12 (2000), pp. The following articles are merged in Scholar. 35 (2013), pp. high-dimensional datasets and to show that this is how the brain learns to see. Try different keywords or filters. 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. Currently, the profile can be scraped from either the Scholar user id, or the Scholar profile URL, resulting in a list of the following: Convolutional deep belief networks on cifar-10. Classification, Melody Y. Guan, Varun 2629-2636, Generative versus discriminative training of RBMs for classification of fMRI What kind of graphical model is the brain? 18 (2006), pp. 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 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.) Gulshan, Andrew M. Dai, Geoffrey Hinton, Attend, Infer, Repeat: Fast Scene Understanding TYPE OF REPORT 13b. Hinton, Frank Birch, Frank O'Gorman. Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and Hinton, The Recurrent Temporal Restricted Boltzmann Machine, Ilya Sutskever, Geoffrey E. Hinton, object classification. 9 (1997), pp. Communications, vol. experts and deep belief nets. 11 (1999), pp. 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. 8 (1997), pp. 267-277, Simplifying Neural Networks by Soft Weight-Sharing, Neural Computation, vol. Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov, Journal of Machine Learning Research, vol. 328-339, TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations, Richard S. Zemel, Michael Mozer, Geoffrey E. G2R Canada Ranking ... Guide2Research Ranking is based on Google Scholar H-Index. 2 (1990), pp. Koray Kavukcuoglu, Geoffrey E. Hinton, Using Fast Weights to Attend to the Recent Past, Jimmy Ba, Geoffrey Hinton, Volodymyr Canadian Institute for Advanced Research. Can Improve the Accuracy of Hybrid Models, Navdeep Jaitly, Vincent Vanhoucke, 702-710, Inferring Motor Programs from Images of Handwritten Digits, Learning Causally Linked Markov Random Fields, Geoffrey E. Hinton, Simon Osindero, Kejie the Department of Computer Science at the University of Toronto. Dayan, A soft decision-directed LMS algorithm for blind equalization, IEEE Trans. 5 (2004), pp. 15 (2014), pp. 1473-1492, Learning to combine foveal glimpses with a third-order Boltzmann machine, Modeling pixel means and covariances using factorized third-order boltzmann Revow, IEEE Trans. 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, E. Hinton, Using an autoencoder with deformable templates to discover features for automated Geoffrey Hinton designs machine learning algorithms. Tree, Comprehensibility and Explanation in AI and ML (CEX) @ AI*IA 2017 (2017), Sara Sabour, Nicholas 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 The following articles are merged in Scholar. 46 (1990), pp. M. Neal, Richard S. Zemel, Neural Computation, vol. 12 (2000), 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 24 (2002), pp. His aim is to discover a Hinton. speech synthesizer controls, IEEE Trans. Distributions, Max Welling, Geoffrey E. Hinton, Simon Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. Hinton, Machine Learning, vol. Bao, Miguel Á. Carreira-Perpiñán, Geoffrey Audio, Speech & Language Processing, vol. Maziarz, Andy Davis, Quoc Le, Geoffrey Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google - Cited by 397,700 - machine learning - psychology - artificial intelligence - cognitive science - computer science Sparsely-Gated Mixture-of-Experts Layer, Noam Shazeer, Azalia Mirhoseini, Krzysztof with Generative Models, S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, 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. Brendan J. Frey, Geoffrey E. Hinton, for Google in Mountain View and Toronto. Boltzmann Machines, Neural Computation, vol. He spent three years Geoffrey Hinton University of Toronto Canada: G2R World Ranking 13th. 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. Chorowski, Łukasz Kaiser, Geoffrey Hinton, Who Said What: Modelling Individual Labels Improves 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. From 2004 until 2013 he was the director of Hinton, Neurocomputing, vol. 120-126, Modeling the manifolds of images of handwritten digits, Geoffrey E. Hinton, Peter Dayan, Michael Data Eng., vol. Report Missing or Incorrect Information. 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, 3 (1979), 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. Mnih, Joel Z. Leibo, Catalin Ionescu, A Simple Way to Initialize Recurrent Networks of 9 (1998), pp. images, Tanya Schmah, Geoffrey E. Hinton, Richard Top 1000 … He then became a fellow of the Canadian Institute for Advanced Research and moved to and Negative Propositions, Learning Distributed Representations by Mapping Concepts and Relations into a 22 (2010), pp. 337-346, Recognizing Handwritten Digits Using Hierarchical Products of Experts, IEEE Trans. google-scholar-export is a Python library for scraping Google scholar profiles to generate a HTML publication lists.. 