Stochastic Spectral Descent for Restricted Boltzmann Machines
David Carlson, Volkan Cevher, Lawrence Carin
Abstract
Restricted Boltzmann Machines (RBMs) are widely used as building blocks for deep learning models. Learning typically proceeds by using stochastic gradient descent, and the gradients are estimated with sampling methods. However, the gradient estimation is a computational bottleneck, so better use of the gradients will speed up the descent algorithm. To this end, we first derive upper bounds on the RBM cost function, then show that descent methods can have natural ad- vantages by operating in the L∞and Shatten-∞norm. We introduce a new method called “Stochastic Spectral Descent” that updates parameters in the normed space. Empirical results show dramatic improvements over stochastic gradient descent, and have only have a fractional increase on the per-iteration cost.
BibTeX
@InProceedings{pmlr-v38-carlson15,
title = {{Stochastic Spectral Descent for Restricted Boltzmann Machines}},
author = {Carlson, David and Cevher, Volkan and Carin, Lawrence},
booktitle = {Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics},
pages = {111--119},
year = {2015},
editor = {Lebanon, Guy and Vishwanathan, S. V. N.},
volume = {38},
series = {Proceedings of Machine Learning Research},
address = {San Diego, California, USA},
month = {09--12 May},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v38/carlson15.pdf},
url = {https://proceedings.mlr.press/v38/carlson15.html},
abstract = {Restricted Boltzmann Machines (RBMs) are widely used as building blocks for deep learning models. Learning typically proceeds by using stochastic gradient descent, and the gradients are estimated with sampling methods. However, the gradient estimation is a computational bottleneck, so better use of the gradients will speed up the descent algorithm. To this end, we first derive upper bounds on the RBM cost function, then show that descent methods can have natural ad- vantages by operating in the L∞and Shatten-∞norm. We introduce a new method called “Stochastic Spectral Descent” that updates parameters in the normed space. Empirical results show dramatic improvements over stochastic gradient descent, and have only have a fractional increase on the per-iteration cost.}
}