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On the Convergence of Bound Optimization Algorithms
Ruslan R Salakhutdinov, Sam T Roweis, Zoubin Ghahramani
Many practitioners who use the EM algorithm complain that it is sometimes slow. When does this happen, and what can be done about it? In this paper, we study the general class of b…
Matrix reconstruction with the local max norm
Rina Foygel, Nathan Srebro, Ruslan Salakhutdinov
We introduce a new family of matrix norms, the "local max" norms, generalizing existing methods such as the max norm, the trace norm (nuclear norm), and the weighted or smoothed we…
Exploiting compositionality to explore a large space of model structures
Roger Grosse, Ruslan R Salakhutdinov, William T. Freeman +1
The recent proliferation of richly structured probabilistic models raises the question of how to automatically determine an appropriate model for a dataset. We investigate this que…
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…