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DTSTAMP;TZID=America/Vancouver:20221206T140000
DTSTART;TZID=America/Vancouver:20221206T140000
DTEND;TZID=America/Vancouver:20221206T150000

UID:20221206T140000@prima2022.primamath.org
SUMMARY:Deep generative models and Schrödinger Bridge
DESCRIPTION:Deep generative models have achieved enormous success in learning the
underlying high-dimensional data distribution from samples. In this talk, we
will introduce two methods to learn deep generative models. First, we will
introduce variational gradient flow (VGrow) which can be used to minimize the
f-divergence between the evolving distribution and the target distribution. In
particular, we showed that the commonly used logD-trick indeed belongs to
f-divergence. Second, we will introduce a Schrödinger Bridge approach to
learning deep generative models. Our theoretical results guarantee that the
distribution learned by our approach converges to the target distribution.
Experimental results on multimodal synthetic data and benchmark data support our
theoretical findings and indicate that the generative model via Schrödinger
Bridge is comparable with state-of-the-art GANs, suggesting a new formulation of
generative learning. We demonstrate its usefulness in image interpolation and
image inpainting.

STATUS:CONFIRMED
LOCATION:Grand Ballroom
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