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X-WR-CALNAME:Advanced training of energy-based models
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UID:2022-02-14-ruiqi-gao@cml.ics.uci.edu
DTSTAMP:20220214T000000Z
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SUMMARY:[CML Seminar] Ruiqi Gao: Advanced training of energy-based models
LOCATION:Online (live stream)
DESCRIPTION:Ruiqi Gao\, Research Scientist\, Google Brain\n\nTitle: Advance
d training of energy-based models\n\nAbstract: Energy-based models (EBMs)
are an appealing class of probabilistic models that can be learned from un
labeled data\, but two challenges remain for training them on high-dimensi
onal datasets: maximum-likelihood learning requires expensive MCMC samplin
g\, and energy potentials learned with non-convergent MCMC can be highly b
iased. I present two algorithms to tackle these challenges: (1) Diffusion
Recovery Likelihood\, which tractably learns and samples from a sequence o
f EBMs trained on increasingly noisy versions of a dataset\, and (2) Flow
Contrastive Estimation\, which jointly estimates an EBM and a flow-based m
odel via a shared adversarial value function.\n\nhttps://cml.ics.uci.edu/s
eminars/2022-02-14-ruiqi-gao
X-ALT-DESC;FMTTYPE=text/html:Ruiqi Gao\, Research Scient
ist\, Google Brain
Title: Advanced training of energy-based
models
Abstract: Energy-based models (EBMs) are an appealing
class of probabilistic models that can be learned from unlabeled data\, b
ut two challenges remain for training them on high-dimensional datasets: m
aximum-likelihood learning requires expensive MCMC sampling\, and energy p
otentials learned with non-convergent MCMC can be highly biased. I present
two algorithms to tackle these challenges: (1) Diffusion Recovery Likelih
ood\, which tractably learns and samples from a sequence of EBMs trained o
n increasingly noisy versions of a dataset\, and (2) Flow Contrastive Esti
mation\, which jointly estimates an EBM and a flow-based model via a share
d adversarial value function.
https://cml.ics.uci.edu/seminars/2022-02-14-rui
qi-gao
URL:https://cml.ics.uci.edu/seminars/2022-02-14-ruiqi-gao
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