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Please note: This seminar will be given online.

Dallas Card, Postdoctoral scholar
NLP Group and the Data Science Institute, Stanford University

Machine learning and natural language processing have become increasingly influential, both in commercial applications and as key tools for research in the natural and social sciences. In both cases, however, research in these fields raises numerous concerns related to bias, transparency, robustness, and how we communicate information.

Please note: This seminar will be given online.

Hongyang Zhang, Postdoctoral Fellow
Toyota Technological Institute at Chicago

Deep learning models are often vulnerable to adversarial examples. In this talk, we will focus on robustness and security of machine learning against adversarial examples. There are two types of defenses against such attacks: 1) empirical and 2) certified adversarial robustness.

Please note: This master’s thesis presentation will be given online.

Kyle Robinson, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Dan Brown