AMS Weekly Seminar | Justin Solomon

Location: Gilman 50

When: March 6th at 1:30 p.m.

Title: Navigating, Restructuring and Reshaping Learned Latent Spaces

Abstract: Modern machine learning architectures often embed their inputs into a lower-dimensional latent space before generating a final output.  A vast set of empirical results—and some emerging theory—predicts that these lower-dimensional codes often are highly structured, capturing lower-dimensional variation in the data.  Based on this observation, in this talk I will describe efforts in my group to develop lightweight algorithms that navigate, restructure, and reshape learned latent spaces.  Along the way, I will consider a variety of practical problems in machine learning, including low-rank adaptation of large models, regularization to promote local latent structure, and efficient training/evaluation of generative models.

Zoom link: https://wse.zoom.us/j/93287142219?pwd=z9fqWnRMzmzS0SGijRiie5yN3kHRSZ.1