tom Mitchell -
2008 Workshop: Parallel Implementations of Learning Algorithms: What have you done for me lately? »
Robert Thibadeau · David S Touretzky · Dan Hammerstrom · Tom Mitchell -
2008 Poster: Using matrices to model symbolic relationship »
Ilya Sutskever · Geoffrey E Hinton -
2008 Demonstration: Visualizing NIPS Cooperations using Multiple Maps t-SNE »
Laurens van der Maaten · Geoffrey E Hinton -
2008 Spotlight: Using matrices to model symbolic relationship »
Ilya Sutskever · Geoffrey E Hinton -
2008 Poster: The Recurrent Temporal Restricted Boltzmann Machine »
Ilya Sutskever · Geoffrey E Hinton · Graham Taylor -
2008 Poster: A Scalable Hierarchical Distributed Language Model »
Andriy Mnih · Geoffrey E Hinton -
2008 Poster: Implicit Mixtures of Restricted Boltzmann Machines »
Vinod Nair · Geoffrey E Hinton -
2008 Poster: Competing RBM density models for classification of fMRI images »
Tanya Schmah · Geoffrey E Hinton · Richard Zemel -
2007 Tutorial: Deep Belief Nets »
Geoffrey E Hinton -
2007 Poster: Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes »
Russ Salakhutdinov · Geoffrey E Hinton -
2007 Poster: Modeling image patches with a directed hierarchy of Markov random fields »
Simon Osindero · Geoffrey E Hinton -
2006 Workshop: New directions on decoding mental states from fMRI data »
John-Dylan Haynes · Tom Mitchell · Francisco Pereira -
2006 Poster: Modeling Human Motion Using Binary Latent Variables »
Graham Taylor · Geoffrey E Hinton · Sam T Roweis -
2006 Spotlight: Modeling Human Motion Using Binary Latent Variables »
Graham Taylor · Geoffrey E Hinton · Sam T Roweis