Mean field theory for deep dropout networks: digging up gradient
backpropagation deeply

Mean field theory for deep dropout networks: digging up gradient backpropagation deeply

19 December 2019

Wei Huang

Papers citing "Mean field theory for deep dropout networks: digging up gradient backpropagation deeply"

4 / 4 papers shown

Title
On the Initialisation of Wide Low-Rank Feedforward Neural Networks Thiziri Nait Saada Jared Tanner 13 1 0 31 Jan 2023
Component-Wise Natural Gradient Descent -- An Efficient Neural Network Optimization Tran van Sang Mhd Irvan R. Yamaguchi Toshiyuki Nakata 15 1 0 11 Oct 2022
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks Lechao Xiao Yasaman Bahri Jascha Narain Sohl-Dickstein S. Schoenholz Jeffrey Pennington 238 348 0 14 Jun 2018
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,145 0 06 Jun 2015