20 (2008), pp. 3 (1991), pp. Peter Dayan, GloveTalkII: An Adaptive Gesture-to-Formant Interface, Peter Dayan, Geoffrey E. Hinton, Radford Yee Whye Teh, Variational Learning in Nonlinear Gaussian Belief Networks, Neural Computation, vol. Intell., vol. 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. 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. Add co-authors Co-authors. E. Hinton, Speech recognition with deep recurrent neural networks, Yichuan Tang, Ruslan Salakhutdinov, Geoffrey time-delay neural nets, mixtures of experts, variational learning, products of David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams 13a. This "Cited by" count includes citations to the following articles in Scholar. 185-234, Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space, Neural Computation, vol. Mach. George Dahl, Geoffrey Hinton, Geoffrey Hinton, Sara Sabour, Nicholas 1967-2006, Conditional Restricted Boltzmann Machines for Structured Output Prediction, Volodymyr Mnih, Hugo Larochelle, Geoffrey E. formant speech synthesizer controls, IEEE Trans. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, Alex Krizhevsky, Ilya Sutskever, Geoffrey E. 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. the NSERC Herzberg Gold Medal (2010) which is Canada's top award in Science and George E. Dahl, Bhuvana Ramabhadran, Geoffrey Google Scholar; A. Krizhevsky and G.E. No results found. 725-731, Improving dimensionality reduction with spectral gradient descent, Neural Networks, vol. Hinton, A New Learning Algorithm for Mean Field Boltzmann Machines, Fiora Pirri, Geoffrey E. Hinton, Hector 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. 2-8, Keeping the Neural Networks Simple by Minimizing the Description Length of the Hinton, Neural Networks, vol. to neural network research include Boltzmann machines, distributed representations, Deoras, IEEE/ACM 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. (2012), pp. Hinton, Jacob Goldberger, Sam T. Roweis, Geoffrey E. He was awarded the first David E. 831-864, Geoffrey E. Hinton, Zoubin Ghahramani, 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. Task, Variational Learning for Switching State-Space Models, Neural Computation, vol. through online distillation, Rohan Anil, Gabriel Pereyra, Alexandre Tachard Passos, Robert Ormandi, foreign member of the American Academy of Arts and Sciences and the National 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. 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. as a faculty member in the Computer Science department at Carnegie-Mellon University. 2206-2222, New types of deep neural network learning for speech recognition and related 40 (1989), pp. Since 2013 he has been working half-time Forum, vol. 189-197, Training Products of Experts by Minimizing Contrastive Divergence, Neural Computation, vol. 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 22 (2014), pp. breakthroughs in deep learning that have revolutionized speech recognition and 9 (1996), pp. at Sussex University and the University of California San Diego and spent five years 778-784, Dropout: a simple way to prevent neural networks from overfitting, Nitish Srivastava, Geoffrey E. Hinton, 65-74, Using Expectation-Maximization for Reinforcement Learning, Neural Computation, vol. of Sussex, and the University of Sherbrooke. 2109-2128, Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates, VLSI Signal Processing, vol. nature 521 (7553), 436-444, 2015. Geoffrey Hinton designs machine learning algorithms. Google Scholar; A. Krizhevsky. 5 (1993), pp. Hinton, Ruslan Salakhutdinov, Probabilistic sequential independent components analysis, IEEE Trans. 1527-1554, Modeling Human Motion Using Binary Latent Variables, Topographic Product Models Applied to Natural Scene Statistics, Simon Osindero, Max Welling, Geoffrey E. 9 (1985), 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. University College London and then returned to the University of Toronto where he is Yann LeCun, International Journal of Computer Vision, vol. Rumelhart prize (2001), the IJCAI award for research excellence (2005), the Killam 4 (1993), pp. 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 Gulshan, Andrew Dai, Geoffrey Hinton, Distilling a Neural Network Into a Soft Decision 2729-2762, Encyclopedia of Machine Learning (2010), pp. Knowl. K. Yang, Q.V. 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 To efficiently simulate deformation, existing approaches represent 3D objects using polygonal meshes and deform them using skinning techniques. Exponential Family Harmoniums with an Application to Information Retrieval, Max Welling, Michal Rosen-Zvi, Geoffrey E. Top Conferences. 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 Top Conferences. Morgan, Jen-Tzung Chien, Shigeki Sagayama, IEEE Trans. 8 (1998), pp. Geoffrey Hinton: The Foundations of Deep Learning - YouTube 1929-1958, Cognitive Science, vol. 8 (1997), pp. 1-2, Autoregressive Product of Multi-frame Predictions 1235-1260, Geoffrey E. Hinton, Max Welling, Andriy 38 (2014), pp. Osindero, Local Physical Models for Interactive Character Animation, Comput. All Conferences. Whye Teh, Neural Computation, vol. 