Geoffrey E. Hinton

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)

Reducing the dimensionality of data with neural networks.

Geoffrey E. Hinton, Ruslan R. 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)

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)

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)

A View of the Em Algorithm That Justifies Incremental, Sparse, and Other Variants

Radford M. Neal, Geoffrey E. Hinton
1998

The EM algorithm performs maximum likelihood estimation for data in which some variables are unobserved. We present a function that resembles negative free energy and show that the M step maximizes… (More)

Neighbourhood Components Analysis

Jacob Goldberger, Sam T. Roweis, Geoffrey E. Hinton, Ruslan Salakhutdinov
NIPS
2004

In this paper we propose a novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm. The algorithm directly maximizes a stochastic variant of the… (More)

Parallel Distributed Processing

J L Mcclelland, Geoffrey E. Hinton
1986

What makes people smarter than machines? They certainly are not quicker or more precise. Yet people are far better at perceiving objects in natural scenes and noting their relations , at… (More)

A Practical Guide to Training Restricted Boltzmann Machines

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

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)

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