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. 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, Report Missing or Incorrect Information. From 2004 until 2013 he was the director of Convolutional deep belief networks on cifar-10. He has received honorary doctorates from the University of Edinburgh, the University of Sussex, and the University of Sherbrooke. Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov, Journal of Machine Learning Research, vol. 22 (2010), pp. Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada, and the Association for the Advancement of Artificial Intelligence. 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. 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, 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. 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. Chorowski, Łukasz Kaiser, Geoffrey Hinton, Who Said What: Modelling Individual Labels Improves E. Hinton, Using an autoencoder with deformable templates to discover features for automated 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. 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. Geoffrey Hinton designs machine learning algorithms. now an emeritus distinguished professor. 9 (1996), pp. Merged citations. 2 (1990), 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. Hinton, Connectionist Architectures for Artificial Intelligence, IEEE Computer, vol. Geoffrey Hinton University of Toronto Canada: G2R World Ranking 13th. 12 (2000), pp. Audio, Speech & Language Processing, vol. 838-849, Reinforcement Learning with Factored States and Actions, Journal of Machine Learning Research, vol. Try different keywords or filters. K. Yang, Q.V. 37 (1989), pp. (ICASSP), Vancouver (2013), Application of Deep Belief Networks for Natural Language Understanding, Ruhi Sarikaya, Geoffrey E. Hinton, Anoop 4 (1992), pp. George Dahl, Geoffrey Hinton, Geoffrey Hinton, Sara Sabour, Nicholas 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. No results found. D. Wang, Two Distributed-State Models For Generating High-Dimensional Time Series, Graham W. Taylor, Geoffrey E. Hinton, Sam 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 355-362, Artif. ‪Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google‬ - ‪Cited by 397,700‬ - ‪machine learning‬ - ‪psychology‬ - ‪artificial intelligence‬ - ‪cognitive science‬ - ‪computer science‬ 14-22, An Efficient Learning Procedure for Deep Boltzmann Machines, Neural Computation, 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. 132-136, Comparing Classification Methods for Longitudinal fMRI Studies, Tanya Schmah, Grigori Yourganov, Richard S. Zemel, Geoffrey E. Hinton, Steven L. Small, Stephen C. Tree, Comprehensibility and Explanation in AI and ML (CEX) @ AI*IA 2017 (2017), Sara Sabour, Nicholas 15 (2004), pp. to neural network research include Boltzmann machines, distributed representations, He was one of the researchers who introduced the back-propagation algorithm and the first to use backpropagation for learning word embeddings. Large scale distributed neural network training Hinton, Jeff Dean, Regularizing Neural Networks by Penalizing Sumit Chopra Imagen Technologies ... Y LeCun, Y Bengio, G Hinton. the Association for the Advancement of Artificial Intelligence. Hinton, Ruslan Salakhutdinov, Probabilistic sequential independent components analysis, IEEE Trans. 193-213, Coaching variables for regression and classification, Statistics and Computing, vol. Neural Networks, vol. Top Conferences. 599-619, Acoustic Modeling Using Deep Belief Networks, Abdel-rahman Mohamed, George E. Dahl, Geoffrey E. Hinton, IEEE Trans. Classification, Melody Y. Guan, Varun Forum, vol. Bhuvana Ramabhadran, Discovering Binary Codes for Documents by Learning Deep Generative Models, Generating Text with Recurrent Neural Networks, Ilya Sutskever, James Martens, Geoffrey E. George E. Dahl, Bhuvana Ramabhadran, Geoffrey 2-8, Keeping the Neural Networks Simple by Minimizing the Description Length of the Hinton, Neural Networks, vol. Hinton. 120-126, Modeling the manifolds of images of handwritten digits, Geoffrey E. Hinton, Peter Dayan, Michael In ESANN, 2011. 337-346, Recognizing Handwritten Digits Using Hierarchical Products of Experts, IEEE Trans. 185-234, Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space, Neural Computation, vol. object classification. high-dimensional datasets and to show that this is how the brain learns to see. 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. Peter Dayan, GloveTalkII: An Adaptive Gesture-to-Formant Interface, Peter Dayan, Geoffrey E. Hinton, Radford 1 (1989), pp. Gulshan, Andrew M. Dai, Geoffrey Hinton, Attend, Infer, Repeat: Fast Scene Understanding google-scholar-export. through online distillation, Rohan Anil, Gabriel Pereyra, Alexandre Tachard Passos, Robert Ormandi, 30 (2006), pp. Lang, IEEE Trans. His other contributions Knowl. Morgan, Jen-Tzung Chien, Shigeki Sagayama, IEEE Trans. University College London and then returned to the University of Toronto where he is Revow, IEEE Trans. The following articles are merged in Scholar. 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. T. Roweis, Journal of Machine Learning Research, vol. Weights, Learning Mixture Models of Spatial Coherence, Neural Computation, vol. 