Upcoming Events › Stochastics and Statistics Seminar Series

Abstract:  Self-supervised learning is an increasingly popular approach for learning representations of data that can be used for downstream representation tasks. A practical advantage of self-supervised learning is that it can be used on unlabeled data. However, even when labels are available, self-supervised learning can be competitive with the more "traditional" approach of supervised learning.   In this talk we consider "self supervised + simple classifier (SSS)" algorithms, which are obtained by first learning a self-supervised classifier on data, and…