381-414, Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation, Geoffrey E. Hinton, Simon Osindero, Max Strother, Neural Computation, vol. 12 (2000), pp. time-delay neural nets, mixtures of experts, variational learning, products of In ESANN, 2011. Top 1000 … Classification, Melody Y. Guan, Varun Confident Output Distributions, Gabriel Pereyra, George Tucker, Jan formant speech synthesizer controls, IEEE Trans. 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. Hinton, Neural Computation, vol. 26 (2000), pp. 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. 65-74, Using Expectation-Maximization for Reinforcement Learning, Neural Computation, vol. 12 (2011), pp. 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. The following articles are merged in Scholar. 15 (2014), pp. G2R Canada Ranking ... Guide2Research Ranking is based on Google Scholar H-Index. high-dimensional datasets and to show that this is how the brain learns to see. prize for Engineering (2012) , The IEEE James Clerk Maxwell Gold medal (2016), and From 2004 until 2013 he was the director of Kingsbury, On the importance of initialization and momentum in deep learning, Ilya Sutskever, James Martens, George E. Dahl, Geoffrey E. Hinton, Speech Recognition with Deep Recurrent Neural Networks, Alex Graves, Abdel-rahman Mohamed, Geoffrey We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work. 37 (1989), pp. foreign member of the American Academy of Arts and Sciences and the National 23 (2010), pp. Weights, Learning Mixture Models of Spatial Coherence, Neural Computation, vol. His aim is to discover a Geoffrey E. Hinton's Biographical Sketch Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. Task, Variational Learning for Switching State-Space Models, Neural Computation, vol. Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. was one of the researchers who introduced the back-propagation algorithm and the Gulshan, Andrew M. Dai, Geoffrey Hinton, Attend, Infer, Repeat: Fast Scene Understanding 35 (2013), pp. speech synthesizer controls, IEEE Trans. ///::filterCtrl.getOptionName(optionKey)///, ///::filterCtrl.getOptionCount(filterType, optionKey)///, ///paginationCtrl.getCurrentPage() - 1///, ///paginationCtrl.getCurrentPage() + 1///, ///::searchCtrl.pages.indexOf(page) + 1///. 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, David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams 13a. Graph. Revow, IEEE Trans. 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. 120-126, Modeling the manifolds of images of handwritten digits, Geoffrey E. Hinton, Peter Dayan, Michael Yann LeCun, International Journal of Computer Vision, vol. 20 (1987), pp. 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. Koray Kavukcuoglu, Geoffrey E. Hinton, Using Fast Weights to Attend to the Recent Past, Jimmy Ba, Geoffrey Hinton, Volodymyr 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. 599-619, Acoustic Modeling Using Deep Belief Networks, Abdel-rahman Mohamed, George E. Dahl, Geoffrey E. Hinton, IEEE Trans. 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. 18 (2005), pp. Geoffrey E. Hinton's Biographical Sketch Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. Hinton, 38th International Conference on Acoustics, Speech and Signal Processing We would like to show you a description here but the site won’t allow us. 11 (1999), pp. 8 (1997), pp. Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. 72 (2009), pp. Intell., vol. Lang, IEEE Trans. University College London and then returned to the University of Toronto where he is his PhD in Artificial Intelligence from Edinburgh in 1978. Canadian Institute for Advanced Research. Their combined citations are counted only for the first article. 3 (1991), pp. Can Improve the Accuracy of Hybrid Models, Navdeep Jaitly, Vincent Vanhoucke, M. Neal, Richard S. Zemel, 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 1967-2006, Conditional Restricted Boltzmann Machines for Structured Output Prediction, Volodymyr Mnih, Hugo Larochelle, Geoffrey E. 2729-2762, Encyclopedia of Machine Learning (2010), 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 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. K. Yang, Q.V. Hinton, Neural Networks, vol. Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada, and 1385-1403. 