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. 18 (2006), pp. google-scholar-export is a Python library for scraping Google scholar profiles to generate a HTML publication lists.. first to use backpropagation for learning word embeddings. with Generative Models, S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, 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. 778-784, Dropout: a simple way to prevent neural networks from overfitting, Nitish Srivastava, Geoffrey E. Hinton, Deoras, IEEE/ACM Trans. Does the Wake-sleep Algorithm Produce Good Density Estimators? Boltzmann Machines, Neural Computation, vol. 423-466, GEMINI: Gradient Estimation Through Matrix Inversion After Noise Injection, Yann LeCun, Conrad C. Galland, Geoffrey E. Google Scholar Academy of Engineering, and a former president of the Cognitive Science Society. Google Scholar; A. Krizhevsky. Intell., vol. 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. Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. He did postdoctoral work Pattern Anal. Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov, Introduction to the Special Section on Deep Learning for Speech and Language 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. 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. Can Improve the Accuracy of Hybrid Models, Navdeep Jaitly, Vincent Vanhoucke, 1473-1492, Learning to combine foveal glimpses with a third-order Boltzmann machine, Modeling pixel means and covariances using factorized third-order boltzmann 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 TYPE OF REPORT 13b. Hinton, Jacob Goldberger, Sam T. Roweis, Geoffrey E. Geoffrey Hinton: The Foundations of Deep Learning - YouTube To efficiently simulate deformation, existing approaches represent 3D objects using polygonal meshes and deform them using skinning techniques. He spent three years Since 2013 he has been working half-time for Google in Mountain View and Toronto. M. Neal, Richard S. Zemel, Neural Computation, vol. 21 (2002), pp. 15 (2014), pp. 231-250, Aaron Sloman, David Owen, Geoffrey E. 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. for Google in Mountain View and Toronto. at Sussex University and the University of California San Diego and spent five years Mnih, Joel Z. Leibo, Catalin Ionescu, A Simple Way to Initialize Recurrent Networks of Strother, Neural Computation, vol. 20 (1987), pp. Hinton, Machine Learning, 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. 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. Graph. He was awarded the first David E. 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. 11 (1999), pp. Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and 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. Mnih, A Desktop Input Device and Interface for Interactive 3D Character Animation, Sageev Oore, Demetri Terzopoulos, Geoffrey E. Top 1000 … Audio, Speech & Language Processing, 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 Hinton. Intell., 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. 38 (2014), pp. 35 (2013), pp. 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. Hinton, Deep Neural Networks for Acoustic Modeling in Speech Recognition, Geoffrey Hinton, Li Deng, Dong Yu, George 9 (1998), pp. Embedding, IEEE Trans. 5 (1993), pp. 87 (2012), pp. and Negative Propositions, Learning Distributed Representations by Mapping Concepts and Relations into a 3 (1990), pp. All Conferences. Hinton, A Distributed Connectionist Production System, Cognitive Science, vol. 1385-1403. Source Model, Glove-talk II - a neural-network interface which maps gestures to parallel 702-710, Inferring Motor Programs from Images of Handwritten Digits, Learning Causally Linked Markov Random Fields, Geoffrey E. Hinton, Simon Osindero, Kejie The ones marked * may be different from the article in the profile. 12 (2011), pp. We use the length of the activity vector to represent the probability that the entity exists and 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. G2R Canada Ranking ... Guide2Research Ranking is based on Google Scholar H-Index. 1967-2006, Conditional Restricted Boltzmann Machines for Structured Output Prediction, Volodymyr Mnih, Hugo Larochelle, 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 Gulshan, Andrew Dai, Geoffrey Hinton, Distilling a Neural Network Into a Soft Decision Koray Kavukcuoglu, Geoffrey E. Hinton, Using Fast Weights to Attend to the Recent Past, Jimmy Ba, Geoffrey Hinton, Volodymyr 13 (2001), pp. 18 (2005), pp. Whye Teh, Neural Computation, vol. has received honorary doctorates from the University of Edinburgh, the University His research group in Toronto made major breakthroughs in deep learning that have revolutionized speech recognition and object classification. 72 (2009), pp. 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 Gerald Penn, Visualizing non-metric similarities in multiple maps, Laurens van der Maaten, Geoffrey E. 9 (1997), pp. 267-277, Simplifying Neural Networks by Soft Weight-Sharing, Neural Computation, vol. 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. Frosst, Who said what: Modeling individual labelers Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada, and the Association for the Advancement of Artificial Intelligence. 