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arXiv · 1711.02679

Neural Variational Inference and Learning in Undirected Graphical Models

Volodymyr Kuleshov·Stefano Ermon
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Abstract

Many problems in machine learning are naturally expressed in the language of undirected graphical models. Here, we propose black-box learning and inference algorithms for undirected models that optimize a variational approximation to the log-likelihood of the model. Central to our approach is an upper bound on the log-partition function parametrized by a function q that we express as a flexible neural network. Our bound makes it possible to track the partition function during learning, to speed-up sampling, and to train a broad class of hybrid directed/undirected models via a unified variational inference framework. We empirically demonstrate the effectiveness of our method on several popular generative modeling datasets.

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BibTeXRIS

Volodymyr Kuleshov, Stefano Ermon. 2017-11-07. Neural Variational Inference and Learning in Undirected Graphical Models. https://arxiv.org/abs/1711.02679

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