Wang-Zhou Dai: Bridging Machine Learning and Logical Reasoning by Abductive Learning

Perception and reasoning are two representative abilities of intelligence that are integrated seamlessly during problem-solving processes. In the area of artificial intelligence (AI), perception is usually realised by machine learning and reasoning is often formalised by logic programming. However, the two categories of techniques were developed separately throughout most of the history of AI. This talk will introduce the abductive learning framework targeted at unifying the two AI paradigms in a mutually beneficial way. In this framework, machine learning models learn to perceive primitive logical facts from the raw data, while logical reasoning is able to correct the wrongly perceived facts for improving the machine learning models. We demonstrate that by using the abductive learning framework, computers can learn to recognise numbers and resolve equations with unknown arithmetic operations simultaneously from images of simple hand-written equations. Moreover, the learned models can be generalized to complex equations and adapted to different tasks, which is beyond the capability of state-of-the-art deep learning models.

Bio: Wang-Zhou Dai is a research associate in the Department of Computing, Imperial College London. His research interests lie in the area of Artificial Intelligence and machine learning, especially in applying first-order logical background knowledge in general machine learning techniques. He has published multiple research papers on major conferences and journals in AI and machine learning including AAAI, ILP, ICDM, ACML and Machine Learning, etc. He has been awarded the IBM PhD Fellowship and Google Excellence Scholarship during his PhD study, and now he is serving as a PC member and reviewer in many top AI & machine learning conferences.

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Видео Wang-Zhou Dai: Bridging Machine Learning and Logical Reasoning by Abductive Learning канала London Machine Learning Meetup