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. 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. Zeiler, M. Ranzato, R. Monga, M. Mao, K. Yang, Q.V. 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. 1998  This page was last modified on 13 December 2008, at 09:45. This joint paper from the major speech recognition laboratories, summarizing . 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. 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. Ruslan Salakhutdinov, Andriy Mnih, Geoffrey E. Hinton: University of Toronto: 2007 : ICML (2007) 85 : 2 Modeling Human Motion Using Binary Latent Variables. Geoffrey Hinton. , Sallans, B., and Ghahramani, Z. Williams, C. K. I., Revow, M. and Hinton, G. E. Bishop, C. M., Hinton, G.~E. 5786, pp. GEMINI: Gradient Estimation Through Matrix Inversion After Noise Injection. 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. ... Hinton, G. E. & Salakhutdinov, R. Reducing the dimensionality of data with . Hinton, G.E. 1990  Thank you so much for doing an AMA! We use the length of the activity vector to represent the probability that the entity exists and its orientation to represent the instantiation parameters. 1991  TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations. and Hinton, G. E. Sutskever, I., Hinton, G.~E. 1. Verified … Research, Vol 5 (Aug), Spatial 1990  2007  2004  A Desktop Input Device and Interface for Interactive 3D Character Animation. A Fast Learning Algorithm for Deep Belief Nets. 1998  1996  Three new graphical models for statistical language modelling. 313. no. Yuecheng, Z., Mnih, A., and Hinton, G.~E. 2003  Reinforcement Learning with Factored States and Actions. Senior, V. Vanhoucke, J. 2006  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. Using Generative Models for Handwritten Digit Recognition. 2009  Autoencoders, Minimum Description Length and Helmholtz Free Energy. 1988  2010  Papers published by Geoffrey Hinton with links to code and results. Using Expectation-Maximization for Reinforcement Learning. 2001  1985  But Hinton says his breakthrough method should be dispensed with, and a … IEEE Signal Processing Magazine 29.6 (2012): 82-97. Discovering High Order Features with Mean Field Modules. Glove-TalkII-a neural-network interface which maps gestures to parallel formant speech synthesizer controls. Geoffrey E Hinton, Sara Sabour, Nicholas Frosst. Susskind,J., Memisevic, R., Hinton, G. and Pollefeys, M. Hinton, G. E., Krizhevsky, A. and Wang, S. Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space. 2018  Ennis M, Hinton G, Naylor D, Revow M, Tibshirani R. Grzeszczuk, R., Terzopoulos, D., and Hinton, G.~E. Building adaptive interfaces with neural networks: The glove-talk pilot study. Connectionist Architectures for Artificial Intelligence. 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:. 2003  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. ... Yep, I think I remember all of these papers. Variational Learning in Nonlinear Gaussian Belief Networks. Tagliasacchi, A. 2002  A Parallel Computation that Assigns Canonical Object-Based Frames of Reference. The recent success of deep networks in machine learning and AI, however, has … and Richard Durbin in the News and Views section 1994  Unsupervised Learning and Map Formation: Foundations of Neural Computation (Computational Neuroscience) by Geoffrey Hinton (1999-07-08) by Geoffrey Hinton | Jan 1, 1692 Paperback 1983-1976, Journal of Machine Learning 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 1983-1976, [Home Page] Adaptive Elastic Models for Hand-Printed Character Recognition. Le, 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. The backpropagation of error algorithm (BP) is often said to be impossible to implement in a real brain. [top] 1989  Keeping the Neural Networks Simple by Minimizing the Description Length of the Weights. [8] Hinton, Geoffrey, et al. Abstract: A capsule is a group of neurons whose outputs represent different properties of the same entity. 2005  The Machine Learning Tsunami. 2000  Science, Vol. 2016  Energy-Based Models for Sparse Overcomplete Representations. Recognizing Handwritten Digits Using Hierarchical Products of Experts. This is called the teacher model. Yoshua Bengio, (2014) - Deep learning and cultural evolution 1986  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. Hinton, G. E. (2007) To recognize shapes, first learn to generate images Andrew Brown, Geoffrey Hinton Products of Hidden Markov Models. 1991  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. A New Learning Algorithm for Mean Field Boltzmann Machines. 1984  1997  Developing Population Codes by Minimizing Description Length. In broad strokes, the process is the following. 2001  2004  Using Free Energies to Represent Q-values in a Multiagent Reinforcement Learning Task. Modeling High-Dimensional Data by Combining Simple Experts. Geoffrey Hinton interview. 2005  Mapping Part-Whole Hierarchies into Connectionist Networks. (2019). Restricted Boltzmann machines were developed using binary stochastic hidden units. 1994  [full paper ] [supporting online material (pdf) ] [Matlab code ] Papers on deep learning without much math. Hierarchical Non-linear Factor Analysis and Topographic Maps. 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 1989  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. 2013  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. Improving dimensionality reduction with spectral gradient descent. 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. 1999  Topographic Product Models Applied to Natural Scene Statistics. 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? Ghahramani, Z., Korenberg, A.T. and Hinton, G.E. of Nature. Browse State-of-the-Art Methods Trends About RC2020 Log In/Register; Get the weekly digest … 1988  Recognizing Handwritten Digits Using Mixtures of Linear Models. 1985  Kornblith, S., Norouzi, M., Lee, H. and Hinton, G. Anil, R., Pereyra, G., Passos, A., Ormandi, R., Dahl, G. and Hinton, 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. 2019  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, Connectionist Symbol Processing - Preface. 1997  504 - 507, 28 July 2006. Evaluation of Adaptive Mixtures of Competing Experts. Hinton, G. E. and Salakhutdinov, R. R. (2006) Reducing the dimensionality of data with neural networks. 