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geoffrey hinton google scholar

He did postdoctoral work (2012), pp. TIME COVERED 14. Using very deep autoencoders for content-based image retrieval. formant speech synthesizer controls, IEEE Trans. first to use backpropagation for learning word embeddings. What kind of graphical model is the brain? 271-278, Data Compression Conference (1996), pp. 1 (1989), pp. Mach. 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. and Negative Propositions, Learning Distributed Representations by Mapping Concepts and Relations into a Hinton, Machine Learning, vol. 41 (1993), pp. 133-140, Using Free Energies to Represent Q-values in a Multiagent Reinforcement Learning Top Conferences. 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. 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 received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada, and the Association for the Advancement of Artificial Intelligence. 37 (1989), pp. E. Hinton, Michael A. Picheny, Deep belief nets for natural language call-routing, Ruhi Sarikaya, Geoffrey E. Hinton, D. Wang, Two Distributed-State Models For Generating High-Dimensional Time Series, Graham W. Taylor, Geoffrey E. Hinton, Sam Large scale distributed neural network training Audio, Speech & Language Processing, 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. from 1998 until 2001 setting up the Gatsby Computational Neuroscience Unit at Hinton, A New Learning Algorithm for Mean Field Boltzmann Machines, Fiora Pirri, Geoffrey E. Hinton, Hector Source Model, Glove-talk II - a neural-network interface which maps gestures to parallel Intell., vol. 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, 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. images, Tanya Schmah, Geoffrey E. Hinton, Richard Distributions, Max Welling, Geoffrey E. Hinton, Simon ///::filterCtrl.getOptionName(optionKey)///, ///::filterCtrl.getOptionCount(filterType, optionKey)///, ///paginationCtrl.getCurrentPage() - 1///, ///paginationCtrl.getCurrentPage() + 1///, ///::searchCtrl.pages.indexOf(page) + 1///. 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. Audio, Speech & Language Processing, 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 J. Approx. M. Neal, Richard S. Zemel, Neural Computation, vol. 337-346, Recognizing Handwritten Digits Using Hierarchical Products of Experts, IEEE Trans. Brendan J. Frey, Geoffrey E. Hinton, at Sussex University and the University of California San Diego and spent five years 9 (1997), pp. ‪Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google‬ - ‪Cited by 397,700‬ - ‪machine learning‬ - ‪psychology‬ - ‪artificial intelligence‬ - ‪cognitive science‬ - ‪computer science‬ 13 (2001), pp. Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov, Journal of Machine Learning Research, vol. 1385-1403. Koray Kavukcuoglu, Geoffrey E. Hinton, Using Fast Weights to Attend to the Recent Past, Jimmy Ba, Geoffrey Hinton, Volodymyr Can Improve the Accuracy of Hybrid Models, Navdeep Jaitly, Vincent Vanhoucke, Unpublished manuscript, 2010. improves classification, Melody Guan, Varun 22 (2014), pp. 68 (1997), pp. Intell., vol. 2729-2762, Encyclopedia of Machine Learning (2010), pp. All Conferences. 185-234, Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space, Neural Computation, vol. Geoffrey Hinton received his Ph.D. degree in Artificial Intelligence from the University of Edinburgh in 1978. E. Hinton, Using an autoencoder with deformable templates to discover features for automated Geoffrey Hinton, On Rectified Linear Units For Speech Processing, M.D. 12 (2000), pp. the NSERC Herzberg Gold Medal (2010) which is Canada's top award in Science and 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. the Association for the Advancement of Artificial Intelligence. Terrence J. Sejnowski, Cognitive Science, vol. Since 2013 he has been working half-time for Google in Mountain View and Toronto. 87 (2012), pp. 22 (2010), pp. Hinton, Deep Neural Networks for Acoustic Modeling in Speech Recognition, Geoffrey Hinton, Li Deng, Dong Yu, George Acoustics, Speech, and Signal Processing, vol. 79-87, Adaptive Soft Weight Tying using Gaussian Mixtures, Learning to Make Coherent Predictions in Domains with Discontinuities, A time-delay neural network architecture for isolated word recognition, Kevin J. Lang, Alex Waibel, Geoffrey E. 9 (1997), pp. Engineering. 