Tuesday, March 31, 2020 - 4:30pm to 5:30pm
Event Calendar Category
LIDS Seminar Series
Our machine learning models have attained impressive accuracy on many benchmark tasks. Yet, these models remain remarkably brittle---small perturbations of natural inputs can completely degrade their performance.
Why is this the case?
In this talk, we show that this brittleness can, in part, be attributed to the fact that our models often make decisions in a very different way than humans do. Viewing neural networks as feature extractors, we study how features extracted by neural networks may diverge from those used by humans, and how adversarially robust models seem to make progress towards bridging this gap.
Aleksander Madry is a Professor of Computer Science in the MIT EECS Department and a Principal Investigator in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from MIT in 2011 and, prior to joining the MIT faculty, he spent some time at Microsoft Research New England and on the faculty of EPFL.
Aleksander's research interests span algorithms, continuous optimization, the science of deep learning, and understanding machine learning from a robustness perspective. His work has been recognized with a number of awards, including an NSF CAREER Award, an Alfred P. Sloan Research Fellowship, an ACM Doctoral Dissertation Award Honorable Mention, and Presburger Award.
Join Zoom Meeting https://mit.zoom.us/j/268033196
Meeting ID 268 033 196
Join by SIP 268033196[at]zoomcrc[dot]com
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