IDeAS Seminar: Oscar Mickelin, Massachusetts Institute of Technology (MIT)

Themes from numerical tensor calculus​

Low-rank tensor methods compress high-dimensional arrays into more manageable sizes. This circumvents the curse of dimensionality where storage and computational costs scale exponentially with the data dimension. Over the past decade, this has enabled advances in signal processing, numerical linear algebra, machine learning, and many other fields.

IDeAS Seminar: Yunpeng Shi, University of Minnesota

Robust Group Synchronization via Cycle-Edge Message Passing

We propose a general framework for group synchronization with adversarial corruption and sufficiently small noise. Specifically, we apply a novel message passing procedure that uses cycle consistency information in order to estimate the corruption levels of group ratios and consequently infers the corrupted group ratios and solves the synchronization problem. 


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