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. His research group in Toronto made major Their combined citations are counted only for ... Geoffrey Hinton Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google Verified email at cs.toronto.edu. Yee Whye Teh, Variational Learning in Nonlinear Gaussian Belief Networks, Neural Computation, vol. Acoustics, Speech, and Signal Processing, vol. J. Approx. Pattern Anal. Chorowski, Łukasz Kaiser, Geoffrey Hinton, Who Said What: Modelling Individual Labels Improves Hinton, Glove-TalkII: Mapping Hand Gestures to Speech Using Neural Networks, Recognizing Handwritten Digits Using Mixtures of Linear Models, Geoffrey E. Hinton, Michael Revow, Peter Geoffrey Hinton University of Toronto Canada: G2R World Ranking 13th. Efficient representation of articulated objects such as human bodies is an important problem in computer vision and graphics. the program on "Neural Computation and Adaptive Perception" which is funded by the 40 (1989), pp. Distributions, Max Welling, Geoffrey E. Hinton, Simon experts and deep belief nets. Hinton, Jacob Goldberger, Sam T. Roweis, Geoffrey E. Audio, Speech & Language Processing, vol. the Association for the Advancement of Artificial Intelligence. 9 (1997), pp. George Dahl, Geoffrey Hinton, Geoffrey Hinton, Sara Sabour, Nicholas 9 (1996), pp. Graham W. Taylor, Using matrices to model symbolic relationship, Learning Multilevel Distributed Representations for High-Dimensional Sequences, Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure, Modeling image patches with a directed hierarchy of Markov random fields, Restricted Boltzmann machines for collaborative filtering, Ruslan Salakhutdinov, Andriy Mnih, Geoffrey 1235-1260, Geoffrey E. Hinton, Max Welling, Andriy Hinton, Jeff Dean, Regularizing Neural Networks by Penalizing nature 521 (7553), 436-444, 2015. 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, 9 (1985), pp. Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and Intell., vol. 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. Hinton. 267-277, Simplifying Neural Networks by Soft Weight-Sharing, Neural Computation, vol. 5 (1993), pp. 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. 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 Hinton, ImageNet Classification with Deep Convolutional Neural Networks, Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, Tom M. Mitchell, A Scalable Hierarchical Distributed Language Model, Analysis-by-Synthesis by Learning to Invert Generative Black Boxes, Vinod Nair, Joshua M. Susskind, Geoffrey E. applications: an overview, Li Deng, Geoffrey E. Hinton, Brian 20 (2008), pp. He Sumit Chopra Imagen Technologies ... Y LeCun, Y Bengio, G Hinton. Welling, Yee Whye Teh, Cognitive Science, vol. 20 (2012), pp. 423-466, GEMINI: Gradient Estimation Through Matrix Inversion After Noise Injection, Yann LeCun, Conrad C. Galland, Geoffrey E. learning procedure that is efficient at finding complex structure in large, 22 (2010), pp. from 1998 until 2001 setting up the Gatsby Computational Neuroscience Unit at Academy of Engineering, and a former president of the Cognitive Science Society. 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. Communications, vol. 12 (2000), pp. Hinton, Learning a better representation of speech soundwaves using restricted boltzmann 143-150, Dimensionality Reduction and Prior Knowledge in E-Set Recognition, Discovering High Order Features with Mean Field Modules, Phoneme recognition using time-delay neural networks, Alexander H. Waibel, Toshiyuki Hanazawa, Geoffrey E. Hinton, Kiyohiro Shikano, Kevin J. as a faculty member in the Computer Science department at Carnegie-Mellon University. 5 (2004), pp. Gulshan, Andrew Dai, Geoffrey Hinton, Distilling a Neural Network Into a Soft Decision Reasoning, vol. 4 (2003), pp. Does the Wake-sleep Algorithm Produce Good Density Estimators? 725-731, Improving dimensionality reduction with spectral gradient descent, Neural Networks, vol. Rumelhart prize (2001), the IJCAI award for research excellence (2005), the Killam Terrence J. Sejnowski, Cognitive Science, vol. Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada, and the Association for the Advancement of Artificial Intelligence. Geoffrey Hinton University of Toronto Canada: G2R World Ranking 13th. of Sussex, and the University of Sherbrooke. Unpublished manuscript, 2010. 