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Compressing Variational Bayes

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Oct 06

Date and time: 6 October 2020, 5pm – 6pm
Compressing Variational Bayes
Speaker: Stephan Mandt, University of California, Irvine

Watch the recorded presentation here:


Abstract: Neural image compression algorithms have recently outperformed their classical counterparts in rate-distortion performance and show great potential to also revolutionize video coding. In this talk, I will show how recent innovations from approximate Bayesian inference and generative modelling can lead to dramatic performance improvements in compression. In particular, I will explain how sequential variational autoencoders can be converted into video codecs, how deep latent variable models can be compressed in post-processing with variable bitrates, and how iterative amortized inference can be used to achieve the world record in image compression performance.

Bio: Stephan Mandt is an Assistant Professor of Computer Science at the University of California, Irvine. From 2016 until 2018, he was a Senior Researcher and Head of the statistical machine learning group at Disney Research, first in Pittsburgh and later in Los Angeles. He held previous postdoctoral positions at Columbia University and Princeton University. Stephan holds a PhD in Theoretical Physics from the University of Cologne. He is a Fellow of the German National Merit Foundation, a Kavli Fellow of the U.S. National Academy of Sciences, and was a visiting researcher at Google Brain. Stephan regularly serves as an Area Chair for NeurIPS, ICML, AAAI, and ICLR, and is a member of the Editorial Board of JMLR. His research is currently supported by NSF, DARPA, Intel, and Qualcomm.

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