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X-WR-CALNAME:Exploring the limits of lossy data compression with deep learn
ing
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UID:2021-04-26-yibo-yang@cml.ics.uci.edu
DTSTAMP:20210426T000000Z
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DTSTART;TZID=America/Los_Angeles:20210426T130000
DTEND;TZID=America/Los_Angeles:20210426T140000
SUMMARY:[CML Seminar] Yibo Yang: Exploring the limits of lossy data compres
sion with deep learning
LOCATION:Online (live stream)
DESCRIPTION:Yibo Yang\, PhD Student\, Department of Computer Science\, Univ
ersity of California\, Irvine\n\nTitle: Exploring the limits of lossy data
compression with deep learning\n\nAbstract: Probabilistic machine learnin
g\, particularly deep learning\, is reshaping data compression. Recent wor
k established a close connection between lossy compression and latent vari
able models such as variational autoencoders (VAEs). In this talk\, I give
an overview of learned data compression and present research addressing s
ome of its limitations: algorithmic improvements inspired by variational i
nference that push the performance limits of VAE-based lossy compression t
o a new state of the art on images\; a new algorithm that compresses the v
ariational posteriors of pre-trained latent variable models\; and ongoing
work exploring fundamental bounds on lossy compression using stochastic ap
proximation.\n\nhttps://cml.ics.uci.edu/seminars/2021-04-26-yibo-yang
X-ALT-DESC;FMTTYPE=text/html:Yibo Yang\, PhD Student\, D
epartment of Computer Science\, University of California\, Irvine
<
b>Title: Exploring the limits of lossy data compression with deep lear
ning
Abstract: Probabilistic machine learning\, particularly
deep learning\, is reshaping data compression. Recent work established a
close connection between lossy compression and latent variable models such
as variational autoencoders (VAEs). In this talk\, I give an overview of
learned data compression and present research addressing some of its limit
ations: algorithmic improvements inspired by variational inference that pu
sh the performance limits of VAE-based lossy compression to a new state of
the art on images\; a new algorithm that compresses the variational poste
riors of pre-trained latent variable models\; and ongoing work exploring f
undamental bounds on lossy compression using stochastic approximation.
https:
//cml.ics.uci.edu/seminars/2021-04-26-yibo-yang
URL:https://cml.ics.uci.edu/seminars/2021-04-26-yibo-yang
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