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