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On the Disconnect Between Theory and Practice of Neural Networks: Limits
  of the NTK Perspective

On the Disconnect Between Theory and Practice of Neural Networks: Limits of the NTK Perspective

29 September 2023
Jonathan Wenger
Felix Dangel
Agustinus Kristiadi
ArXivPDFHTML

Papers citing "On the Disconnect Between Theory and Practice of Neural Networks: Limits of the NTK Perspective"

3 / 3 papers shown
Title
Posterior Refinement Improves Sample Efficiency in Bayesian Neural
  Networks
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
Agustinus Kristiadi
Runa Eschenhagen
Philipp Hennig
BDL
21
12
0
20 May 2022
Wide Mean-Field Bayesian Neural Networks Ignore the Data
Wide Mean-Field Bayesian Neural Networks Ignore the Data
Beau Coker
W. Bruinsma
David R. Burt
Weiwei Pan
Finale Doshi-Velez
UQCV
BDL
37
22
0
23 Feb 2022
Deep Networks and the Multiple Manifold Problem
Deep Networks and the Multiple Manifold Problem
Sam Buchanan
D. Gilboa
John N. Wright
166
39
0
25 Aug 2020
1