第42回統計的機械学習セミナー / The 42nd Statistical Machine Learning Seminar

Date&Time
2018年7月19日(木)
/ 19 July, 2018 (Thu) 15:00

Admission Free,No Booking Necessary

Place
統計数理研究所 セミナー室1 (3階, D305)
/ Seminar room 1 (3F, D305) @ The Institute of Statistical Mathematics
区切り線
Speaker
Alexandros Dimakis (University of Texas at Austin)
Title
Generative Adversarial Networks (GANs) and Compressed Sensing.
Abstract

The goal of compressed sensing is to estimate a vector from an underdetermined system of noisy linear measurements, by making use of prior knowledge on the structure of vectors in the relevant domain. For almost all results in this literature, the structure is represented by sparsity in a well-chosen basis. We show how to achieve guarantees similar to standard compressed sensing but without employing sparsity at all. Instead, we suppose that vectors lie near the range of a generative model, e.g. a GAN or a VAE. We show how the problems of image inpainting and super-resolution are special cases of our general framework.

We show how to generalize the RIP condition for generative models and that random gaussian measurement matrices have this property with high probability. A Lipschitz condition for the generative neural network is a key technical condition.
We will also discuss on-going work for adding causality and distributed training to these models.

(based on joint work with Ashish Bora, Ajil Jalal and Eric Price)
Code

Bio

Alex Dimakis is an Associate Professor at the ECE department, University of Texas at Austin. He received his Ph.D. in 2008 from UC Berkeley and the Diploma degree from the National Technical University of Athens in 2003. During 2009 he was a CMI postdoctoral scholar at Caltech.

He received an NSF Career award, a Google faculty research award and the Eli Jury dissertation award. He is the co-recipient of several best paper awards including the joint Information Theory and Communications Society Best Paper Award in 2012. He is currently serving as an associate editor for IEEE Transactions on Information Theory. His research interests include information theory, coding theory and machine learning.