Geoffrey E. Hinton

ImageNet Classification with Deep Convolutional Neural Networks

Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton
NIPS
2012

We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we… (More)

A Fast Learning Algorithm for Deep Belief Nets

Geoffrey E. Hinton, Simon Osindero, Yee Whye Teh
Neural Computation
2006

We show how to use complementary priors to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary… (More)

Dropout: a simple way to prevent neural networks from overfitting

Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov
Journal of Machine Learning Research
2014

Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making… (More)

Reducing the dimensionality of data with neural networks.

Geoffrey E. Hinton, Ruslan Salakhutdinov
Science
2006

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. Gradient descent can… (More)

Learning representations by back-propagating errors

David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams
Nature
1986

We describe a new learning procedure, back-propagation, for networks of neurone-like units. The procedure repeatedly adjusts the weights of the connections in the network so as to minimize a measure… (More)

Training Products of Experts by Minimizing Contrastive Divergence

Geoffrey E. Hinton
Neural Computation
2002

It is possible to combine multiple latent-variable models of the same data by multiplying their probability distributions together and then renormalizing. This way of combining individual expert… (More)

Improving neural networks by preventing co-adaptation of feature detectors

Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov
ArXiv
2012

When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This “overfitting” is greatly reduced by randomly omitting half of the… (More)

Visualizing Data using t-SNE

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. The technique is a variation of Stochastic… (More)

Rectified Linear Units Improve Restricted Boltzmann Machines

Restricted Boltzmann machines were developed using binary stochastic hidden units. These can be generalized by replacing each binary unit by an infinite number of copies that all have the same… (More)

A Practical Guide to Training Restricted Boltzmann Machines

Geoffrey E. Hinton
Neural Networks: Tricks of the Trade
2012

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