Abstract for the talk on 17.03.2022 (17:00 h)

Math Machine Learning seminar MPI MIS + UCLA

Gabin Maxime Nguegnang (RWTH Aachen University)
Convergence of gradient descent for learning deep linear neural networks
See the video of this talk.

We study the convergence properties of gradient descent for training deep linear neural networks, i.e., deep matrix factorizations, by extending a previous analysis for the related gradient flow. We show that under suitable conditions on the step sizes gradient descent converges to a critical point of the loss function, i.e., the square loss in this work. Furthermore, we demonstrate that for almost all initializations gradient descent converges to a global minimum in the case of two layers. In the case of three or more layers we show that gradient descent converges to a global minimum on the manifold matrices of some fixed rank, where the rank cannot be determined a priori.


23.03.2022, 11:13