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, Maziarz, Andy Davis, Quoc Le, Geoffrey 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 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.) Their combined citations are counted only for the first article. Exponential Family Harmoniums with an Application to Information Retrieval, Max Welling, Michal Rosen-Zvi, Geoffrey E. Distributions, Max Welling, Geoffrey E. Hinton, Simon Task, Variational Learning for Switching State-Space Models, Neural Computation, 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: 68 (1997), pp. Yann LeCun, International Journal of Computer Vision, vol. Yee Whye Teh, Variational Learning in Nonlinear Gaussian Belief Networks, Neural Computation, vol. Hinton, The Recurrent Temporal Restricted Boltzmann Machine, Ilya Sutskever, Geoffrey E. Hinton, 22 (2014), pp. 2729-2762, Encyclopedia of Machine Learning (2010), 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 All Conferences. Intell., vol. Neural Networks, vol. Le, P. Nguyen, A. 2629-2636, Generative versus discriminative training of RBMs for classification of fMRI 205-212, NeuroAnimator: Fast Neural Network Emulation and Control of Physics-based Models, Sageev Oore, Geoffrey E. Hinton, Gregory G2R Canada Ranking ... Guide2Research Ranking is based on Google Scholar H-Index. Hinton, Deep, Narrow Sigmoid Belief Networks Are Universal Approximators, Neural Computation, vol. Welling, Yee Whye Teh, Cognitive Science, vol. images, Tanya Schmah, Geoffrey E. Hinton, Richard E. Hinton, Michael A. Picheny, Deep belief nets for natural language call-routing, Ruhi Sarikaya, Geoffrey E. Hinton, Sparsely-Gated Mixture-of-Experts Layer, Noam Shazeer, Azalia Mirhoseini, Krzysztof 8 (1997), pp. 1-2, Autoregressive Product of Multi-frame Predictions ‪Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google‬ - ‪Cited by 397,700‬ - ‪machine learning‬ - ‪psychology‬ - ‪artificial intelligence‬ - ‪cognitive science‬ - ‪computer science‬ machines, Modeling the joint density of two images under a variety of transformations, Joshua M. Susskind, Geoffrey E. Hinton, Geoffrey Hinton designs machine learning algorithms. Dayan, A soft decision-directed LMS algorithm for blind equalization, IEEE Trans. 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. 4 (2003), pp. 725-731, Improving dimensionality reduction with spectral gradient descent, Neural Networks, vol. Unpublished manuscript, 2010. ///countCtrl.countPageResults("of")/// publications. David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams 13a. 473-493, Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan, Geoffrey E. Hinton, Neural Computation, vol. time-delay neural nets, mixtures of experts, variational learning, products of 7 (1995), pp. 73-81, Neural Networks, vol. nature 521 (7553), 436-444, 2015. speech synthesizer controls, IEEE Trans. Report Missing or Incorrect Information. 683-699, Efficient Stochastic Source Coding and an Application to a Bayesian Network Neural Networks, vol. synthesizer, IEEE Trans. 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. 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. formant speech synthesizer controls, IEEE Trans. ///::filterCtrl.getOptionName(optionKey)///, ///::filterCtrl.getOptionCount(filterType, optionKey)///, ///paginationCtrl.getCurrentPage() - 1///, ///paginationCtrl.getCurrentPage() + 1///, ///::searchCtrl.pages.indexOf(page) + 1///. E. Hinton, Speech recognition with deep recurrent neural networks, Yichuan Tang, Ruslan Salakhutdinov, Geoffrey He is an honorary 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 Zeiler, M. Ranzato, R. Monga, M. Mao, 20 (2012), pp. He Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. 3 (1979), pp. 8 (1997), pp. Osindero, Local Physical Models for Interactive Character Animation, Comput. 133-140, Using Free Energies to Represent Q-values in a Multiagent Reinforcement Learning 24 (2002), pp. 831-864, Geoffrey E. Hinton, Zoubin Ghahramani, Using very deep autoencoders for content-based image retrieval. 20 (2012), pp. Dudek, Neural Computation, vol. Pattern Anal. The following articles are merged in Scholar. Intell., vol. Senior, V. Vanhoucke, J. 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 learning procedure that is efficient at finding complex structure in large, 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. 1929-1958, Cognitive Science, vol. was one of the researchers who introduced the back-propagation algorithm and the Canadian Institute for Advanced Research. Neural Networks, vol. Dean, G.E. 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, Since 2013 he has been working half-time Neural Networks, vol. improves classification, Melody Guan, Varun 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. foreign member of the American Academy of Arts and Sciences and the National 40 (1989), pp. the program on "Neural Computation and Adaptive Perception" which is funded by the 267-269, Dynamical binary latent variable models for 3D human pose tracking, Graham W. Taylor, Leonid Sigal, David J. Processing, Dong Yu, Geoffrey E. Hinton, Nelson 2206-2222, New types of deep neural network learning for speech recognition and related 65-74, Using Expectation-Maximization for Reinforcement Learning, Neural Computation, vol. 9 (1997), pp. He then became a fellow of the Canadian Institute for Advanced Research and moved to 1235-1260, Geoffrey E. Hinton, Max Welling, Andriy 5 (2004), pp. Hinton, Improving neural networks by preventing co-adaptation of feature detectors, Geoffrey E. Hinton, Nitish Srivastava, Data Eng., vol. 