Past talk · AI/ML Seminar Series
Exploring the limits of lossy data compression with deep learning
PhD Student, Department of Computer Science, University of California, Irvine
- Date & time
- Monday, April 26, 2021 · 1:00 PM
- Location
- Online (live stream)
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 limitations: algorithmic improvements inspired by variational inference that push the performance limits of VAE-based lossy compression to a new state of the art on images; a new algorithm that compresses the variational posteriors of pre-trained latent variable models; and ongoing work exploring fundamental bounds on lossy compression using stochastic approximation.
About the speaker
Yibo Yang is a PhD student advised by Stephan Mandt in the Computer Science department at UC Irvine. His research interests include probability theory, information theory, and their applications in statistical machine learning.