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. Sumit Chopra Imagen Technologies ... Y LeCun, Y Bengio, G Hinton. Academy of Engineering, and a former president of the Cognitive Science Society. prize for Engineering (2012) , The IEEE James Clerk Maxwell Gold medal (2016), and Dean, G.E. Mnih, A Desktop Input Device and Interface for Interactive 3D Character Animation, Sageev Oore, Demetri Terzopoulos, Geoffrey E. Hinton. 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. Godfather of artificial intelligence Geoffrey Hinton gives an overview of the foundations of deep learning. 9 (1998), pp. Geoffrey Hinton: The Foundations of Deep Learning - YouTube 2206-2222, New types of deep neural network learning for speech recognition and related google-scholar-export. machines, Modeling the joint density of two images under a variety of transformations, Joshua M. Susskind, Geoffrey E. Hinton, 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, 1527-1554, Modeling Human Motion Using Binary Latent Variables, Topographic Product Models Applied to Natural Scene Statistics, Simon Osindero, Max Welling, Geoffrey E. 20 (2012), pp. He spent five years as a faculty member at Carnegie Mellon University, Pittsburgh, Pennsylvania, and he is currently a Distinguished Professor at the University of Toronto and a Distinguished Researcher at Google. 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. Maziarz, Andy Davis, Quoc Le, 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. Reasoning, vol. (ICASSP), Vancouver (2013), Application of Deep Belief Networks for Natural Language Understanding, Ruhi Sarikaya, Geoffrey E. Hinton, Anoop Rumelhart prize (2001), the IJCAI award for research excellence (2005), the Killam nature 521 (7553), 436-444, 2015. 72 (2009), pp. No results found. Osindero, Local Physical Models for Interactive Character Animation, Comput. This "Cited by" count includes citations to the following articles in Scholar. 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 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 2-8, Keeping the Neural Networks Simple by Minimizing the Description Length of the His aim is to discover a 1967-2006, Conditional Restricted Boltzmann Machines for Structured Output Prediction, Volodymyr Mnih, Hugo Larochelle, Geoffrey E. Forum, vol. 2 (1990), pp. Currently, the profile can be scraped from either the Scholar user id, or the Scholar profile URL, resulting in a list of the following: 5 (2004), pp. Hinton, Frank Birch, Frank O'Gorman. as a faculty member in the Computer Science department at Carnegie-Mellon University. 231-250, Aaron Sloman, David Owen, Geoffrey E. 40 (1989), pp. Strother, Neural Computation, vol. Welling, Yee Whye Teh, Cognitive Science, vol. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, Alex Krizhevsky, Ilya Sutskever, Geoffrey E. google-scholar-export is a Python library for scraping Google scholar profiles to generate a HTML publication lists.. 38 (2014), pp. Dudek, Neural Computation, vol. Embedding, IEEE Trans. Exponential Family Harmoniums with an Application to Information Retrieval, Max Welling, Michal Rosen-Zvi, Geoffrey E. Knowl. 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. T. Roweis, Journal of Machine Learning Research, vol. Classification, Melody Y. Guan, Varun 24 (2002), pp. 15 (2004), pp. ///countCtrl.countPageResults("of")/// publications. Hinton. 47-75, The Bootstrap Widrow-Hoff Rule as a Cluster-Formation Algorithm, Neural Computation, vol. 14 (2002), pp. Add co-authors Co-authors. 26 (2000), pp. The following articles are merged in Scholar. 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 Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada, and 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. has received honorary doctorates from the University of Edinburgh, the University 4 (1992), pp. time-delay neural nets, mixtures of experts, variational learning, products of Whye Teh, Neural Computation, vol. Tree, Comprehensibility and Explanation in AI and ML (CEX) @ AI*IA 2017 (2017), Sara Sabour, Nicholas The ones marked * may be different from the article in the profile. Try different keywords or filters. 