1967-2006, Conditional Restricted Boltzmann Machines for Structured Output Prediction, Volodymyr Mnih, Hugo Larochelle, Geoffrey E. machines, Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine, George E. Dahl, Marc'Aurelio Ranzato, Abdel-rahman Mohamed, Geoffrey E. Hinton, Phone recognition using Restricted Boltzmann Machines, Rectified Linear Units Improve Restricted Boltzmann Machines, Temporal-Kernel Recurrent Neural Networks, Neural Networks, vol. 132-136, Comparing Classification Methods for Longitudinal fMRI Studies, Tanya Schmah, Grigori Yourganov, Richard S. Zemel, Geoffrey E. Hinton, Steven L. Small, Stephen C. 1078-1101, Discovering Multiple Constraints that are Frequently Approximately Satisfied, Improving deep neural networks for LVCSR using rectified linear units and dropout, George E. Dahl, Tara N. Sainath, Geoffrey E. Hinton, Modeling Documents with Deep Boltzmann Machines, Nitish Srivastava, Ruslan Salakhutdinov, Geoffrey E. Hinton, Marc'Aurelio Ranzato, Volodymyr Mnih, Joshua M. Susskind, Geoffrey E. Hinton, IEEE Trans. 1235-1260, Geoffrey E. Hinton, Max Welling, Andriy Classification, Melody Y. Guan, Varun He was one of the researchers who introduced the back-propagation algorithm and the first to use backpropagation for learning word embeddings. Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada, and the Association for the Advancement of Artificial Intelligence. foreign member of the American Academy of Arts and Sciences and the National 4 (1993), pp. He did postdoctoral work at Sussex University and the University of California San Diego and spent five years as a faculty member in the Computer Science department at Carnegie-Mellon University. prize for Engineering (2012) , The IEEE James Clerk Maxwell Gold medal (2016), and 23 (2010), pp. Intell., vol. 113 (2015), pp. 232-244, Learning Hierarchical Structures with Linear Relational Embedding, Relative Density Nets: A New Way to Combine Backpropagation with HMM's, Extracting Distributed Representations of Concepts and Relations from Positive 9 (1997), pp. as a faculty member in the Computer Science department at Carnegie-Mellon University. M. Neal, Richard S. Zemel, Neural Computation, vol. learning procedure that is efficient at finding complex structure in large, Frosst, Who said what: Modeling individual labelers He did postdoctoral work at Sussex University and the University of California San Diego and spent five years as a faculty member in the Computer Science department at Carnegie-Mellon University. 267-277, Simplifying Neural Networks by Soft Weight-Sharing, Neural Computation, vol. 1 (1989), pp. 37 (1989), pp. He is an honorary foreign member of the American Academy of Arts and Sciences and the National Academy of Engineering, and a former president of the Cognitive Science Society. Confident Output Distributions, Gabriel Pereyra, George Tucker, Jan 231-250, Aaron Sloman, David Owen, Geoffrey E. Geoffrey E. Hinton Google Brain Toronto {sasabour, frosst, geoffhinton}@google.com Abstract A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or an object part. The following articles are merged in Scholar. Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. He was awarded the first David E. Rumelhart prize (2001), the IJCAI award for research excellence (2005), the Killam prize for Engineering (2012) , The IEEE James Clerk Maxwell Gold medal (2016), and the NSERC Herzberg Gold Medal (2010) which is Canada's top award in Science and Engineering. Neural Networks, vol. He spent five years as a faculty member at Carnegie Mellon University, Pittsburgh, Pennsylvania, and he is currently a Distinguished Professor at the University of Toronto and a Distinguished Researcher at Google. 79-87, Adaptive Soft Weight Tying using Gaussian Mixtures, Learning to Make Coherent Predictions in Domains with Discontinuities, A time-delay neural network architecture for isolated word recognition, Kevin J. Lang, Alex Waibel, Geoffrey E. 267-269, Dynamical binary latent variable models for 3D human pose tracking, Graham W. Taylor, Leonid Sigal, David J. Unpublished manuscript, 2010. 275-279, Autoencoders, Minimum Description Length and Helmholtz Free Energy, Developing Population Codes by Minimizing Description Length, Glove-Talk: a neural network interface between a data-glove and a speech Their combined citations are counted only for ... Geoffrey Hinton Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google Verified email at cs.toronto.edu. Hinton, Learning a better representation of speech soundwaves using restricted boltzmann University College London and then returned to the University of Toronto where he is E. Hinton, Michael A. Picheny, Deep belief nets for natural language call-routing, Ruhi Sarikaya, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov, Introduction to the Special Section on Deep Learning for Speech and Language 13 (2001), pp. Efficient representation of articulated objects such as human bodies is an important problem in computer vision and graphics. 