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Increasing Depth Leads to U-Shaped Test Risk in Over-parameterized
  Convolutional Networks

Increasing Depth Leads to U-Shaped Test Risk in Over-parameterized Convolutional Networks

19 October 2020
Eshaan Nichani
Adityanarayanan Radhakrishnan
Caroline Uhler
ArXivPDFHTML

Papers citing "Increasing Depth Leads to U-Shaped Test Risk in Over-parameterized Convolutional Networks"

5 / 5 papers shown
Title
Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks
Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks
Eshaan Nichani
Alexandru Damian
Jason D. Lee
MLT
38
13
0
11 May 2023
Room dimensions and absorption inference from room transfer function via
  machine learning
Room dimensions and absorption inference from room transfer function via machine learning
Yuanxin Xia
C. Jeong
13
2
0
25 Apr 2023
Towards Learning Convolutions from Scratch
Towards Learning Convolutions from Scratch
Behnam Neyshabur
SSL
220
71
0
27 Jul 2020
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
220
348
0
14 Jun 2018
Benefits of depth in neural networks
Benefits of depth in neural networks
Matus Telgarsky
136
602
0
14 Feb 2016
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