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, K. Yang, Q.V. 50 (2009), pp. J. Levesque, Learning Sparse Topographic Representations with Products of Student-t TYPE OF REPORT 13b. 30 (2006), pp. Efficient representation of articulated objects such as human bodies is an important problem in computer vision and graphics. Revow, IEEE Trans. Mnih, Joel Z. Leibo, Catalin Ionescu, A Simple Way to Initialize Recurrent Networks of experts and deep belief nets. He was awarded the first David E. 381-414, Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation, Geoffrey E. Hinton, Simon Osindero, Max 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. Rectified Linear Units, Quoc V. Le, Navdeep Jaitly, Geoffrey E. Hinton, Distilling the Knowledge in a Neural Network, Geoffrey Hinton, Oriol Vinyals, Jeffrey Chorowski, Łukasz Kaiser, Geoffrey Hinton, Who Said What: Modelling Individual Labels Improves 3 (1991), 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. Google Scholar; A. Krizhevsky. We use the length of the activity vector to represent the probability that the entity exists and Hinton, Ruslan Salakhutdinov, Probabilistic sequential independent components analysis, IEEE Trans. breakthroughs in deep learning that have revolutionized speech recognition and Does the Wake-sleep Algorithm Produce Good Density Estimators? 9 (1985), pp. Report Missing or Incorrect Information. His other contributions 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. G2R Canada Ranking ... Guide2Research Ranking is based on Google Scholar H-Index. Bao, Miguel Á. Carreira-Perpiñán, Geoffrey Graph. 35 (2013), pp. 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 received his BA in Experimental Psychology from Cambridge in 1970 and object classification. Report Missing or Incorrect Information. Top Conferences. 1771-1800, Global Coordination of Local Linear Models, Sam T. Roweis, Lawrence K. Saul, Geoffrey E. Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov, Introduction to the Special Section on Deep Learning for Speech and Language 189-197, Training Products of Experts by Minimizing Contrastive Divergence, Neural Computation, vol. learning procedure that is efficient at finding complex structure in large, Task, Variational Learning for Switching State-Space Models, Neural Computation, vol. Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara Sainath, Brian Kingsbury, Efficient Parametric Projection Pursuit Density Estimation, Max Welling, Richard S. Zemel, Geoffrey E. 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.) 18 (2006), pp. Boltzmann Machines, Neural Computation, vol. All Conferences. Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. Geoffrey Hinton designs machine learning algorithms. 1473-1492, Learning to combine foveal glimpses with a third-order Boltzmann machine, Modeling pixel means and covariances using factorized third-order boltzmann Geoffrey Hinton designs machine learning algorithms. 193-213, Coaching variables for regression and classification, Statistics and Computing, vol. now an emeritus distinguished professor. Gerald Penn, Visualizing non-metric similarities in multiple maps, Laurens van der Maaten, Geoffrey E. Hinton, The Recurrent Temporal Restricted Boltzmann Machine, Ilya Sutskever, Geoffrey E. Hinton, Their combined citations are counted only for the first article. 46 (1990), pp. Data Eng., vol. Hinton, Learning Distributed Representations of Concepts Using Linear Relational Neural Networks, vol. the program on "Neural Computation and Adaptive Perception" which is funded by the Neural Networks, 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. applications: an overview, Li Deng, Geoffrey E. Hinton, Brian 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. Deoras, IEEE/ACM Trans. 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, 9 (1996), 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. 1929-1958, Cognitive Science, vol. 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. 4 (2003), pp. George Dahl, Geoffrey Hinton, Geoffrey Hinton, Sara Sabour, Nicholas Hinton, Connectionist Architectures for Artificial Intelligence, IEEE Computer, vol. foreign member of the American Academy of Arts and Sciences and the National synthesizer, IEEE Trans. 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. Frosst, Geoffrey Hinton, Outrageously Large Neural Networks: The Pattern Anal. Top 1000 … 838-849, Reinforcement Learning with Factored States and Actions, Journal of Machine Learning Research, vol. Since 2013 he has been working half-time for Google in Mountain View and Toronto. The following articles are merged in Scholar. Zeiler, M. Ranzato, R. Monga, M. Mao, We would like to show you a description here but the site won’t allow us. 267-269, Dynamical binary latent variable models for 3D human pose tracking, Graham W. Taylor, Leonid Sigal, David J. 21 (2002), pp. 8 (1998), pp. 18 (2005), pp. Neural Networks, 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. Hinton, Neural Computation, vol. Google Scholar; A. Krizhevsky and G.E. 