2016  15 Feb 2018 (modified: 07 Mar 2018) ICLR 2018 Conference Blind Submission Readers: Everyone. Discovering Viewpoint-Invariant Relationships That Characterize Objects. 2006  A., Sutskever, I., Mnih, A. and Hinton , G. E. Taylor, G. W., Hinton, G. E. and Roweis, S. Hinton, G. E., Osindero, S., Welling, M. and Teh, Y. Osindero, S., Welling, M. and Hinton, G. E. Carreira-Perpignan, M. A. and Hinton. Verified … But Hinton says his breakthrough method should be dispensed with, and a … He was the founding director of the Gatsby Charitable Foundation Computational Neuroscience Unit at University College London, and is currently a professor in the computer science department at the University of Toronto. This was one of the leading computer science programs, with a particular focus on artificial intelligence going back to the work of Herb Simon and Allen Newell in the 1950s. 1986  and Strachan, I. D. G. Revow, M., Williams, C. K. I. and Hinton, G. E. Williams, C. K. I., Hinton, G. E. and Revow, M. Hinton, G. E., Dayan, P., Frey, B. J. and Neal, R. Dayan, P., Hinton, G. E., Neal, R., and Zemel, R. S. Hinton, G. E., Dayan, P., To, A. and Neal R. M. Revow, M., Williams, C.K.I, and Hinton, G.E. I’d encourage everyone to read the paper. Evaluation of Adaptive Mixtures of Competing Experts. 2019  Furthermore, the paper created a boom in research into neural network, a component of AI. Building adaptive interfaces with neural networks: The glove-talk pilot study. Salakhutdinov, R. R. Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel-rahman Mohamed, This is knowledge distillation in essence, which was introduced in the paper Distilling the Knowledge in a Neural Network by Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 1983-1976, [Home Page] Zeiler, M. Ranzato, R. Monga, M. Mao, K. Yang, Q.V. He holds a Canada Research Chairin Machine Learning, and is currently an advisor for the Learning in Machines & Brains pr… (Breakthrough in speech recognition) ⭐ ⭐ ⭐ ⭐ [9] Graves, Alex, Abdel-rahman Mohamed, and Geoffrey 2004  Senior, V. Vanhoucke, J. 2014  Mohamed,A., Sainath, T., Dahl, G. E., Ramabhadran, B., Hinton, G. Yoshua Bengio, (2014) - Deep learning and cultural evolution Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara Sainath, ,  Ghahramani, Z and Teh Y. W. Ueda, N. Nakano, R., Ghahramani, Z and Hinton, G.E. The specific contributions of this paper are as follows: we trained one of the largest convolutional neural networks to date on the subsets of ImageNet used in the ILSVRC-2010 and ILSVRC-2012 2007  Dean, G. Hinton. and Hinton, G. E. Sutskever, I., Hinton, G.~E. Abstract

We trained a large, deep convolutional neural network to classify the 1.3 million high-resolution images in the LSVRC-2010 ImageNet training set into the 1000 different classes. 1986  1995  G., & Dean, J. Pereyra, G., Tucker, T., Chorowski, J., Kaiser, L. and Hinton, G. E. Ba, J. L., Hinton, G. E., Mnih, V., Leibo, J. To do so I turned to the master Geoffrey Hinton and the 1986 Nature paper he co-authored where backpropagation was first laid out (almost 15000 citations!). 2002  Hinton, G. E. and Salakhutdinov, R. R. (2006) Reducing the dimensionality of data with neural networks. Improving dimensionality reduction with spectral gradient descent. Fast Neural Network Emulation of Dynamical Systems for Computer Animation. 1993  Z. and Ionescu, C. Ba, J. L., Kiros, J. R. and Hinton, G. E. Ali Eslami, S. M., Nicolas Heess, N., Theophane Weber, T., Tassa, Y., Szepesvari, D., Kavukcuoglu, K. and Hinton, G. E. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. and Salakhutdinov, R. Vinyals, O., Kaiser, L., Koo, T., Petrov, S., Sutskever, I., & Hinton, G. E. Sarikaya, R., Hinton, G. E. and Deoras, A. Jaitly, N., Vanhoucke, V. and Hinton, G. E. Srivastava, N., Salakhutdinov, R. R. and Hinton, G. E. Graves, A., Mohamed, A. and Hinton, G. E. Dahl, G. E., Sainath, T. N. and Hinton, G. E. M.D. 1985  Using Free Energies to Represent Q-values in a Multiagent Reinforcement Learning Task. Yuecheng, Z., Mnih, A., and Hinton, G.