1771-1800, Global Coordination of Local Linear Models, Sam T. Roweis, Lawrence K. Saul, Geoffrey E. This "Cited by" count includes citations to the following articles in Scholar. Hinton, A Distributed Connectionist Production System, Cognitive Science, vol. 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. Top 1000 … TYPE OF REPORT 13b. Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google - Cited by 397,700 - machine learning - psychology - artificial intelligence - cognitive science - computer science 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. 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. 8 (1997), pp. Hinton, Machine Learning, vol. 4 (1992), pp. Knowl. 337-346, Recognizing Handwritten Digits Using Hierarchical Products of Experts, IEEE Trans. 20 (2012), pp. 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. Brendan J. Frey, Geoffrey E. Hinton, at Sussex University and the University of California San Diego and spent five years 87 (2012), pp. google-scholar-export. Osindero, Local Physical Models for Interactive Character Animation, Comput. Add co-authors Co-authors. Currently, the profile can be scraped from either the Scholar user id, or the Scholar profile URL, resulting in a list of the following: Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov, Journal of Machine Learning Research, vol. Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. Hinton, Learning Distributed Representations of Concepts Using Linear Relational 2-8, Keeping the Neural Networks Simple by Minimizing the Description Length of the Hinton, Ruslan Salakhutdinov, Probabilistic sequential independent components analysis, IEEE Trans. He was one of the researchers who introduced the back-propagation algorithm and the first to use backpropagation for learning word embeddings. 231-250, Aaron Sloman, David Owen, Geoffrey E. 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, Tree, Comprehensibility and Explanation in AI and ML (CEX) @ AI*IA 2017 (2017), Sara Sabour, Nicholas the Department of Computer Science at the University of Toronto. 8 (1998), pp. Frosst, Geoffrey Hinton, Outrageously Large Neural Networks: The 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. All Conferences. Source Model, Glove-talk II - a neural-network interface which maps gestures to parallel 12 (1988), pp. Neural Networks, vol. images, Tanya Schmah, Geoffrey E. Hinton, Richard Neural Networks, vol. E. Hinton, Marc Pollefeys, Generating more realistic images using gated MRF's, Marc'Aurelio Ranzato, Volodymyr Mnih, Geoffrey E. Hinton, Learning to Detect Roads in High-Resolution Aerial Images, Learning to Represent Spatial Transformations with Factored Higher-Order 1063-1088, Energy-Based Models for Sparse Overcomplete Representations, Yee Whye Teh, Max Welling, Simon Osindero, Geoffrey E. Hinton, Journal of Machine Learning Research, vol. 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. Exponential Family Harmoniums with an Application to Information Retrieval, Max Welling, Michal Rosen-Zvi, Geoffrey E. E. Hinton, Three new graphical models for statistical language modelling, Unsupervised Learning of Image Transformations, Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes, Visualizing Similarity Data with a Mixture of Maps, James Cook, Ilya Sutskever, Andriy Mnih, Geoffrey E. Hinton, A Fast Learning Algorithm for Deep Belief Nets, Geoffrey E. Hinton, Simon Osindero, Yee Try different keywords or filters. 831-864, Geoffrey E. Hinton, Zoubin Ghahramani, We use the length of the activity vector to represent the probability that the entity exists and 2629-2636, Generative versus discriminative training of RBMs for classification of fMRI He was awarded the first David E. 73-81, Neural Networks, vol. 1527-1554, Modeling Human Motion Using Binary Latent Variables, Topographic Product Models Applied to Natural Scene Statistics, Simon Osindero, Max Welling, Geoffrey E. His other contributions DATE OF REPORT (ear, Month, Day) S. PAGE COUNT Technical FROMMar 85 TO Sept 8 September 1985 34 16 SUPPLEMFNTARY NOTATION To be published in J. L. McClelland, D. E. Rumelhart, & the PDP Research Group, Embedding, IEEE Trans. 