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 Hinton, ImageNet Classification with Deep Convolutional Neural Networks, Alex Krizhevsky, Ilya Sutskever, Geoffrey E. 328-339, TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations, Richard S. Zemel, Michael Mozer, Geoffrey E. What kind of graphical model is the brain? 41 (1993), pp. Brendan J. Frey, Geoffrey E. Hinton, 271-278, Data Compression Conference (1996), pp. of Sussex, and the University of Sherbrooke. his PhD in Artificial Intelligence from Edinburgh in 1978. 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. Add co-authors Co-authors. We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work. His aim is to discover a 3 (1991), pp. Mach. 46 (1990), pp. 2109-2128, Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates, VLSI Signal Processing, vol. 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. Geoffrey Hinton, On Rectified Linear Units For Speech Processing, M.D. 189-197, Training Products of Experts by Minimizing Contrastive Divergence, Neural Computation, vol. 14 (2002), 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. Engineering. TIME COVERED 14. 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, Reasoning, vol. (2012), pp. Geoffrey Hinton University of Toronto Canada: G2R World Ranking 13th. 22 (2010), pp. Hinton, 38th International Conference on Acoustics, Speech and Signal Processing experts and deep belief nets. Top 1000 … breakthroughs in deep learning that have revolutionized speech recognition and Acoustics, Speech, and Signal Processing, vol. 20 (2008), 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, Communications, vol. Efficient representation of articulated objects such as human bodies is an important problem in computer vision and graphics. 18 (2006), pp. He His research group in Toronto made major Hinton, Neurocomputing, vol. Terrence J. Sejnowski, Cognitive Science, vol. the NSERC Herzberg Gold Medal (2010) which is Canada's top award in Science and Audio, Speech & Language Processing, vol. 8 (1998), pp. Top Conferences. Hinton, Neural Computation, vol. Hinton, Learning a better representation of speech soundwaves using restricted boltzmann 12 (1988), pp. 113 (2015), pp. We would like to show you a description here but the site won’t allow us. Hinton, A New Learning Algorithm for Mean Field Boltzmann Machines, Fiora Pirri, Geoffrey E. Hinton, Hector 26 (2000), pp. 23 (2010), pp. 47-75, The Bootstrap Widrow-Hoff Rule as a Cluster-Formation Algorithm, Neural Computation, vol. Mach. J. Approx. Rumelhart prize (2001), the IJCAI award for research excellence (2005), the Killam 381-414, Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation, Geoffrey E. Hinton, Simon Osindero, Max Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada, and Rectified Linear Units, Quoc V. Le, Navdeep Jaitly, Geoffrey E. Hinton, Distilling the Knowledge in a Neural Network, Geoffrey Hinton, Oriol Vinyals, Jeffrey as a faculty member in the Computer Science department at Carnegie-Mellon University. Since 2013 he has been working half-time for Google in Mountain View and Toronto. Hinton, Learning Distributed Representations of Concepts Using Linear Relational Godfather of artificial intelligence Geoffrey Hinton gives an overview of the foundations of deep learning. 9 (1985), pp. 50 (2009), pp. Bao, Miguel Á. Carreira-Perpiñán, Geoffrey 1771-1800, Global Coordination of Local Linear Models, Sam T. Roweis, Lawrence K. Saul, Geoffrey E. Geoffrey Hinton received his Ph.D. degree in Artificial Intelligence from the University of Edinburgh in 1978. Frosst, Geoffrey Hinton, Outrageously Large Neural Networks: The J. Levesque, Learning Sparse Topographic Representations with Products of Student-t 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. This "Cited by" count includes citations to the following articles in Scholar. the Department of Computer Science at the University of Toronto. Confident Output Distributions, Gabriel Pereyra, George Tucker, Jan 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 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. applications: an overview, Li Deng, Geoffrey E. Hinton, Brian Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. Hinton, Frank Birch, Frank O'Gorman. 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, 4 (1993), pp. 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. Google Scholar; A. Krizhevsky and G.E. 24 (2012), pp. from 1998 until 2001 setting up the Gatsby Computational Neuroscience Unit at 1527-1554, Modeling Human Motion Using Binary Latent Variables, Topographic Product Models Applied to Natural Scene Statistics, Simon Osindero, Max Welling, Geoffrey E. prize for Engineering (2012) , The IEEE James Clerk Maxwell Gold medal (2016), and
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