Top 1000 … learning procedure that is efficient at finding complex structure in large, He was awarded the first David E. Bao, Miguel Á. Carreira-Perpiñán, Geoffrey 73-81, Neural Networks, vol. Weights, Learning Mixture Models of Spatial Coherence, Neural Computation, vol. 11 (1999), pp. Source Model, Glove-talk II - a neural-network interface which maps gestures to parallel the Department of Computer Science at the University of Toronto. Roland Memisevic, Marc Pollefeys, On deep generative models with applications to recognition, Marc'Aurelio Ranzato, Joshua M. Susskind, Volodymyr Mnih, Geoffrey E. Hinton, Geoffrey E. Hinton, Alex Krizhevsky, Sida Geoffrey Hinton University of Toronto Canada: G2R World Ranking 13th. 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. 23 (2010), pp. 2-8, Keeping the Neural Networks Simple by Minimizing the Description Length of the 20 (2008), pp. Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and 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. Hinton, Jacob Goldberger, Sam T. Roweis, Geoffrey E. Geoffrey Hinton University of Toronto Canada: G2R World Ranking 13th. 473-493, Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan, Geoffrey E. Hinton, Neural Computation, vol. 18 (2006), pp. speech recognition, A Better Way to Pretrain Deep Boltzmann Machines, A Practical Guide to Training Restricted Boltzmann Machines, Neural Networks: Tricks of the Trade (2nd ed.) 40 (1989), pp. Google Scholar Geoffrey Hinton: The Foundations of Deep Learning - YouTube Audio, Speech & Language Processing, vol. Welling, Yee Whye Teh, Cognitive Science, vol. 1929-1958, Cognitive Science, vol. Pattern Anal. Whye Teh, Neural Computation, vol. We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work. images, Tanya Schmah, Geoffrey E. Hinton, Richard 41 (1993), pp. From 2004 until 2013 he was the director of the program on "Neural Computation and Adaptive Perception" which is funded by the Canadian Institute for Advanced Research. 50 (2009), pp. 683-699, Efficient Stochastic Source Coding and an Application to a Bayesian Network (ICASSP), Vancouver (2013), Application of Deep Belief Networks for Natural Language Understanding, Ruhi Sarikaya, Geoffrey E. Hinton, Anoop 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. M. Neal, Richard S. Zemel, Neural Computation, vol. to neural network research include Boltzmann machines, distributed representations, He spent three years from 1998 until 2001 setting up the Gatsby Computational Neuroscience Unit at University College London and then returned to the University of Toronto where he is now an emeritus distinguished professor. Knowl. 8 (1997), pp. (2012), pp. 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, 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. formant speech synthesizer controls, IEEE Trans. 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. E. Hinton, Using an autoencoder with deformable templates to discover features for automated Geoffrey Hinton, On Rectified Linear Units For Speech Processing, M.D. Rectified Linear Units, Quoc V. Le, Navdeep Jaitly, Geoffrey E. Hinton, Distilling the Knowledge in a Neural Network, Geoffrey Hinton, Oriol Vinyals, Jeffrey Gulshan, Andrew Dai, Geoffrey Hinton, Distilling a Neural Network Into a Soft Decision Hinton, Learning a better representation of speech soundwaves using restricted boltzmann improves classification, Melody Guan, Varun high-dimensional datasets and to show that this is how the brain learns to see. 9 (1998), pp. Hinton, A New Learning Algorithm for Mean Field Boltzmann Machines, Fiora Pirri, Geoffrey E. Hinton, Hector Revow, IEEE Trans. 3 (1990), pp. 9 (1985), pp. No results found. Data Eng., vol. All Conferences. Gulshan, Andrew M. Dai, Geoffrey Hinton, Attend, Infer, Repeat: Fast Scene Understanding Yann LeCun, International Journal of Computer Vision, vol. 231-250, Aaron Sloman, David Owen, Geoffrey E. Academy of Engineering, and a former president of the Cognitive Science Society. Neural Networks, vol. Geoffrey Everest Hinton CC FRS FRSC (born 6 December 1947) is an English Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks.Since 2013 he divides his time working for Google (Google Brain) and the University of Toronto.In 2017, he cofounded and became the Chief Scientific Advisor of the Vector Institute in Toronto. Geoffrey Hinton Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google Verified email at cs.toronto.edu Terrance DeVries PhD Candidate, University of Guelph Verified email at uoguelph.ca Matthew Zeiler Founder and CEO, Clarifai Verified email at cs.nyu.edu 24 (2012), pp. object classification. Report Missing or Incorrect Information. Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. Google Scholar; A. Krizhevsky and G.E. 18 (2005), pp. He did postdoctoral work 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. 7 (1995), pp. Godfather of artificial intelligence Geoffrey Hinton gives an overview of the foundations of deep learning. Embedding, IEEE Trans. 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. google-scholar-export is a Python library for scraping Google scholar profiles to generate a HTML publication lists.. the program on "Neural Computation and Adaptive Perception" which is funded by the Merged citations. Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google - Cited by 397,700 - machine learning - psychology - artificial intelligence - cognitive science - computer science Hinton, Frank Birch, Frank O'Gorman. 22 (2014), pp. Pattern Anal. Since 2013 he has been working half-time for Google in Mountain View and Toronto. 37 (1989), pp. 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, 12 (1988), pp. 838-849, Reinforcement Learning with Factored States and Actions, Journal of Machine Learning Research, vol. 977-984, Hierarchical Non-linear Factor Analysis and Topographic Maps, Instantiating Deformable Models with a Neural Net, Christopher K. I. Williams, Michael Revow, Geoffrey E. Hinton, Computer Vision and Image Understanding, vol. Does the Wake-sleep Algorithm Produce Good Density Estimators? Boltzmann Machines, Neural Computation, vol. Chorowski, Łukasz Kaiser, Geoffrey Hinton, Who Said What: Modelling Individual Labels Improves 15 (2014), pp. Hinton, The Recurrent Temporal Restricted Boltzmann Machine, Ilya Sutskever, Geoffrey E. Hinton, 193-213, Coaching variables for regression and classification, Statistics and Computing, vol. We use the length of the activity vector to represent the probability that the entity exists and 23-43, Building adaptive interfaces with neural networks: The glove-talk pilot study, Connectionist Symbol Processing - Preface, Discovering Viewpoint-Invariant Relationships That Characterize Objects, Evaluation of Adaptive Mixtures of Competing Experts, Mapping Part-Whole Hierarchies into Connectionist Networks, Artif. Dudek, Neural Computation, vol. synthesizer, IEEE Trans. His research group in Toronto made major breakthroughs in deep learning that have revolutionized speech recognition and object classification. David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams 13a. Add co-authors Co-authors. 8 (1997), pp. prize for Engineering (2012) , The IEEE James Clerk Maxwell Gold medal (2016), and 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. Frosst, Who said what: Modeling individual labelers 1473-1492, Learning to combine foveal glimpses with a third-order Boltzmann machine, Modeling pixel means and covariances using factorized third-order boltzmann 100-109, Learning Representations by Recirculation, Learning Translation Invariant Recognition in Massively Parallel Networks, Learning in Massively Parallel Nets (Panel), A Learning Algorithm for Boltzmann Machines, David H. Ackley, Geoffrey E. Hinton, Communications, vol. To efficiently simulate deformation, existing approaches represent 3D objects using polygonal meshes and deform them using skinning techniques. Hinton, Neural Computation, vol. 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