Deep networks are mysterious. These over-parametrized machine learning models, trained with rudimentary optimization algorithms on non-convex landscapes in millions of dimensions have defied attempts to put a sound theoretical footing beneath their impressive performance. This talk will shed light upon some of these mysteries. I will employ diverse ideas—from thermodynamics and optimal transportation to partial differential equations, control theory and Bayesian inference—and paint a picture of the training process of deep networks. Along the way, I will develop state-of-the-art algorithms for non-convex optimization. The goal of machine perception is not just to classify objects in images but instead, enable intelligent agents that can seamlessly interact with our physical world. I will conclude with a vision of how advances in machine learning and robotics may come together to help build such an Embodied Intelligence.
Pratik Chaudhari is a PhD candidate in Computer Science at UCLA where he works with Stefano Soatto. His research interests include deep learning, robotics and computer vision. He has worked on perception and control algorithms for safe autonomous urban driving as a part of nuTonomy Inc. Pratik holds Master’s and Engineer’s degrees from MIT and a Bachelor’s degree from IIT Bombay in Aeronautics and Astronautics.