Jun 14, 2019
A wide class of machine learning algorithms can be reduced to variable elimination on factor graphs. While factor graphs provide a unifying notation for these algorithms, they do not provide a compact way to express repeated structure when compared to plate diagrams for directed graphical models. In this talk, I will describe a generalization of undirected factor graphs to plated factor graphs, and a corresponding generalization of the variable elimination algorithm that exploits efficient tensor algebra in graphs with plates of variables. This tensor variable elimination algorithm has been integrated into the Pyro probabilistic programming language, enabling scalable, automated exact inference in a wide variety of deep generative models with repeated discrete latent structure. I will discuss applications of such models to polyphonic music modeling, animal movement modeling, and unsupervised word-level sentiment analysis, as well as algorithmic applications to exact subcomputations in approximate inference and ongoing work on extensions to continuous latent variables.
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
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