|
Despite their wide applicability, deep learning systems often fail to exactly capture simple "known" features of many problem domains, such as those governed by physical laws or those that incorporate decision-making procedures. In this talk, I will present methods for these types of structural constraints — such as those associated with decision making, optimization problems, or physical simulation — directly into the predictions of a deep network. Our tool for achieving this will be the use of so-called "implicit layers" in deep models: layers that are defined implicitly in terms of conditions we would like them to satisfy, rather than via explicit computation graphs. l discuss how we can use these layers to embed (exact) physical constraints, robust control criteria, and task-based objectives, all within modern deep learning models. I will also highlight several applications of this work in reinforcement learning, control, energy systems, and other settings, and discuss generalizations and directions for future work in the area. Zico Kolter is an Associate Professor in the Computer Science Department at Carnegie Mellon University, and also serves as Chief Scientist of AI Research for the Bosch Center for Artificial Intelligence. His work spans the intersection of machine learning and optimization, with a focus on developing more robust and rigorous methods in deep learning. In addition, he has worked in a number of application areas, highlighted by work on sustainability and smart energy systems. He is a recipient of the DARPA Young Faculty Award, a Sloan Fellowship, and Best Paper awards at NeurIPS, ICML (honorable mention), IJCAI, KDD, and PESGM.
|