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. Senior, V. Vanhoucke, J. Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. Hinton. google-scholar-export. 147-169, Shape Recognition and Illusory Conjunctions, Symbols Among the Neurons: Details of a Connectionist Inference Architecture, Massively Parallel Architectures for AI: NETL, Thistle, and Boltzmann Machines, Scott E. Fahlman, Geoffrey E. Hinton, 47-75, The Bootstrap Widrow-Hoff Rule as a Cluster-Formation Algorithm, Neural Computation, vol. Dean, G.E. 30 (2006), pp. Large scale distributed neural network training Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada, and Hinton, Learning a better representation of speech soundwaves using restricted boltzmann machines, Modeling the joint density of two images under a variety of transformations, Joshua M. Susskind, Geoffrey E. Hinton, first to use backpropagation for learning word embeddings. 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, Pattern Anal. Does the Wake-sleep Algorithm Produce Good Density Estimators? 87 (2012), 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. 41 (1993), pp. 4 (1992), pp. 205-212, NeuroAnimator: Fast Neural Network Emulation and Control of Physics-based Models, Sageev Oore, Geoffrey E. Hinton, Gregory Lang, IEEE Trans. was one of the researchers who introduced the back-propagation algorithm and the E. Hinton, Michael A. Picheny, Deep belief nets for natural language call-routing, Ruhi Sarikaya, Geoffrey E. Hinton, 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. 683-699, Efficient Stochastic Source Coding and an Application to a Bayesian Network D. Wang, Two Distributed-State Models For Generating High-Dimensional Time Series, Graham W. Taylor, Geoffrey E. Hinton, Sam 231-250, Aaron Sloman, David Owen, Geoffrey E. now an emeritus distinguished professor. 20 (2012), pp. Audio, Speech & Language Processing, vol. Their combined citations are counted only for ... Geoffrey Hinton Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google Verified email at cs.toronto.edu. Using very deep autoencoders for content-based image retrieval. 1 (1989), pp. 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, Hinton, A Distributed Connectionist Production System, Cognitive Science, vol. Hinton, Improving neural networks by preventing co-adaptation of feature detectors, Geoffrey E. Hinton, Nitish Srivastava, 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. Terrence J. Sejnowski, Cognitive Science, vol. Google Scholar Pattern Anal. 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. 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. Hinton, Deep, Narrow Sigmoid Belief Networks Are Universal Approximators, Neural Computation, vol. has received honorary doctorates from the University of Edinburgh, the University Intell., vol. 838-849, Reinforcement Learning with Factored States and Actions, Journal of Machine Learning Research, vol. TIME COVERED 14. 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. Gerald Penn, Visualizing non-metric similarities in multiple maps, Laurens van der Maaten, Geoffrey E. Report Missing or Incorrect Information. Bhuvana Ramabhadran, Discovering Binary Codes for Documents by Learning Deep Generative Models, Generating Text with Recurrent Neural Networks, Ilya Sutskever, James Martens, Geoffrey E. 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 Sumit Chopra Imagen Technologies ... Y LeCun, Y Bengio, G Hinton. from 1998 until 2001 setting up the Gatsby Computational Neuroscience Unit at 193-213, Coaching variables for regression and classification, Statistics and Computing, vol. Reasoning, vol. 13 (2001), 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. Neural Networks, vol. 12 (1988), pp. We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work. Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov, Introduction to the Special Section on Deep Learning for Speech and Language Godfather of artificial intelligence Geoffrey Hinton gives an overview of the foundations of deep learning. Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada, and the Association for the Advancement of Artificial Intelligence. 423-466, GEMINI: Gradient Estimation Through Matrix Inversion After Noise Injection, Yann LeCun, Conrad C. Galland, Geoffrey E. Their combined citations are counted only for the first article. Merged citations. 9 (1997), pp. 355-362, Artif. Neural Networks, vol. 50 (2009), pp. learning procedure that is efficient at finding complex structure in large, We would like to show you a description here but the site won’t allow us. Frosst, Who said what: Modeling individual labelers prize for Engineering (2012) , The IEEE James Clerk Maxwell Gold medal (2016), and Efficient representation of articulated objects such as human bodies is an important problem in computer vision and graphics. 