Jun 14, 2019
Sum-product networks (SPNs) are a prominent class of tractable probabilistic model, facilitating efficient marginalization, conditioning, and other inference routines. However, despite these attractive properties, SPNs have received rather little attention in the (probabilistic) deep learning community, which rather focuses on intractable models such as generative adversarial networks, variational autoencoders, normalizing flows, and autoregressive density estimators. In this talk, I discuss several recent endeavors which demonstrate that i) SPNs can be effectively used as deep learning models, and ii) that hybrid learning approaches utilizing SPNs and other deep learning models are in fact sensible and beneficial.
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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