Deep learning has been proven very successful in many applications that require advanced pattern matching, including computer vision. However, it is still unclear how deep learning could be involved in other tasks such as logic reasoning. In this talk, I introduce two of our recent works on this direction, Visual Question and Answering and Computer Go. We show that with different architecture, we could achieve state-of-the-art performance against existing approaches.
Yuandong Tian is a Research Scientist in Facebook AI Research, working on Deep Learning and Computer Vision. Prior to that, he was a Software Engineer in Google Self-driving Car team in 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.