Lipschitz constant estimation for general neural network architectures using control tools

arXiv:2405.01125v1 Announce Type: new
Abstract: This paper is devoted to the estimation of the Lipschitz constant of neural networks using semidefinite programming. For this purpose, we interpret neural networks as time-varying dynamical systems, where the $k$-th layer corresponds to the dynamics at time $k$. A key novelty with respect to prior work is that we use this interpretation to exploit the series interconnection structure of neural networks with a dynamic programming recursion. Nonlinearities, such as activation functions and nonlinear pooling …

abstract architectures arxiv control cs.lg cs.sy dynamics eess.iv eess.sy general key layer network networks neural network neural networks paper programming systems tools type