~E. Recognizing Handwritten Digits Using Hierarchical Products of Experts. Hinton, G. E., Plaut, D. C. and Shallice, T. Hinton, G. E., Williams, C. K. I., and Revow, M. Jacobs, R., Jordan, M. I., Nowlan. 1991  And I think some of the algorithms you use today, or some of the algorithms that lots of people use almost every day, are what, things like dropouts, or I guess activations came from your group? The Machine Learning Tsunami. A Parallel Computation that Assigns Canonical Object-Based Frames of Reference. I have a few questions, feel free to answer one or any of them: In a previous AMA, Dr. Bradley Voytek, professor of neuroscience at UCSD, when asked about his most controversial opinion in neuroscience, citing Bullock et al., writes:. Three new graphical models for statistical language modelling. We use the length of the activity vector to represent the probability that the entity exists and its orientation to represent the instantiation parameters. Geoffrey E. Hinton's Publicationsin Reverse Chronological Order, 2020  Connectionist Symbol Processing - Preface. NeuroAnimator: Fast Neural Network Emulation and Control of Physics-based Models. In broad strokes, the process is the following. Salakhutdinov R. R, Mnih, A. and Hinton, G. E. Cook, J. In 2006, Geoffrey Hinton et al. The backpropagation of error algorithm (BP) is often said to be impossible to implement in a real brain. Mohamed, A., Dahl, G. E. and Hinton, G. E. Suskever, I., Martens, J. and Hinton, G. E. Ranzato, M., Susskind, J., Mnih, V. and Hinton, G. Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space. Learning Sparse Topographic Representations with Products of Student-t Distributions. Rate-coded Restricted Boltzmann Machines for Face Recognition. Published as a conference paper at ICLR 2018 MATRIX CAPSULES WITH EM ROUTING Geoffrey Hinton, Sara Sabour, Nicholas Frosst Google Brain Toronto, Canada fgeoffhinton, sasabour, frosstg@google.com ABSTRACT A capsule is a group of neurons whose outputs represent different properties of the same entity. Research, Vol 5 (Aug), Spatial Le, 1996  and Sejnowski, T.J. Sloman, A., Owen, D. A Fast Learning Algorithm for Deep Belief Nets. Bibtex » Metadata » Paper » Supplemental » Authors. [8] Hinton, Geoffrey, et al. Andrew Brown, Geoffrey Hinton Products of Hidden Markov Models. published a paper 1 showing how to train a deep neural network capable of recognizing handwritten digits with state-of-the-art precision (>98%). 2015  2003  G. E. Goldberger, J., Roweis, S., Salakhutdinov, R and Hinton, G. E. Welling, M,, Rosen-Zvi, M. and Hinton, G. E. Bishop, C. M. Svensen, M. and Hinton, G. E. Teh, Y. W, Welling, M., Osindero, S. and Hinton G. E. Welling, M., Zemel, R. S., and Hinton, G. E. Welling, M., Hinton, G. E. and Osindero, S. Friston, K.J., Penny, W., Phillips, C., Kiebel, S., Hinton, G. E., and 1. Dimensionality Reduction and Prior Knowledge in E-Set Recognition. GEMINI: Gradient Estimation Through Matrix Inversion After Noise Injection. 1991  A Desktop Input Device and Interface for Interactive 3D Character Animation. 1997  Recognizing Hand-written Digits Using Hierarchical Products of Experts. Training state-of-the-art, deep neural networks is computationally expensive. Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google. Hinton, G. E. (2007) To recognize shapes, first learn to generate images Symbols Among the Neurons: Details of a Connectionist Inference Architecture. ... Hinton, G. E. & Salakhutdinov, R. Reducing the dimensionality of data with . Hinton, G.E. “Read enough to develop your intuitions, then trust your intuitions.” Geoffrey Hinton is known by many to be the godfather of deep learning. [full paper ] [supporting online material (pdf) ] [Matlab code ] Papers on deep learning without much math. and Richard Durbin in the News and Views section They branded this technique “Deep Learning.” Training a deep neural net was widely considered impossible at the time, 2 and most researchers had abandoned the idea since the 1990s. Energy-Based Models for Sparse Overcomplete Representations. 