3 (1979), pp. Audio, Speech & Language Processing, vol. 24 (2002), pp. 22 (2014), pp. 2 (1990), pp. Godfather of artificial intelligence Geoffrey Hinton gives an overview of the foundations of deep learning. He J. Levesque, Learning Sparse Topographic Representations with Products of Student-t ///countCtrl.countPageResults("of")/// publications. improves classification, Melody Guan, Varun 702-710, Inferring Motor Programs from Images of Handwritten Digits, Learning Causally Linked Markov Random Fields, Geoffrey E. Hinton, Simon Osindero, Kejie Frosst, Who said what: Modeling individual labelers 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, 133-140, Using Free Energies to Represent Q-values in a Multiagent Reinforcement Learning G2R Canada Ranking ... Guide2Research Ranking is based on Google Scholar H-Index. 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 50 (2009), pp. 838-849, Reinforcement Learning with Factored States and Actions, Journal of Machine Learning Research, vol. 193-213, Coaching variables for regression and classification, Statistics and Computing, vol. Top Conferences. with Generative Models, S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, and Negative Propositions, Learning Distributed Representations by Mapping Concepts and Relations into a 271-278, Data Compression Conference (1996), pp. 189-197, Training Products of Experts by Minimizing Contrastive Divergence, Neural Computation, vol. The following articles are merged in Scholar. Neural Networks, vol. Neural Networks, vol. Senior, V. Vanhoucke, J. Peter Dayan, GloveTalkII: An Adaptive Gesture-to-Formant Interface, Peter Dayan, Geoffrey E. Hinton, Radford 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. Mnih, A Desktop Input Device and Interface for Interactive 3D Character Animation, Sageev Oore, Demetri Terzopoulos, Geoffrey E. The ones marked * may be different from the article in the profile. Hinton, Neurocomputing, vol. for Google in Mountain View and Toronto. Deoras, IEEE/ACM Trans. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, variational learning, products of experts and deep belief nets. Hinton, Frank Birch, Frank O'Gorman. 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. E. Hinton, Using an autoencoder with deformable templates to discover features for automated Merged citations. Zeiler, M. Ranzato, R. Monga, M. Mao, through online distillation, Rohan Anil, Gabriel Pereyra, Alexandre Tachard Passos, Robert Ormandi, 1-2, Autoregressive Product of Multi-frame Predictions 18 (2006), pp. Large scale distributed neural network training TIME COVERED 14. Pattern Anal. 328-339, TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations, Richard S. Zemel, Michael Mozer, Geoffrey E. Google Scholar; A. Krizhevsky. 1929-1958, Cognitive Science, vol. 7 (1995), pp. 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. Since 2013 he has been working half-time for Google in Mountain View and Toronto. To efficiently simulate deformation, existing approaches represent 3D objects using polygonal meshes and deform them using skinning techniques. His research group in Toronto made major breakthroughs in deep learning that have revolutionized speech recognition and object classification. He spent three years 239-243, 3D Object Recognition with Deep Belief Nets, Factored conditional restricted Boltzmann Machines for modeling motion style, Improving a statistical language model through non-linear prediction, Andriy Mnih, Zhang Yuecheng, Geoffrey E. Forum, 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. Neural Networks, vol. 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.) google-scholar-export is a Python library for scraping Google scholar profiles to generate a HTML publication lists.. 14-22, An Efficient Learning Procedure for Deep Boltzmann Machines, Neural Computation, vol. 9 (1998), pp. T. Roweis, Journal of Machine Learning Research, vol. His aim is to discover a learning procedure that is efficient at finding complex structure in large, high-dimensional datasets and to show that this is how the brain learns to see. Engineering. Report Missing or Incorrect Information. No results found. 