Stephan Mandt(University of California, Irvine)
hosted by Machine Learning Group of Prof. Marius Kloft
"From probabilistic forecasting to neural data compression and back: a latent variable perspective"
The past few years have seen deep generative models mature into promising applications.Two of these applications include neural data compression and forecasting high-dimensional time series, including video.I will begin by reviewing the basic ideas behind neural data compression and show how advances in approximate Bayesian inferenceand generative modeling can significantly improve the compression performance of existing models.Finally, I show how neural video codecs can inspire probabilistic forecasting,leading to probabilistic sequence prediction methods with high potential for data-driven weather prediction.
Bio: Stephan Mandt is an Assistant Professor of Computer Science and Statistics 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 in Pittsburgh and Los Angeles.He held previous postdoctoral positions at Columbia University and Princeton University.Stephan holds a Ph.D. in Theoretical Physics from the University of Cologne, where he received the German National Merit Scholarship.He is furthermore a receipient of the NSF CAREER Award, the UCI ICS Mid-Career Excellence in Research Award,a Kavli Fellow of the U.S. National Academy of Sciences, a member of the ELLIS Society, and a former visiting researcher at Google Brain.Stephan regularly serves as an Area Chair, Action Editor, or Editorial Board member for NeurIPS, ICML, AAAI, ICLR, TMLR, and JMLR.His research is currently supported by NSF, DARPA, DOE, Disney, Intel, and Qualcomm.
|Time:||Thursday, 14.07.2022, 11:00|