Doing the impossible: Why neural networks can be trained at all

13 May 2018

Papers citing "Doing the impossible: Why neural networks can be trained at all"

3 / 3 papers shown

Title
PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs Xuhui Meng Zhen Li Dongkun Zhang George Karniadakis PINN AI4CE 22 442 0 23 Sep 2019
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks P. Stinis Tobias J. Hagge A. Tartakovsky Enoch Yeung GAN AI4CE 53 33 0 22 Mar 2018
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 183 1,185 0 30 Nov 2014