{"id":49064,"date":"2024-08-26T10:37:23","date_gmt":"2024-08-26T14:37:23","guid":{"rendered":"https:\/\/engineering.jhu.edu\/ams\/?post_type=tribe_events&p=49064"},"modified":"2025-01-30T08:40:32","modified_gmt":"2025-01-30T13:40:32","slug":"ams-weekly-seminar-associate-professor-justin-solomon","status":"publish","type":"tribe_events","link":"https:\/\/engineering.jhu.edu\/ams\/event\/ams-weekly-seminar-associate-professor-justin-solomon\/","title":{"rendered":"AMS Weekly Seminar | Justin Solomon"},"content":{"rendered":"

Location:\u00a0<\/strong>Gilman 50<\/p>\n

When:<\/strong>\u00a0March 6th at 1:30 p.m.<\/p>\n

Title:\u00a0<\/strong>Navigating, Restructuring and Reshaping Learned Latent Spaces<\/span><\/p>\n

Abstract:\u00a0<\/strong>Modern machine learning architectures often embed their inputs into a lower-dimensional latent space before generating a final output.\u00a0 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.\u00a0 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.\u00a0 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.<\/span><\/p>\n

Zoom link:<\/strong> https:\/\/wse.zoom.us\/j\/93287142219?pwd=z9fqWnRMzmzS0SGijRiie5yN3kHRSZ.1<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Location:\u00a0Gilman 50 When:\u00a0March 6th at 1:30 p.m. Title:\u00a0Navigating, Restructuring and Reshaping Learned Latent Spaces Abstract:\u00a0Modern machine learning architectures often embed their inputs into a lower-dimensional latent space before generating 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