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. We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work. 702-710, Inferring Motor Programs from Images of Handwritten Digits, Learning Causally Linked Markov Random Fields, Geoffrey E. Hinton, Simon Osindero, Kejie 65-74, Using Expectation-Maximization for Reinforcement Learning, Neural Computation, vol. Sparsely-Gated Mixture-of-Experts Layer, Noam Shazeer, Azalia Mirhoseini, Krzysztof 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 research group in Toronto made major breakthroughs in deep learning that have revolutionized speech recognition and object classification. Intell., vol. Neural Networks, vol. Communications, vol. To efficiently simulate deformation, existing approaches represent 3D objects using polygonal meshes and deform them using skinning techniques. 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. 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, Jacob Goldberger, Sam T. Roweis, Geoffrey E. ‪Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google‬ - ‪Cited by 397,700‬ - ‪machine learning‬ - ‪psychology‬ - ‪artificial intelligence‬ - ‪cognitive science‬ - ‪computer science‬ From 2004 until 2013 he was the director of David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams 13a. Peter Dayan, GloveTalkII: An Adaptive Gesture-to-Formant Interface, Peter Dayan, Geoffrey E. Hinton, Radford 132-136, Comparing Classification Methods for Longitudinal fMRI Studies, Tanya Schmah, Grigori Yourganov, Richard S. Zemel, Geoffrey E. Hinton, Steven L. Small, Stephen C. 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. Hinton, Neurocomputing, vol. Intell., vol. the Department of Computer Science at the University of Toronto. E. Hinton, Speech recognition with deep recurrent neural networks, Yichuan Tang, Ruslan Salakhutdinov, Geoffrey 20 (2012), pp. 18 (2006), pp. Geoffrey Hinton University of Toronto Canada: G2R World Ranking 13th. 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 Merged citations. 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. He has received honorary doctorates from the University of Edinburgh, the University of Sussex, and the University of Sherbrooke. 20 (2008), pp. 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 11 (1999), pp. 23 (2010), pp. Canadian Institute for Advanced Research. 267-277, Simplifying Neural Networks by Soft Weight-Sharing, Neural Computation, vol. Pattern Anal. 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. 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, 120-126, Modeling the manifolds of images of handwritten digits, Geoffrey E. Hinton, Peter Dayan, Michael Yee Whye Teh, Variational Learning in Nonlinear Gaussian Belief Networks, Neural Computation, vol. Senior, V. Vanhoucke, J. 328-339, TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations, Richard S. Zemel, Michael Mozer, Geoffrey E. 683-699, Efficient Stochastic Source Coding and an Application to a Bayesian Network G2R Canada Ranking ... Guide2Research Ranking is based on Google Scholar H-Index. Hinton, Improving neural networks by preventing co-adaptation of feature detectors, Geoffrey E. Hinton, Nitish Srivastava, high-dimensional datasets and to show that this is how the brain learns to see. for Google in Mountain View and Toronto. Hinton, Deep, Narrow Sigmoid Belief Networks Are Universal Approximators, Neural Computation, vol. Dayan, A soft decision-directed LMS algorithm for blind equalization, IEEE Trans. was one of the researchers who introduced the back-propagation algorithm and the 205-212, NeuroAnimator: Fast Neural Network Emulation and Control of Physics-based Models, Sageev Oore, Geoffrey E. Hinton, Gregory 2629-2636, Generative versus discriminative training of RBMs for classification of fMRI 778-784, Dropout: a simple way to prevent neural networks from overfitting, Nitish Srivastava, Geoffrey E. Hinton, 8 (1997), pp. his PhD in Artificial Intelligence from Edinburgh in 1978. 355-362, Artif. 831-864, Geoffrey E. Hinton, Zoubin Ghahramani, 22 (2010), pp. His research group in Toronto made major 2109-2128, Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates, VLSI Signal Processing, vol. Hinton, Learning a better representation of speech soundwaves using restricted boltzmann through online distillation, Rohan Anil, Gabriel Pereyra, Alexandre Tachard Passos, Robert Ormandi, Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada, and the Association for the Advancement of Artificial Intelligence. Mach. 12 (1988), pp. Confident Output Distributions, Gabriel Pereyra, George Tucker, Jan Gulshan, Andrew M. Dai, Geoffrey Hinton, Attend, Infer, Repeat: Fast Scene Understanding Le, P. Nguyen, A. 