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Gaussian Process Surrogate Models for Neural Networks

Gaussian Process Surrogate Models for Neural Networks

11 August 2022
Michael Y. Li
Erin Grant
Thomas L. Griffiths
    BDL
    SyDa
ArXivPDFHTML

Papers citing "Gaussian Process Surrogate Models for Neural Networks"

4 / 4 papers shown
Title
Feature Fusion for Improved Classification: Combining Dempster-Shafer
  Theory and Multiple CNN Architectures
Feature Fusion for Improved Classification: Combining Dempster-Shafer Theory and Multiple CNN Architectures
Ayyub Alzahem
W. Boulila
Maha Driss
Anis Koubaa
15
2
0
23 May 2024
Bayesian Model Selection, the Marginal Likelihood, and Generalization
Bayesian Model Selection, the Marginal Likelihood, and Generalization
Sanae Lotfi
Pavel Izmailov
Gregory W. Benton
Micah Goldblum
A. Wilson
UQCV
BDL
45
55
0
23 Feb 2022
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
347
0
14 Jun 2018
Towards A Rigorous Science of Interpretable Machine Learning
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez
Been Kim
XAI
FaML
225
3,658
0
28 Feb 2017
1