The Polyak-Lojasiewicz condition as a framework for over-parameterized optimization and its application to deep learning

Mikhail Belkin, UCSD
Event time: 
Wednesday, December 1, 2021 - 2:30pm
Location: See map
Event description: 

Abstract:  The success of deep learning is due, to a large extent, to the remarkable effectiveness of gradient-based optimization methods applied to large neural networks. In this talk I will discuss some general mathematical principles allowing for efficient optimization in over-parameterized non-linear systems, a setting that includes deep neural networks. I will discuss that optimization problems corresponding to these systems are not convex, even locally, but instead satisfy the Polyak-Lojasiewicz (PL) condition on most of the parameter space, allowing for efficient optimization by gradient descent or SGD. I will connect the PL condition of these systems to the condition number associated to the tangent kernel and show how a non-linear theory for those systems parallels  classical analyses of over-parameterized linear equations. As a separate related developement, I will discuss a  perspective on the remarkable recently discovered phenomenon of transition  to linearity (constancy of NTK) in certain classes of large neural networks. I will show how  this transition to linearity results from the scaling of the Hessian with the size of the network controlled by certain fuctional norms.  Combining these ideas, I will show how the transition to linearity can be used to demonstrate the PL condition and convergence for a general class of wide neural networks. Finally I will comment systems which are ”almost” over-parameterized, which appears to be common in practice.  

Based on joint work with Chaoyue Liu and Libin Zhu

Event Type: 
Applied Mathematics