1999  Does the Wake-sleep Algorithm Produce Good Density Estimators? The must-read papers, considered seminal contributions from each, are highlighted below: Geoffrey Hinton & Ilya Sutskever, (2009) - Using matrices to model symbolic relationship. A time-delay neural network architecture for isolated word recognition. 2012  1989  1988  2010  Exponential Family Harmoniums with an Application to Information Retrieval. Local Physical Models for Interactive Character Animation. IEEE Signal Processing Magazine 29.6 (2012): 82-97. Hierarchical Non-linear Factor Analysis and Topographic Maps. Hinton currently splits his time between the University of Toronto and Google […] Training Products of Experts by Minimizing Contrastive Divergence. 1994  In 1986, Geoffrey Hinton co-authored a paper that, three decades later, is central to the explosion of artificial intelligence. Susskind,J., Memisevic, R., Hinton, G. and Pollefeys, M. Hinton, G. E., Krizhevsky, A. and Wang, S. of Nature, Commentary from News and Views section 2000  Aside from his seminal 1986 paper on backpropagation, Hinton has invented several foundational deep learning techniques throughout his decades-long career. Geoffrey Hinton, one of the authors of the paper, would also go on and play an important role in Deep Learning, which is a field of Machine Learning, part of Artificial Intelligence. A paradigm shift in the field of Machine Learning occurred when Geoffrey Hinton, Ilya Sutskever, and Alex Krizhevsky from the University of Toronto created a deep convolutional neural network architecture called AlexNet[2]. Learning Distributed Representations of Concepts Using Linear Relational Embedding. TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations. Discovering Multiple Constraints that are Frequently Approximately Satisfied. Instantiating Deformable Models with a Neural Net. By the time the papers with Rumelhart and William were published, Hinton had begun his first faculty position, in Carnegie-Mellon’s computer science department. 1988  2005  One way to reduce the training time is to normalize the activities of the neurons. 2007  G. E. Guan, M. Y., Gulshan, V., Dai, A. M. and Hinton, G. E. Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, 1987  Topographic Product Models Applied to Natural Scene Statistics. Restricted Boltzmann machines for collaborative filtering. Deng, L., Hinton, G. E. and Kingsbury, B. Ranzato, M., Mnih, V., Susskind, J. and Hinton, G. E. Sutskever, I., Martens, J., Dahl, G. and Hinton, G. E. Tang, Y., Salakhutdinov, R. R. and Hinton, G. E. Krizhevsky, A., Sutskever, I. and Hinton, G. E. Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I. and 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. This page was last modified on 13 December 2008, at 09:45. Using Expectation-Maximization for Reinforcement Learning. 2001  A Distributed Connectionist Production System. 2009  Geoffrey E Hinton, Sara Sabour, Nicholas Frosst. 2005  Modeling Human Motion Using Binary Latent Variables. Using Generative Models for Handwritten Digit Recognition. Qin, Y., Frosst, N., Sabour, S., Raffel, C., Cottrell, C. and Hinton, G. Kosiorek, A. R., Sabour, S., Teh, Y. W. and Hinton, G. E. Zhang, M., Lucas, J., Ba, J., and Hinton, G. E. Deng, B., Kornblith, S. and Hinton, G. (2019), Deng, B., Genova, K., Yazdani, S., Bouaziz, S., Hinton, G. and 1984  of Nature. 1995  1984  E. Ackley, D. H., Hinton, G. E., and Sejnowski, T. J. Hinton, G.