Testing Arithmetic Circuits

Jun 14, 2019

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I will discuss Testing Arithmetic Circuits (TACs), which are new tractable probabilistic models that are universal function approximators like neural networks. A TAC represents a piecewise multilinear function and computes a marginal query on the newly introduced Testing Bayesian Network (TBN). The structure of a TAC is automatically compiled from a Bayesian network and its parameters are learned from labeled data using gradient descent. TACs can incorporate background knowledge that is encoded in the Bayesian network, whether conditional independence or domain constraints. Hence, the behavior of a TAC comes with some guarantees that are invariant to how it is trained from data. Moreover, a TAC is amenable to being interpretable since its nodes and parameters have precise meanings by virtue of being compiled from a Bayesian network. This recent work aims to fuse models (Bayesian networks) and functions (DNNs) with the goal of realizing their collective benefits.

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About ICML 2019

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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