6.7k citations · 6.8k across the 6 of their papers we have counts for
6 papers
Speech Recognition with Deep Recurrent Neural Networks
Alex Graves, Abdel-rahman Mohamed, Geoffrey Hinton
Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs…
Discovering Multiple Constraints that are Frequently Approximately Satisfied
Geoffrey E. Hinton, Yee Whye Teh
Some high-dimensional data.sets can be modelled by assuming that there are many different linear constraints, each of which is Frequently Approximately Satisfied (FAS) by the data.…
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…