1999  1995  We use the length of the activity vector to represent the probability that the entity exists and its orientation to represent the instantiation parameters. Restricted Boltzmann machines for collaborative filtering. Recognizing Hand-written Digits Using Hierarchical Products of Experts. Extracting Distributed Representations of Concepts and Relations from Positive and Negative Propositions. Does the Wake-sleep Algorithm Produce Good Density Estimators? 1993  They can be approximated efficiently by noisy, rectified linear units. Salakhutdinov, R. R. Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel-rahman Mohamed, Learning Distributed Representations of Concepts Using Linear Relational Embedding. 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. and Sejnowski, T.J. Sloman, A., Owen, D. Dean, G. Hinton. 2000  ,  Ghahramani, Z and Teh Y. W. Ueda, N. Nakano, R., Ghahramani, Z and Hinton, G.E. Each layer in a capsule network contains many capsules. Massively Parallel Architectures for AI: NETL, Thistle, and Boltzmann Machines. Bibtex » Metadata » Paper » Supplemental » Authors. Vision in Humans and Robots, Commentary by Graeme Mitchison The must-read papers, considered seminal contributions from each, are highlighted below: Geoffrey Hinton & Ilya Sutskever, (2009) - Using matrices to model symbolic relationship. 1993  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 2015  of Nature, Commentary from News and Views section Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google. The learning and inference rules for these "Stepped Sigmoid Units" are unchanged. Aside from his seminal 1986 paper on backpropagation, Hinton has invented several foundational deep learning techniques throughout his decades-long career. 15 Feb 2018 (modified: 07 Mar 2018) ICLR 2018 Conference Blind Submission Readers: Everyone. Training Products of Experts by Minimizing Contrastive Divergence. 1986  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. 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. Dimensionality Reduction and Prior Knowledge in E-Set Recognition. of Nature, Commentary by John Maynard Smith in the News and Views section 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. Introduction. A time-delay neural network architecture for isolated word recognition. Fast Neural Network Emulation of Dynamical Systems for Computer Animation. 1987  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]. In 1986, Geoffrey Hinton co-authored a paper that, three decades later, is central to the explosion of artificial intelligence. (Breakthrough in speech recognition) ⭐ ⭐ ⭐ ⭐ [9] Graves, Alex, Abdel-rahman Mohamed, and Geoffrey "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups." Salakhutdinov R. R, Mnih, A. and Hinton, G. E. Cook, J. 2012  Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara Sainath, Hinton., G., Birch, F. and O'Gorman, F. 1996  2002  One way to reduce the training time is to normalize the activities of the neurons. 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. But Hinton says his breakthrough method should be dispensed with, and a new … 415 People Used More Courses ›› View Course 2008  published a paper 1 showing how to train a deep neural network capable of recognizing handwritten digits with state-of-the-art precision (>98%). 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. Active capsules at one level make predictions, via transformation matrices, … Exponential Family Harmoniums with an Application to Information Retrieval. Discovering Multiple Constraints that are Frequently Approximately Satisfied. 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. Hinton currently splits his time between the University of Toronto and Google […] Geoffrey Hinton. 2007  Last week, Geoffrey Hinton and his team published two papers that introduced a completely new type of neural network based … In 1986, Geoffrey Hinton co-authored a paper that, three decades later, is central to the explosion of artificial intelligence. He holds a Canada Research Chairin Machine Learning, and is currently an advisor for the Learning in Machines & Brains pr… Discovering Viewpoint-Invariant Relationships That Characterize Objects. 2014  Ashburner, J. Oore, S., Terzopoulos, D. and Hinton, G. E. Hinton G. E., Welling, M., Teh, Y. W, and Osindero, S. Hinton, G.E. Train a large model that performs and generalizes very well. Local Physical Models for Interactive Character Animation. and Brian Kingsbury. 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. Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton. 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. Timothy P Lillicrap, Adam Santoro, Luke Marris, Colin J Akerman, Geoffrey Hinton During learning, the brain modifies synapses to improve behaviour. Furthermore, the paper created a boom in research into neural network, a component of AI. Variational Learning for Switching State-Space Models. https://hypatia.cs.ualberta.ca/reason/index.php/Researcher:Geoffrey_E._Hinton_(9746). A Distributed Connectionist Production System. “Read enough to develop your intuitions, then trust your intuitions.” Geoffrey Hinton is known by many to be the godfather of deep learning. Modeling Human Motion Using Binary Latent Variables. 1992  Geoffrey E. Hinton's Publicationsin Reverse Chronological Order, 2020  1992  Instantiating Deformable Models with a Neural Net. Using Pairs of Data-Points to Define Splits for Decision Trees. Learning Sparse Topographic Representations with Products of Student-t Distributions. Rate-coded Restricted Boltzmann Machines for Face Recognition. NeuroAnimator: Fast Neural Network Emulation and Control of Physics-based Models. 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 … In 2006, Geoffrey Hinton et al. 2006  P. Nguyen, A. Symbols Among the Neurons: Details of a Connectionist Inference Architecture. 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 1987  A Learning Algorithm for Boltzmann Machines. 1984  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!). 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 Training state-of-the-art, deep neural networks is computationally expensive. 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. 2017  The architecture they created beat state of the art results by an enormous 10.8% on the ImageNet challenge. You and Hinton, approximate Paper, spent many hours reading over that. Learning Translation Invariant Recognition in Massively Parallel Networks. Mohamed,A., Sainath, T., Dahl, G. E., Ramabhadran, B., Hinton, G. 1995  I’d encourage everyone to read the paper. 2011  Hello Dr. Hinton! 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. Efficient Stochastic Source Coding and an Application to a Bayesian Network Source Model.