Dimensionality reduction methods allow us to visualize the structure of large, highdimensional datasets by giving each datapoint a location in a twodimensional map. Sam Roweis was involved in the development of several different methods for producing maps that preserve local similarity by displaying very similar datapoints at nearby locations in the map without worrying too much about the map distances between dissimilar datapoints. One of these methods, called Stochastic Neighbor Embedding, converts the problem of finding a good map into the problem of matching two probability distributions. It uses the density under a highdimensional Gaussian centered at each datapoint to determine the probability of picking each of the other datapoints as a neighbor. It then uses exactly the same method to determine neighbor probabilities using the twodimensional locations of the corresponding map points. The aim is to move the map points so that the neighbor probabilities computed in the highdimensional dataspace are wellmodeled by the neighbor probabilities computed in the lowdimensional map. This leads to very nice maps for a variety of datasets. I will describe some further developments of this method that lead to even better maps.
Author Information
Geoffrey E Hinton (Google & University of Toronto)
Geoffrey Hinton received his PhD in Artificial Intelligence from Edinburgh in 1978 and spent five years as a faculty member at CarnegieMellon where he pioneered backpropagation, Boltzmann machines and distributed representations of words. In 1987 he became a fellow of the Canadian Institute for Advanced Research and moved to the University of Toronto. In 1998 he founded the Gatsby Computational Neuroscience Unit at University College London, returning to the University of Toronto in 2001. His group at the University of Toronto then used deep learning to change the way speech recognition and object recognition are done. He currently splits his time between the University of Toronto and Google. In 2010 he received the NSERC Herzberg Gold Medal, Canada's top award in Science and Engineering.
More from the Same Authors

2019 Poster: Lookahead Optimizer: k steps forward, 1 step back »
Michael Zhang · James Lucas · Jimmy Ba · Geoffrey E Hinton 
2019 Poster: Stacked Capsule Autoencoders »
Adam Kosiorek · Sara Sabour · Yee Whye Teh · Geoffrey E Hinton 
2019 Poster: When does label smoothing help? »
Rafael Müller · Simon Kornblith · Geoffrey E Hinton 
2019 Spotlight: When does label smoothing help? »
Rafael Müller · Simon Kornblith · Geoffrey E Hinton 
2018 Poster: Assessing the Scalability of BiologicallyMotivated Deep Learning Algorithms and Architectures »
Sergey Bartunov · Adam Santoro · Blake Richards · Luke Marris · Geoffrey E Hinton · Timothy Lillicrap 
2017 Poster: Dynamic Routing Between Capsules »
Sara Sabour · Nicholas Frosst · Geoffrey E Hinton 
2017 Spotlight: Dynamic Routing Between Capsules »
Sara Sabour · Nicholas Frosst · Geoffrey E Hinton 
2016 Poster: Attend, Infer, Repeat: Fast Scene Understanding with Generative Models »
S. M. Ali Eslami · Nicolas Heess · Theophane Weber · Yuval Tassa · David Szepesvari · koray kavukcuoglu · Geoffrey E Hinton 
2016 Poster: Using Fast Weights to Attend to the Recent Past »
Jimmy Ba · Geoffrey E Hinton · Volodymyr Mnih · Joel Leibo · Catalin Ionescu 
2016 Oral: Using Fast Weights to Attend to the Recent Past »
Jimmy Ba · Geoffrey E Hinton · Volodymyr Mnih · Joel Leibo · Catalin Ionescu 
2015 Poster: Grammar as a Foreign Language »
Oriol Vinyals · Łukasz Kaiser · Terry Koo · Slav Petrov · Ilya Sutskever · Geoffrey Hinton 
