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X-WR-CALNAME:Deep Generative Models in Infinite-Dimensional Spaces
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UID:2024-01-29-gavin-kerrigan@cml.ics.uci.edu
DTSTAMP:20240129T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20240129T130000
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SUMMARY:[CML Seminar] Gavin Kerrigan: Deep Generative Models in Infinite-Di
mensional Spaces
LOCATION:Donald Bren Hall 4011
DESCRIPTION:Gavin Kerrigan\, PhD Student\, Department of Computer Science\,
UC Irvine\n\nTitle: Deep Generative Models in Infinite-Dimensional Spaces
\n\nAbstract: Deep generative models have seen a meteoric rise in capabili
ties across a wide array of domains\, ranging from natural language and vi
sion to scientific applications such as precipitation forecasting and mole
cular generation. However\, a number of important applications focus on da
ta which is inherently infinite-dimensional\, such as time-series\, soluti
ons to partial differential equations\, and audio signals. This relatively
under-explored class of problems poses unique theoretical and practical c
hallenges for generative modeling. In this talk\, we will explore recent d
evelopments for infinite-dimensional generative models\, with a focus on d
iffusion-based methodologies.\n\nhttps://cml.ics.uci.edu/seminars/2024-01-
29-gavin-kerrigan
X-ALT-DESC;FMTTYPE=text/html:Gavin Kerrigan\, PhD Studen
t\, Department of Computer Science\, UC Irvine
Title: Deep G
enerative Models in Infinite-Dimensional Spaces
Abstract: De
ep generative models have seen a meteoric rise in capabilities across a wi
de array of domains\, ranging from natural language and vision to scientif
ic applications such as precipitation forecasting and molecular generation
. However\, a number of important applications focus on data which is inhe
rently infinite-dimensional\, such as time-series\, solutions to partial d
ifferential equations\, and audio signals. This relatively under-explored
class of problems poses unique theoretical and practical challenges for ge
nerative modeling. In this talk\, we will explore recent developments for
infinite-dimensional generative models\, with a focus on diffusion-based m
ethodologies.
https://cml.ics.uci.edu/seminars/2024-01-29-gavin-kerrigan
URL:https://cml.ics.uci.edu/seminars/2024-01-29-gavin-kerrigan
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