deep neural networks

Approximation and Optimization for Neural Networks

Gerrit Welper, Assistant Professor, Mathematics, University of Central Florida (UCF)

Mar 4, 16:00 - 17:00

Zoom Meeting 95807131415

Neural Networks optimization deep neural networks Finite element methods

In this talk, we consider new connections between the approximation and optimization of neural networks. Instead of relying on excessive over-parametrization to achieve zero training loss, we identify good minima by comparison with established approximation bounds.

On training algorithms for neural networks

Jinchao Xu, Professor, Applied Mathematics and Computational Science

Nov 21, 12:00 - 13:00

B9 L2 R2322 H1

Deep learning deep neural networks

In this talk, I will first give a convergence analysis of gradient descent (GD) method for training neural networks by relating them with finite element method. I will then present some acceleration techniques for GD method and also give some alternative training algorithms