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DTSTART;TZID=America/New_York:20250404T120000
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DTSTAMP:20260529T152305
CREATED:20241211T195214Z
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SUMMARY:Learning diffusion models in high-dimensions
DESCRIPTION:Member Seminar \nSpeaker: Hugo Cui \nTitle: Learning diffusion models in high-dimensions \nAbstract: We consider the problem of learning a generative model parametrized by a two-layer auto-encoder\, and trained with online stochastic gradient descent\, to sample from a high-dimensional data distribution with an underlying low-dimensional structure. We provide a tight asymptotic characterization of low-dimensional projections of the resulting generated density\, and evidence how mode(l) collapse can arise.  On the other hand\, we discuss how in a case where the architectural bias is suited to the target density\, these simple models can efficiently learn to sample from a binary Gaussian mixture target distribution. \n 
URL:https://cmsa.fas.harvard.edu/event/member-seminar-4425/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Member Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Member-Seminar-4.4.25.png
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