Making Deep Learning more Robust, Modular, and Efficient
, Carnegie Mellon University
Date: Wednesday, April 03, 2019
Time: 4:30 PM to 5:30 PM
Event Type: Seminar
Host: Aleksander Madry
Contact: Marcia G. Davidson, 617-253-3049, email@example.com
Speaker URL: None
TALK: Making Deep Learning more Robust, Modular, and Efficient
Deep learning is often seen as the "breakthrough" AI technology of recent years, revolutionizing areas spanning computer vision, natural language processing, and game playing. However, if we seek to deploy such systems in real-world, safety-critical domains, a starker reality emerges. Modern deep learning systems are brittle (sensitive to adversarial manipulation and a general lack of robustness), opaque (difficult to interpret and debug their components), and expensive (often requiring vastly more data than practical in real-world settings).
These failings are sometimes billed as an argument against deep learning as a whole. But in this talk, I will argue instead for new methods that can address these challenges, while preserving the fundamental benefits of deep learning (namely, end-to-end training of composable, differentiable architectures). First, I will discuss our approaches to designing provably robust deep networks using tools from convex relaxations and duality. I also highlight recent work on scaling these methods to much larger domains, including some initial work on provable robustness at ImageNet scale. Second, I will present our work on integrating more complex modules as interpretable layers within deep learning architectures. I show how modules such as optimization solvers, physical simulation, model-based control, and game equilibrium solvers can all be integrated as layers within a deep network, enabling more intuitive architectures that can learn from vastly less data. Last, I will highlight some additional ongoing directions and open questions in both these areas.
Zico Kolter is an Assistant 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 focuses on the intersection of machine learning and optimization, with a large focus on developing more robust, interpretable, 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, and best paper awards at KDD, PESGM, and IJCAI.
Created by Marcia G. Davidson at Friday, March 22, 2019 at 12:10 PM.