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A note on regularised NTK dynamics with an application to PAC-Bayesian
  training

A note on regularised NTK dynamics with an application to PAC-Bayesian training

20 December 2023
Eugenio Clerico
Benjamin Guedj
ArXivPDFHTML

Papers citing "A note on regularised NTK dynamics with an application to PAC-Bayesian training"

13 / 13 papers shown
Title
Generalization Bounds: Perspectives from Information Theory and
  PAC-Bayes
Generalization Bounds: Perspectives from Information Theory and PAC-Bayes
Fredrik Hellström
G. Durisi
Benjamin Guedj
Maxim Raginsky
17
36
0
08 Sep 2023
Generalisation under gradient descent via deterministic PAC-Bayes
Generalisation under gradient descent via deterministic PAC-Bayes
Eugenio Clerico
Tyler Farghly
George Deligiannidis
Benjamin Guedj
Arnaud Doucet
54
4
0
06 Sep 2022
Quantitative Gaussian Approximation of Randomly Initialized Deep Neural
  Networks
Quantitative Gaussian Approximation of Randomly Initialized Deep Neural Networks
Andrea Basteri
Dario Trevisan
BDL
42
20
0
14 Mar 2022
Risk bounds for aggregated shallow neural networks using Gaussian prior
Risk bounds for aggregated shallow neural networks using Gaussian prior
L. Tinsi
A. Dalalyan
BDL
44
7
0
21 Dec 2021
Conditionally Gaussian PAC-Bayes
Conditionally Gaussian PAC-Bayes
Eugenio Clerico
George Deligiannidis
Arnaud Doucet
78
10
0
22 Oct 2021
Wide stochastic networks: Gaussian limit and PAC-Bayesian training
Wide stochastic networks: Gaussian limit and PAC-Bayesian training
Eugenio Clerico
George Deligiannidis
Arnaud Doucet
50
12
0
17 Jun 2021
PAC-Bayesian Contrastive Unsupervised Representation Learning
PAC-Bayesian Contrastive Unsupervised Representation Learning
Kento Nozawa
Pascal Germain
Benjamin Guedj
SSL
BDL
30
27
0
10 Oct 2019
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural
  Networks
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks
Gaël Letarte
Pascal Germain
Benjamin Guedj
Franccois Laviolette
MQ
AI4CE
UQCV
55
54
0
24 May 2019
Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian
  Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation
Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation
Greg Yang
94
286
0
13 Feb 2019
Generalization Error Bounds of Gradient Descent for Learning
  Over-parameterized Deep ReLU Networks
Generalization Error Bounds of Gradient Descent for Learning Over-parameterized Deep ReLU Networks
Yuan Cao
Quanquan Gu
ODL
MLT
AI4CE
60
155
0
04 Feb 2019
A Primer on PAC-Bayesian Learning
A Primer on PAC-Bayesian Learning
Benjamin Guedj
88
221
0
16 Jan 2019
Learning and Generalization in Overparameterized Neural Networks, Going
  Beyond Two Layers
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers
Zeyuan Allen-Zhu
Yuanzhi Li
Yingyu Liang
MLT
136
769
0
12 Nov 2018
Regularization Matters: Generalization and Optimization of Neural Nets
  v.s. their Induced Kernel
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel
Colin Wei
Jason D. Lee
Qiang Liu
Tengyu Ma
131
245
0
12 Oct 2018
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