14.1k citations · 34.6k across the 53 of their papers we have counts for
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Efficient Parametric Projection Pursuit Density Estimation
Max Welling, Richard S. Zemel, Geoffrey E. Hinton
Product models of low dimensional experts are a powerful way to avoid the curse of dimensionality. We present the ``under-complete product of experts' (UPoE), where each expert mod…
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky +2
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 om…
Deep Lambertian Networks
Yichuan Tang, Ruslan Salakhutdinov, Geoffrey Hinton
Visual perception is a challenging problem in part due to illumination variations. A possible solution is to first estimate an illumination invariant representation before using it…
Deep Mixtures of Factor Analysers
Yichuan Tang, Ruslan Salakhutdinov, Geoffrey Hinton
An efficient way to learn deep density models that have many layers of latent variables is to learn one layer at a time using a model that has only one layer of latent variables. A…
Products of Hidden Markov Models: It Takes N>1 to Tango
Graham W Taylor, Geoffrey E. Hinton
Products of Hidden Markov Models(PoHMMs) are an interesting class of generative models which have received little attention since their introduction. This maybe in part due to thei…
Conditional Restricted Boltzmann Machines for Structured Output Prediction
Volodymyr Mnih, Hugo Larochelle, Geoffrey E. Hinton
Conditional Restricted Boltzmann Machines (CRBMs) are rich probabilistic models that have recently been applied to a wide range of problems, including collaborative filtering, clas…