2015 Tutorial: Deep Learning »
Geoffrey E Hinton · Yoshua Bengio · Yann LeCun 
2014 Workshop: Deep Learning and Representation Learning »
Andrew Y Ng · Yoshua Bengio · Adam Coates · Roland Memisevic · Sharanyan Chetlur · Geoffrey E Hinton · Shamim Nemati · Bryan Catanzaro · Surya Ganguli · Herbert Jaeger · Phil Blunsom · Leon Bottou · Volodymyr Mnih · ChenYu Lee · Rich M Schwartz 
2012 Poster: ImageNet Classification with Deep Convolutional Neural Networks »
Alex Krizhevsky · Ilya Sutskever · Geoffrey E Hinton 
2012 Invited Talk: Dropout: A simple and effective way to improve neural networks »
Geoffrey E Hinton · George Dahl 
2012 Poster: A Better Way to PreTrain Deep Boltzmann Machines »
Russ Salakhutdinov · Geoffrey E Hinton 
2012 Spotlight: ImageNet Classification with Deep Convolutional Neural Networks »
Alex Krizhevsky · Ilya Sutskever · Geoffrey E Hinton 
2010 Workshop: Deep Learning and Unsupervised Feature Learning »
Honglak Lee · Marc'Aurelio Ranzato · Yoshua Bengio · Geoffrey E Hinton · Yann LeCun · Andrew Y Ng 
2010 Oral: Learning to combine foveal glimpses with a thirdorder Boltzmann machine »
Hugo Larochelle · Geoffrey E Hinton 
2010 Poster: Learning to combine foveal glimpses with a thirdorder Boltzmann machine »
Hugo Larochelle · Geoffrey E Hinton 
2010 Poster: Generating more realistic images using gated MRF's »
Marc'Aurelio Ranzato · Volodymyr Mnih · Geoffrey E Hinton 
2010 Poster: Phone Recognition with the MeanCovariance Restricted Boltzmann Machine »
George Dahl · Marc'Aurelio Ranzato · Abdelrahman Mohamed · Geoffrey E Hinton 
2010 Poster: Gated Softmax Classification »
Roland Memisevic · Christopher Zach · Geoffrey E Hinton · Marc Pollefeys 
2009 Workshop: Deep Learning for Speech Recognition and Related Applications »
Li Deng · Dong Yu · Geoffrey E Hinton 
2009 Poster: Replicated Softmax: an Undirected Topic Model »
Russ Salakhutdinov · Geoffrey E Hinton 
2009 Poster: 3D Object Recognition with Deep Belief Nets »
Vinod Nair · Geoffrey E Hinton 
2009 Spotlight: 3D Object Recognition with Deep Belief Nets »
Vinod Nair · Geoffrey E Hinton 
2009 Invited Talk: Deep Learning with Multiplicative Interactions »
Geoffrey E Hinton 
2009 Poster: Zeroshot Learning with Semantic Output Codes »
Mark M Palatucci · Dean Pomerleau · Geoffrey E Hinton · Tom Mitchell 
2008 Poster: Using matrices to model symbolic relationship »
Ilya Sutskever · Geoffrey E Hinton 
2008 Demonstration: Visualizing NIPS Cooperations using Multiple Maps tSNE »
Laurens van der Maaten · Geoffrey E Hinton 
2008 Spotlight: Using matrices to model symbolic relationship »
Ilya Sutskever · Geoffrey E Hinton 
2008 Poster: The Recurrent Temporal Restricted Boltzmann Machine »
Ilya Sutskever · Geoffrey E Hinton · Graham W Taylor 
2008 Poster: A Scalable Hierarchical Distributed Language Model »
Andriy Mnih · Geoffrey E Hinton 
2008 Poster: Implicit Mixtures of Restricted Boltzmann Machines »
Vinod Nair · Geoffrey E Hinton 
2008 Poster: Competing RBM density models for classification of fMRI images »
Tanya Schmah · Geoffrey E Hinton · Richard Zemel 
2007 Tutorial: Deep Belief Nets »
Geoffrey E Hinton 
2007 Poster: Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes »
Russ Salakhutdinov · Geoffrey E Hinton 
2007 Poster: Modeling image patches with a directed hierarchy of Markov random fields »
Simon Osindero · Geoffrey E Hinton 
2006 Poster: Modeling Human Motion Using Binary Latent Variables »
Graham W Taylor · Geoffrey E Hinton · Sam T Roweis 
2006 Spotlight: Modeling Human Motion Using Binary Latent Variables »
Graham W Taylor · Geoffrey E Hinton · Sam T Roweis