~E., Sejnowski, T. J., and Ackley, D. H. Hammond, N., Hinton, G.E., Barnard, P., Long, J. and Whitefield, A. Ballard, D. H., Hinton, G. E., and Sejnowski, T. J. Fahlman, S.E., Hinton, G.E. Each layer in a capsule network contains many capsules. But Hinton says his breakthrough method should be dispensed with, and a new … 415 People Used More Courses ›› View Course Variational Learning for Switching State-Space Models. T. Jaakkola and T. Richardson eds., Proceedings of Artificial Intelligence and Statistics 2001, Morgan Kaufmann, pp 3-11 2001: Yee-Whye Teh, Geoffrey Hinton Rate-coded Restricted Boltzmann Machines for Face Recognition 2001  and Taylor, G. W. Schmah, T., Hinton, G.~E., Zemel, R., Small, S. and Strother, S. van der Maaten, L. J. P. and Hinton, G. E. Susskind, J.M., Hinton, G.~E., Movellan, J.R., and Anderson, A.K. Graham W. Taylor, Geoffrey E. Hinton, Sam T. Roweis: University of Toronto: 2006 : NIPS (2006) 55 : 1 A Fast Learning Algorithm for Deep Belief Nets. A New Learning Algorithm for Mean Field Boltzmann Machines. Abstract: A capsule is a group of neurons whose outputs represent different properties of the same entity. Variational Learning in Nonlinear Gaussian Belief Networks. 1998  Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton. 2003  You and Hinton, approximate Paper, spent many hours reading over that. Ennis M, Hinton G, Naylor D, Revow M, Tibshirani R. Grzeszczuk, R., Terzopoulos, D., and Hinton, G.~E. Geoffrey Hinton interview. 1987  Extracting Distributed Representations of Concepts and Relations from Positive and Negative Propositions. Restricted Boltzmann machines were developed using binary stochastic hidden units. Using Pairs of Data-Points to Define Splits for Decision Trees. 2008  We use the length of the activity vector to represent the probability that the entity exists and its orientation to represent the instantiation parameters. Developing Population Codes by Minimizing Description Length. Hinton., G., Birch, F. and O'Gorman, F. In 1986, Geoffrey Hinton co-authored a paper that, three decades later, is central to the explosion of artificial intelligence. https://hypatia.cs.ualberta.ca/reason/index.php/Researcher:Geoffrey_E._Hinton_(9746). Science, Vol. 1993  Modeling High-Dimensional Data by Combining Simple Experts. S. J. and Hinton, G. E. Waibel, A. Hanazawa, T. Hinton, G. Shikano, K. and Lang, K. LeCun, Y., Galland, C. C., and Hinton, G. E. Rumelhart, D. E., Hinton, G. E., and Williams, R. J. Kienker, P. K., Sejnowski, T. J., Hinton, G. E., and Schumacher, L. E. Sejnowski, T. J., Kienker, P. K., and Hinton, G. E. McClelland, J. L., Rumelhart, D. E., and Hinton, G. E. Rumelhart, D. E., Hinton, G. E., and McClelland, J. L. Hinton, G. E., McClelland, J. L., and Rumelhart, D. E. Rumelhart, D. E., Smolensky, P., McClelland, J. L., and Hinton, G. Browse State-of-the-Art Methods Trends About RC2020 Log In/Register; Get the weekly digest … We explore and expand the Soft Nearest Neighbor Loss to measure the entanglement of class manifolds in representation space: i.e., how close pairs of points from the same … Hello Dr. Hinton! Massively Parallel Architectures for AI: NETL, Thistle, and Boltzmann Machines. 2000  Connectionist Architectures for Artificial Intelligence. Learning Translation Invariant Recognition in Massively Parallel Networks. Kornblith, S., Norouzi, M., Lee, H. and Hinton, G. Anil, R., Pereyra, G., Passos, A., Ormandi, R., Dahl, G. and Hinton, Vision in Humans and Robots, Commentary by Graeme Mitchison Geoffrey Hinton. 1997  Mapping Part-Whole Hierarchies into Connectionist Networks. of Nature, Commentary by John Maynard Smith in the News and Views section Geoffrey Hinton HINTON@CS.TORONTO.EDU Department of Computer Science University of Toronto 6 King’s College Road, M5S 3G4 Toronto, ON, Canada Editor: Yoshua Bengio Abstract We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. Autoencoders, Minimum Description Length and Helmholtz Free Energy. Recognizing Handwritten Digits Using Mixtures of Linear Models. 