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