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SUMMARY:CIS Seminar: "Towards a New Synthesis of Reasoning and Learning"
DESCRIPTION:This talk discusses the role of logical reasoning in statistical machine learning. While their unification has been a long-standing and crucial open problem\, automated reasoning and machine learning are still disparate fields within artificial intelligence. I will describe recent progress towards their synthesis in three facets.\nI start with a very practical question: how can we enforce logical constraints on the output of deep neural networks to incorporate symbolic knowledge? Second\, I explain how circuits developed for tractable logical reasoning can be turned into statistical models. When brought to bear on a variety of machine learning tasks\, including discrete density estimation and simple image classification\, these probabilistic and logistic circuits yield state-of-the-art results. In a third facet\, I argue for high-level representations of uncertainty\, such as probabilistic programs\, probabilistic databases\, and statistical relational models. These pose unique challenges for inference that can only be overcome by high-level reasoning about their first-order structure to exploit symmetry and exchangeability.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-towards-a-new-synthesis-of-reasoning-and-learning/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
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