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Reasoning in Deep Learning
Date: 2017/3/22             Browse: 161
Speaker: Yuandong Tian

Title: Reasoning with Deep Learning in Games

Deep learning has been proven very successful in many applications that require advanced pattern matching. This includes computer vision, speech recognition, natural language processing and even on classic games such as Go. However, it remains an open problem whether Deep Learning will make the same amount of progress in tasks that require sophisticated reasoning, planning and decision making, in particular in complicated game environments that bear a resemblance to real-world applications. In this talk, I present our recent contributions on these directions, as well as discuss issues and challenges.

Yuandong Tian is a Research Scientist in Facebook AI Research, working on reasoning with deep learning in games and theoretical analysis of deep non-convex models. He is the leader researcher and engineer for DarkForest (Facebook Computer Go project). Prior to that, he was a Software Engineer/Researcher in Google Self-driving Car team during 2013-2014. He received Ph.D in Robotics Institute, Carnegie Mellon University on 2013, Bachelor and Master degree of Computer Science in Shanghai Jiao Tong University. He is the recipient of 2013 ICCV Marr Prize Honorable Mentions for his work on global optimal solution to nonconvex optimization in image alignment.

SIST-Seminar 17004