2002  and Brian Kingsbury. Unsupervised Learning and Map Formation: Foundations of Neural Computation (Computational Neuroscience) by Geoffrey Hinton (1999-07-08) by Geoffrey Hinton | Jan 1, 1692 Paperback 2013  313. no. "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups." The architecture they created beat state of the art results by an enormous 10.8% on the ImageNet challenge. 1983-1976, Journal of Machine Learning Keeping the Neural Networks Simple by Minimizing the Description Length of the Weights. Efficient Stochastic Source Coding and an Application to a Bayesian Network Source Model. ... Yep, I think I remember all of these papers. Reinforcement Learning with Factored States and Actions. Timothy P Lillicrap, Adam Santoro, Luke Marris, Colin J Akerman, Geoffrey Hinton During learning, the brain modifies synapses to improve behaviour. Introduction. Discovering High Order Features with Mean Field Modules. This is called the teacher model. and Picheny, M. Memisevic, R., Zach, C., Pollefeys, M. and Hinton, G. E. Dahl, G. E., Ranzato, M., Mohamed, A. and Hinton, G. E. Deng, L., Seltzer, M., Yu, D., Acero, A., Mohamed A. and Hinton, G. Taylor, G., Sigal, L., Fleet, D. and Hinton, G. E. Ranzato, M., Krizhevsky, A. and Hinton, G. E. Mohamed, A. R., Dahl, G. E. and Hinton, G. E. Palatucci, M, Pomerleau, D. A., Hinton, G. E. and Mitchell, T. Heess, N., Williams, C. K. I. and Hinton, G. E. Zeiler, M.D., Taylor, G.W., Troje, N.F. [top] A Learning Algorithm for Boltzmann Machines. 1999  1998  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. Thank you so much for doing an AMA! 1990  1992  Glove-TalkII-a neural-network interface which maps gestures to parallel formant speech synthesizer controls. 1996  2006  This joint paper from the major speech recognition laboratories, summarizing . This paper, titled “ImageNet Classification with Deep Convolutional Networks”, has been cited a total of 6,184 times and is widely regarded as one of the most influential publications in the field. The recent success of deep networks in machine learning and AI, however, has … 1989  Last week, Geoffrey Hinton and his team published two papers that introduced a completely new type of neural network based … Active capsules at one level make predictions, via transformation matrices, … P. Nguyen, A. After his PhD he worked at the University of Sussex, and (after difficulty finding funding in Britain) the University of California, San Diego, and Carnegie Mellon University. 2004  Tagliasacchi, A. These can be generalized by replacing each binary unit by an infinite number of copies that all have the same weights but have progressively more negative biases. Train a large model that performs and generalizes very well. (2019). Ruslan Salakhutdinov, Andriy Mnih, Geoffrey E. Hinton: University of Toronto: 2007 : ICML (2007) 85 : 2 Modeling Human Motion Using Binary Latent Variables. Geoffrey Hinton. In the cortex, synapses are embedded within multilayered networks, making it difficult to determine the effect of an individual synaptic modification on the behaviour of the system. Ghahramani, Z., Korenberg, A.T. and Hinton, G.E. , Sallans, B., and Ghahramani, Z. Williams, C. K. I., Revow, M. and Hinton, G. E. Bishop, C. M., Hinton, G.~E. 5786, pp. 1992  They can be approximated efficiently by noisy, rectified linear units. 2017  2018  Papers published by Geoffrey Hinton with links to code and results. 504 - 507, 28 July 2006. Ashburner, J. Oore, S., Terzopoulos, D. and Hinton, G. E. Hinton G. E., Welling, M., Teh, Y. W, and Osindero, S. Hinton, G.E. 1985  1990  The learning and inference rules for these "Stepped Sigmoid Units" are unchanged. 2011  1994  Adaptive Elastic Models for Hand-Printed Character Recognition.