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Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural
  Network Initialization?

Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization?

2 July 2020
Yaniv Blumenfeld
D. Gilboa
Daniel Soudry
    ODL
ArXivPDFHTML

Papers citing "Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization?"

5 / 5 papers shown
Title
Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning
Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning
Calarina Muslimani
Matthew E. Taylor
OffRL
46
2
0
30 Apr 2024
Dynamical Isometry for Residual Networks
Dynamical Isometry for Residual Networks
Advait Gadhikar
R. Burkholz
ODL
AI4CE
40
2
0
05 Oct 2022
Marginalizable Density Models
Marginalizable Density Models
D. Gilboa
Ari Pakman
Thibault Vatter
BDL
32
5
0
08 Jun 2021
The Future is Log-Gaussian: ResNets and Their Infinite-Depth-and-Width
  Limit at Initialization
The Future is Log-Gaussian: ResNets and Their Infinite-Depth-and-Width Limit at Initialization
Mufan Li
Mihai Nica
Daniel M. Roy
30
33
0
07 Jun 2021
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train
  10,000-Layer Vanilla Convolutional Neural Networks
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
233
348
0
14 Jun 2018
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