EE Systems Seminar
Additivity of Information in Multilayer Networks
ABSTRACT: Multilayer (or deep) networks are powerful probabilistic models based on multiple stages of a linear transform followed by a non-linear (possibly random) function. These models have gained great popularity due to their ability to characterize complex probabilistic relationships arising in a wide variety of inference and learning problems. In this talk, I will describe a new method for analyzing when the inputs to these networks can be recovered from the outputs. Building upon ideas from information theory and statistical physics, the objectives are (1) obtaining succinct formulas for the performance of optimal methods; and (2) delineating between problem regimes in which this performance can or cannot be obtained using computationally efficient methods. A key observation is that the combined effects of the individual components in these models (namely the matrices and the non-linear functions) are additive when viewed in a certain transform domain.
BIO: Galen Reeves joined the faculty at Duke University in Fall 2013, and is currently an Assistant Professor with a joint appointment in the Department of Electrical & Computer Engineering and the Department of Statistical Science. He completed his PhD in Electrical Engineering and Computer Sciences at the University of California, Berkeley in 2011, and he was a postdoctoral associate in the Departments of Statistics at Stanford University from 2011 to 2013. He received the NSF Career Award in 2018.
Contact: Liliana Chavarria at 626-395-4715 email@example.com