38 (2014), pp. Mach. Lang, IEEE Trans. Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google - Cited by 397,700 - machine learning - psychology - artificial intelligence - cognitive science - computer science Koray Kavukcuoglu, Geoffrey E. Hinton, Using Fast Weights to Attend to the Recent Past, Jimmy Ba, Geoffrey Hinton, Volodymyr for Google in Mountain View and Toronto. Add co-authors Co-authors. Gulshan, Andrew M. Dai, Geoffrey Hinton, Attend, Infer, Repeat: Fast Scene Understanding Data Eng., vol. K. Yang, Q.V. 1-2, Autoregressive Product of Multi-frame Predictions 73-81, Neural Networks, vol. 2-8, Keeping the Neural Networks Simple by Minimizing the Description Length of the Report Missing or Incorrect Information. Neural Networks, vol. now an emeritus distinguished professor. 11 (1999), pp. E. Hinton, Using an autoencoder with deformable templates to discover features for automated Hinton, The Recurrent Temporal Restricted Boltzmann Machine, Ilya Sutskever, Geoffrey E. Hinton, Top Conferences. Task, Variational Learning for Switching State-Space Models, Neural Computation, vol. Fleet, Geoffrey E. Hinton, Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images, Marc'Aurelio Ranzato, Alex Krizhevsky, Geoffrey E. Hinton, Roland Memisevic, Christopher Zach, Geoffrey Embedding, IEEE Trans. Hinton, Machine Learning, vol. 1025-1068, Using very deep autoencoders for content-based image retrieval, Binary coding of speech spectrograms using a deep auto-encoder, Li Deng, Michael L. Seltzer, Dong Yu, Alex Acero, Abdel-rahman Mohamed, Geoffrey E. Hinton, Encyclopedia of Machine Learning (2010), pp. Convolutional deep belief networks on cifar-10. through online distillation, Rohan Anil, Gabriel Pereyra, Alexandre Tachard Passos, Robert Ormandi, 969-978, Using fast weights to improve persistent contrastive divergence, Workshop summary: Workshop on learning feature hierarchies, Kai Yu, Ruslan Salakhutdinov, Yann LeCun, Geoffrey E. Hinton, Yoshua Bengio, Zero-shot Learning with Semantic Output Codes, Mark Palatucci, Dean Pomerleau, Geoffrey E. the Association for the Advancement of Artificial Intelligence. 7 (1995), pp. Revow, IEEE Trans. All Conferences. Dudek, Neural Computation, vol. 423-466, GEMINI: Gradient Estimation Through Matrix Inversion After Noise Injection, Yann LeCun, Conrad C. Galland, Geoffrey E. first to use backpropagation for learning word embeddings. Whye Teh, Neural Computation, vol. Top 1000 … Mnih, Joel Z. Leibo, Catalin Ionescu, A Simple Way to Initialize Recurrent Networks of Top Conferences. Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada, and Source Model, Glove-talk II - a neural-network interface which maps gestures to parallel object classification. improves classification, Melody Guan, Varun Terrence J. Sejnowski, A Parallel Computation that Assigns Canonical Object-Based Frames of Reference, Some Demonstrations of the Effects of Structural Descriptions in Mental Imagery, Cognitive Science, vol. with Generative Models, S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Godfather of artificial intelligence Geoffrey Hinton gives an overview of the foundations of deep learning. was one of the researchers who introduced the back-propagation algorithm and the 3 (1990), pp. Their combined citations are counted only for the first article. 68 (1997), pp. 24 (2002), pp. Neural Networks, vol. Forum, vol. time-delay neural nets, mixtures of experts, variational learning, products of 120-126, Modeling the manifolds of images of handwritten digits, Geoffrey E. Hinton, Peter Dayan, Michael 26 (2000), pp. Rectified Linear Units, Quoc V. Le, Navdeep Jaitly, Geoffrey E. Hinton, Distilling the Knowledge in a Neural Network, Geoffrey Hinton, Oriol Vinyals, Jeffrey 9 (1997), pp. 778-784, Dropout: a simple way to prevent neural networks from overfitting, Nitish Srivastava, Geoffrey E. Hinton, 25-33, Fast Neural Network Emulation of Dynamical Systems for Computer Animation, Radek Grzeszczuk, Demetri Terzopoulos, Geoffrey E. Hinton, Glove-TalkII-a neural-network interface which maps gestures to parallel formant Try different keywords or filters. to neural network research include Boltzmann machines, distributed representations, Gerald Penn, Visualizing non-metric similarities in multiple maps, Laurens van der Maaten, Geoffrey E. nature 521 (7553), 436-444, 2015. 