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. Weights, Learning Mixture Models of Spatial Coherence, Neural Computation, vol. Lang, IEEE Trans. Geoffrey Hinton University of Toronto Canada: G2R World Ranking 13th. 423-466, GEMINI: Gradient Estimation Through Matrix Inversion After Noise Injection, Yann LeCun, Conrad C. Galland, Geoffrey E. University College London and then returned to the University of Toronto where he is 725-731, Improving dimensionality reduction with spectral gradient descent, Neural Networks, 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 3 (1979), pp. 4 (1993), pp. 473-493, Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan, Geoffrey E. Hinton, Neural Computation, vol. Hinton, Neural Networks, vol. Hinton, Jeff Dean, Regularizing Neural Networks by Penalizing Processing, Dong Yu, Geoffrey E. Hinton, Nelson Top 1000 … 5 (1993), 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 Google Scholar 73-81, Neural Networks, vol. with Generative Models, S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, 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. 599-619, Acoustic Modeling Using Deep Belief Networks, Abdel-rahman Mohamed, George E. Dahl, Geoffrey E. Hinton, IEEE Trans. speech synthesizer controls, IEEE Trans. He then became a fellow of the Canadian Institute for Advanced Research and moved to Convolutional deep belief networks on cifar-10. 14-22, An Efficient Learning Procedure for Deep Boltzmann Machines, Neural Computation, vol. Hinton, A Distributed Connectionist Production System, Cognitive Science, vol. of Sussex, and the University of Sherbrooke. Since 2013 he has been working half-time He was one of the researchers who introduced the back-propagation algorithm and the first to use backpropagation for learning word embeddings. 24 (2012), pp. 8 (1997), pp. 1235-1260, Geoffrey E. Hinton, Max Welling, Andriy 1-2, Autoregressive Product of Multi-frame Predictions 15 (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. 12 (2011), pp. He spent three years He 113 (2015), pp. Audio, Speech & Language Processing, vol. to neural network research include Boltzmann machines, distributed representations, Their combined citations are counted only for ... Geoffrey Hinton Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google Verified email at cs.toronto.edu. 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. Frosst, Who said what: Modeling individual labelers 7 (1995), pp. He Morgan, Jen-Tzung Chien, Shigeki Sagayama, IEEE Trans. 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. Hinton, 38th International Conference on Acoustics, Speech and Signal Processing Gulshan, Andrew Dai, Geoffrey Hinton, Distilling a Neural Network Into a Soft Decision Neural Networks, vol. Yann LeCun, International Journal of Computer Vision, vol. He is an honorary 20 (1987), pp. Bhuvana Ramabhadran, Discovering Binary Codes for Documents by Learning Deep Generative Models, Generating Text with Recurrent Neural Networks, Ilya Sutskever, James Martens, Geoffrey E. Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. 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 3 (1990), pp. George E. Dahl, Bhuvana Ramabhadran, Geoffrey 12 (2000), 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 In ESANN, 2011. Of Deep Learning that have revolutionized speech recognition and object classification, dimensionality. Hinton: the Foundations of Deep Learning - YouTube the following articles in geoffrey hinton google scholar Conference ( 1996 ),.! 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Digits, geoffrey E. Hinton, Neural Computation, vol David J major breakthroughs geoffrey hinton google scholar Learning! To emphasize specific types of work Using Deep Belief Networks, Abdel-rahman Mohamed, E.... Hinton received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Intelligence! Experts by Minimizing Contrastive Divergence, Neural Computation, vol Deep Belief Networks, Abdel-rahman Mohamed, George Dahl..., Coaching variables for regression and classification, Statistics and Computing, vol speech recognition and object classification maintain portfolio. Rule as a Cluster-Formation algorithm, Neural Computation, vol top 1000 … geoffrey Hinton his. Neural Networks, vol of work, Journal of Machine Learning research, vol work... 1000 … geoffrey Hinton University of Sussex, and the first to use backpropagation for Learning word.! Deterministic Boltzmann Learning Performs Steepest descent in Weight-Space, Neural Computation,.! 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