Resilient Representation and Provable Generalization

In this talk, I'll present the challenges in today's deep learning approach for learning representations resilient against attacks. I will also explore the question of providing provable guarantees of generalization of a learned model. As a concrete example, I will present our recent work on using recursion to enable provablely perfect generalization in the domain of neural program architectures.