1473-1492, Learning to combine foveal glimpses with a third-order Boltzmann machine, Modeling pixel means and covariances using factorized third-order boltzmann Deoras, IEEE/ACM Trans. His other contributions Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara Sainath, Brian Kingsbury, Efficient Parametric Projection Pursuit Density Estimation, Max Welling, Richard S. Zemel, Geoffrey E. In ESANN, 2011. Report Missing or Incorrect Information. S. Zemel, Steven L. Small, Stephen C. Strother, Implicit Mixtures of Restricted Boltzmann Machines, Improving a statistical language model by modulating the effects of context words, Zhang Yuecheng, Andriy Mnih, Geoffrey E. J. Approx. Dean, NIPS Deep Learning and Representation Learning Workshop (2015), Oriol Vinyals, Lukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, Geoffrey Hinton, Marc'Aurelio Ranzato, Geoffrey E. Hinton, 33-55, A better way to learn features: technical perspective, Volodymyr Mnih, Hugo Larochelle, Geoffrey E. Hinton, Deep Belief Networks using discriminative features for phone recognition, Abdel-rahman Mohamed, Tara N. Sainath, Chorowski, Łukasz Kaiser, Geoffrey Hinton, Who Said What: Modelling Individual Labels Improves Le, P. Nguyen, A. Audio, Speech & Language Processing, vol. experts and deep belief nets. 133-140, Using Free Energies to Represent Q-values in a Multiagent Reinforcement Learning 12 (2000), pp. 12 (2000), pp. Large scale distributed neural network training 18 (2005), pp. Zeiler, M. Ranzato, R. Monga, M. Mao, Since 2013 he has been working half-time for Google in Mountain View and Toronto. He is an honorary 2 (1990), pp. Dean, G.E. Top 1000 … Audio, Speech & Language Processing, vol. High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a small central layer to reconstruct high-dimensional input vectors. Knowl. 1385-1403. Linear Space, Modeling High-Dimensional Data by Combining Simple Experts, Rate-coded Restricted Boltzmann Machines for Face Recognition, Recognizing Hand-written Digits Using Hierarchical Products of Experts, Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton, Neural Computation, vol. 4 (2003), pp. 15 (2004), pp. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, Alex Krizhevsky, Ilya Sutskever, Geoffrey E. 473-493, Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan, Geoffrey E. Hinton, Neural Computation, vol. 35 (2013), pp. Sumit Chopra Imagen Technologies ... Y LeCun, Y Bengio, G Hinton. 4-6, Learning to Label Aerial Images from Noisy Data, Products of Hidden Markov Models: It Takes N>1 to Tango, Robust Boltzmann Machines for recognition and denoising, Understanding how Deep Belief Networks perform acoustic modelling, Abdel-rahman Mohamed, Geoffrey E. Hinton, 12 (2011), pp. Hinton, Improving neural networks by preventing co-adaptation of feature detectors, Geoffrey E. Hinton, Nitish Srivastava, 22 (2014), pp. 337-346, Recognizing Handwritten Digits Using Hierarchical Products of Experts, IEEE Trans. Processing, Dong Yu, Geoffrey E. Hinton, Nelson 14 (2002), pp. In this Viewpoint, Geoffrey Hinton of Google’s Brain Team discusses the basics of neural networks: their underlying data structures, how they can be trained and combined to process complex health data sets, and future prospects for harnessing their unsupervised learning to clinical challenges. Since 2013 he has been working half-time for Google in Mountain View and Toronto. Osindero, Local Physical Models for Interactive Character Animation, Comput. formant speech synthesizer controls, IEEE Trans. ///countCtrl.countPageResults("of")/// publications. What kind of graphical model is the brain? Bao, Miguel Á. Carreira-Perpiñán, Geoffrey Brendan J. Frey, Geoffrey E. Hinton, Yee Whye Teh, Variational Learning in Nonlinear Gaussian Belief Networks, Neural Computation, vol. He then became a fellow of the Canadian Institute for Advanced Research and moved to the Department of Computer Science at the University of Toronto. Pattern Anal. 3 (1979), pp. 889-904, Using Pairs of Data-Points to Define Splits for Decision Trees, An Alternative Model for Mixtures of Experts, Lei Xu 0001, Michael I. Jordan, Geoffrey E. 2109-2128, Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates, VLSI Signal Processing, vol. 30 (2006), pp. David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams 13a. Geoffrey Hinton University of Toronto Canada: G2R World Ranking 13th. Canadian Institute for Advanced Research.