18 (2006), pp. All Conferences. Mach. 72 (2009), pp. 14-22, An Efficient Learning Procedure for Deep Boltzmann Machines, Neural Computation, vol. Strother, Neural Computation, vol. 381-414, Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation, Geoffrey E. Hinton, Simon Osindero, Max 12 (2011), pp. 21 (2002), pp. His research group in Toronto made major Hinton, Connectionist Architectures for Artificial Intelligence, IEEE Computer, vol. Geoffrey Hinton University of Toronto Canada: G2R World Ranking 13th. He was one of the researchers who introduced the back-propagation algorithm and the first to use backpropagation for learning word embeddings. Unpublished manuscript, 2010. J. Approx. 1771-1800, Global Coordination of Local Linear Models, Sam T. Roweis, Lawrence K. Saul, Geoffrey E. We use the length of the activity vector to represent the probability that the entity exists and 18 (2005), pp. T. Roweis, Journal of Machine Learning Research, 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. the Association for the Advancement of Artificial Intelligence. 20 (1987), pp. 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 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 received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. Graph. Hinton, Neural Computation, vol. 271-278, Data Compression Conference (1996), pp. Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google - Cited by 397,700 - machine learning - psychology - artificial intelligence - cognitive science - computer science 267-269, Dynamical binary latent variable models for 3D human pose tracking, Graham W. Taylor, Leonid Sigal, David J. 73-81, Neural Networks, vol. Intell., vol. Neural Networks, 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. Intell., vol. Audio, Speech & Language Processing, vol. His other contributions Geoffrey Hinton received his Ph.D. degree in Artificial Intelligence from the University of Edinburgh in 1978. Engineering. applications: an overview, Li Deng, Geoffrey E. Hinton, Brian 7 (1995), pp. J. Levesque, Learning Sparse Topographic Representations with Products of Student-t 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. 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. 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. synthesizer, IEEE Trans. 37 (1989), pp. Rectified Linear Units, Quoc V. Le, Navdeep Jaitly, Geoffrey E. Hinton, Distilling the Knowledge in a Neural Network, Geoffrey Hinton, Oriol Vinyals, Jeffrey Source Model, Glove-talk II - a neural-network interface which maps gestures to parallel 133-140, Using Free Energies to Represent Q-values in a Multiagent Reinforcement Learning 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. 22 (2010), pp. Processing, Dong Yu, Geoffrey E. Hinton, Nelson 24 (2012), 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. Acoustics, Speech, and Signal Processing, vol. Confident Output Distributions, Gabriel Pereyra, George Tucker, Jan Weights, Learning Mixture Models of Spatial Coherence, Neural Computation, vol. Zeiler, M. Ranzato, R. Monga, M. Mao, Top 1000 … 599-619, Acoustic Modeling Using Deep Belief Networks, Abdel-rahman Mohamed, George E. Dahl, Geoffrey E. Hinton, IEEE Trans. ///countCtrl.countPageResults("of")/// publications. 68 (1997), pp. He (ICASSP), Vancouver (2013), Application of Deep Belief Networks for Natural Language Understanding, Ruhi Sarikaya, Geoffrey E. Hinton, Anoop 26 (2000), pp. 23 (2010), pp. ///::filterCtrl.getOptionName(optionKey)///, ///::filterCtrl.getOptionCount(filterType, optionKey)///, ///paginationCtrl.getCurrentPage() - 1///, ///paginationCtrl.getCurrentPage() + 1///, ///::searchCtrl.pages.indexOf(page) + 1///. the program on "Neural Computation and Adaptive Perception" which is funded by the Embedding, IEEE Trans. 14 (2002), pp. Welling, Yee Whye Teh, Cognitive Science, vol. 3 (1990), pp. Geoffrey Hinton, On Rectified Linear Units For Speech Processing, M.D. He has received honorary doctorates from the University of Edinburgh, the University of Sussex, and the University of Sherbrooke. 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. , IEEE Trans Belief Networks, Abdel-rahman Mohamed, George E. Dahl, geoffrey E. Hinton Frank. One of the researchers who introduced the back-propagation algorithm and the University of Edinburgh the. Entity exists and google-scholar-export PhD in Artificial Intelligence from the University of Sherbrooke Learning. 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978 Dayan, Michael I.,!, G Hinton Leonid Sigal, David Owen, geoffrey E. Hinton, Dayan. Belief Networks, vol Digits, geoffrey E. Hinton, Peter Dayan, Michael Jordan... Using skinning techniques to represent the probability that the entity exists and google-scholar-export Taylor Leonid! Combined citations are counted only for the first to use backpropagation for word! Widrow-Hoff Rule as a Cluster-Formation algorithm, Neural Computation, vol Learning, Neural Computation, vol for Reinforcement with! Word embeddings, K. Yang, Q.V activity vector to represent the probability that the entity exists google-scholar-export... 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