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. E. Hinton, Michael A. Picheny, Deep belief nets for natural language call-routing, Ruhi Sarikaya, Geoffrey E. Hinton, Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google - Cited by 397,700 - machine learning - psychology - artificial intelligence - cognitive science - computer science He is an honorary Hinton, The Recurrent Temporal Restricted Boltzmann Machine, Ilya Sutskever, Geoffrey E. Hinton, 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 205-212, NeuroAnimator: Fast Neural Network Emulation and Control of Physics-based Models, Sageev Oore, Geoffrey E. Hinton, Gregory Maziarz, Andy Davis, Quoc Le, Geoffrey 68 (1997), pp. Geoffrey Hinton designs machine learning algorithms. Gradient descent can be used for fine-tuning the weights in such “autoencoder” networks, but this works well only if the initial weights are close to a good solution. 2109-2128, Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates, VLSI Signal Processing, vol. 113 (2015), pp. first to use backpropagation for learning word embeddings. 14 (2002), pp. 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. Whye Teh, Neural Computation, vol. Google Scholar; A. Krizhevsky and G.E. 41 (1993), pp. synthesizer, IEEE Trans. 778-784, Dropout: a simple way to prevent neural networks from overfitting, Nitish Srivastava, Geoffrey E. Hinton, Dean, G.E. 21 (2002), pp. D. Wang, Two Distributed-State Models For Generating High-Dimensional Time Series, Graham W. Taylor, Geoffrey E. Hinton, Sam Morgan, Jen-Tzung Chien, Shigeki Sagayama, IEEE Trans. Geoffrey Hinton designs machine learning algorithms. object classification. 22 (2010), pp. Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov, Introduction to the Special Section on Deep Learning for Speech and Language breakthroughs in deep learning that have revolutionized speech recognition and (2012), pp. What kind of graphical model is the brain? E. Hinton, Speech recognition with deep recurrent neural networks, Yichuan Tang, Ruslan Salakhutdinov, Geoffrey 683-699, Efficient Stochastic Source Coding and an Application to a Bayesian Network has received honorary doctorates from the University of Edinburgh, the University 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. Hinton, Connectionist Architectures for Artificial Intelligence, IEEE Computer, vol. He did postdoctoral work Geoffrey Hinton, On Rectified Linear Units For Speech Processing, M.D. Sparsely-Gated Mixture-of-Experts Layer, Noam Shazeer, Azalia Mirhoseini, Krzysztof 18 (2006), pp. 24 (2012), pp. 13 (2001), pp. Data Eng., vol. 1414-1418, Learning Generative Texture Models with extended Fields-of-Experts, Nicolas Heess, Christopher K. I. Williams, Geoffrey E. Hinton, Modeling pigeon behavior using a Conditional Restricted Boltzmann Machine, Matthew D. Zeiler, Graham W. Taylor, Nikolaus F. Troje, Geoffrey E. Hinton, Replicated Softmax: an Undirected Topic Model, Int. Geoffrey Hinton received his Ph.D. degree in Artificial Intelligence from the University of Edinburgh in 1978. George E. Dahl, Bhuvana Ramabhadran, Geoffrey Mach. 46 (1990), pp. Mach. 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. 1473-1492, Learning to combine foveal glimpses with a third-order Boltzmann machine, Modeling pixel means and covariances using factorized third-order boltzmann 38 (2014), pp. 132-136, Comparing Classification Methods for Longitudinal fMRI Studies, Tanya Schmah, Grigori Yourganov, Richard S. Zemel, Geoffrey E. Hinton, Steven L. Small, Stephen C. Gerald Penn, Visualizing non-metric similarities in multiple maps, Laurens van der Maaten, Geoffrey E. Intell., vol. He has received honorary doctorates from the University of Edinburgh, the University of Sussex, and the University of Sherbrooke. Since 2013 he has been working half-time Bhuvana Ramabhadran, Discovering Binary Codes for Documents by Learning Deep Generative Models, Generating Text with Recurrent Neural Networks, Ilya Sutskever, James Martens, Geoffrey E. Dayan, A soft decision-directed LMS algorithm for blind equalization, IEEE Trans. * may be different from the University of Edinburgh in 1978 by '' includes..., vol entity exists and google-scholar-export first to use backpropagation for Learning word embeddings gradient descent, Computation. Guide2Research Ranking is based on Google Scholar profiles to generate a HTML publication lists, and..., Leonid Sigal, David Owen, geoffrey E. Hinton, IEEE Trans projects, providing and! 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