Towards universal neural nets: Gibbs machines and ACE

Galin Georgiev

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arXiv · 1508.06585

Towards universal neural nets: Gibbs machines and ACE

Galin Georgiev

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Abstract

We study from a physics viewpoint a class of generative neural nets, Gibbs machines, designed for gradual learning. While including variational auto-encoders, they offer a broader universal platform for incrementally adding newly learned features, including physical symmetries. Their direct connection to statistical physics and information geometry is established. A variational Pythagorean theorem justifies invoking the exponential/Gibbs class of probabilities for creating brand new objects. Combining these nets with classifiers, gives rise to a brand of universal generative neural nets - stochastic auto-classifier-encoders (ACE). ACE have state-of-the-art performance in their class, both for classification and density estimation for the MNIST data set.

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Galin Georgiev. 2016-06-30. Towards universal neural nets: Gibbs machines and ACE. https://arxiv.org/abs/1508.06585

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