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1908.10030
Cited By
Finite size corrections for neural network Gaussian processes
27 August 2019
J. Antognini
BDL
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Papers citing
"Finite size corrections for neural network Gaussian processes"
25 / 25 papers shown
Fermions and Supersymmetry in Neural Network Field Theories
Chemical Science (Chem. Sci.), 2025
Samuel Frank
James Halverson
Anindita Maiti
Fabian Ruehle
134
3
0
20 Nov 2025
Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines
Neural Information Processing Systems (NeurIPS), 2024
Edward Milsom
Ben Anson
Laurence Aitchison
266
0
0
08 Oct 2024
Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection
Steven Adams
A. Patané
Morteza Lahijanian
Luca Laurenti
431
8
0
26 Jul 2024
Bayesian RG Flow in Neural Network Field Theories
Jessica N. Howard
Marc S. Klinger
Anindita Maiti
A. G. Stapleton
465
8
0
27 May 2024
A note on regularised NTK dynamics with an application to PAC-Bayesian training
Eugenio Clerico
Benjamin Guedj
393
2
0
20 Dec 2023
Depthwise Hyperparameter Transfer in Residual Networks: Dynamics and Scaling Limit
International Conference on Learning Representations (ICLR), 2023
Blake Bordelon
Lorenzo Noci
Mufan Li
Boris Hanin
Cengiz Pehlevan
480
51
0
28 Sep 2023
A Primer on Bayesian Neural Networks: Review and Debates
Federico Danieli
Konstantinos Pitas
M. Vladimirova
Vincent Fortuin
BDL
AAML
410
40
0
28 Sep 2023
Convolutional Deep Kernel Machines
International Conference on Learning Representations (ICLR), 2023
Edward Milsom
Ben Anson
Laurence Aitchison
BDL
534
6
0
18 Sep 2023
Local Kernel Renormalization as a mechanism for feature learning in overparametrized Convolutional Neural Networks
Nature Communications (Nat. Commun.), 2023
R. Aiudi
R. Pacelli
A. Vezzani
R. Burioni
P. Rotondo
MLT
308
29
0
21 Jul 2023
Structures of Neural Network Effective Theories
cCaugin Ararat
Tianji Cai
Cem Tekin
Zhengkang Zhang
236
14
0
03 May 2023
Non-asymptotic approximations of Gaussian neural networks via second-order Poincaré inequalities
Symposium on Advances in Approximate Bayesian Inference (AABI), 2023
Alberto Bordino
Stefano Favaro
S. Fortini
341
12
0
08 Apr 2023
Bayesian inference with finitely wide neural networks
Physical Review E (PRE), 2023
Chi-Ken Lu
BDL
302
0
0
06 Mar 2023
On Connecting Deep Trigonometric Networks with Deep Gaussian Processes: Covariance, Expressivity, and Neural Tangent Kernel
Chi-Ken Lu
Patrick Shafto
BDL
386
1
0
14 Mar 2022
Contrasting random and learned features in deep Bayesian linear regression
Physical Review E (Phys. Rev. E), 2022
Jacob A. Zavatone-Veth
William L. Tong
Cengiz Pehlevan
BDL
MLT
393
31
0
01 Mar 2022
The edge of chaos: quantum field theory and deep neural networks
SciPost Physics (SciPost Phys.), 2021
Kevin T. Grosvenor
R. Jefferson
262
31
0
27 Sep 2021
A theory of representation learning gives a deep generalisation of kernel methods
International Conference on Machine Learning (ICML), 2021
Adam X. Yang
Maxime Robeyns
Edward Milsom
Ben Anson
Nandi Schoots
Laurence Aitchison
BDL
669
14
0
30 Aug 2021
Deep Stable neural networks: large-width asymptotics and convergence rates
Stefano Favaro
S. Fortini
Stefano Peluchetti
BDL
462
17
0
02 Aug 2021
Asymptotics of representation learning in finite Bayesian neural networks
Neural Information Processing Systems (NeurIPS), 2021
Jacob A. Zavatone-Veth
Abdulkadir Canatar
Benjamin S. Ruben
Cengiz Pehlevan
499
43
0
01 Jun 2021
Non-asymptotic approximations of neural networks by Gaussian processes
Annual Conference Computational Learning Theory (COLT), 2021
Ronen Eldan
Dan Mikulincer
T. Schramm
327
26
0
17 Feb 2021
Generalization bounds for deep learning
Guillermo Valle Pérez
A. Louis
BDL
319
48
0
07 Dec 2020
Statistical Mechanics of Deep Linear Neural Networks: The Back-Propagating Kernel Renormalization
Qianyi Li
H. Sompolinsky
537
90
0
07 Dec 2020
Neural Networks and Quantum Field Theory
James Halverson
Anindita Maiti
Keegan Stoner
392
93
0
19 Aug 2020
Finite Versus Infinite Neural Networks: an Empirical Study
Neural Information Processing Systems (NeurIPS), 2020
Jaehoon Lee
S. Schoenholz
Jeffrey Pennington
Ben Adlam
Lechao Xiao
Roman Novak
Jascha Narain Sohl-Dickstein
456
232
0
31 Jul 2020
Large Deviation Analysis of Function Sensitivity in Random Deep Neural Networks
Bo Li
D. Saad
159
12
0
13 Oct 2019
Non-Gaussian processes and neural networks at finite widths
Mathematical and Scientific Machine Learning (MSML), 2019
Sho Yaida
401
102
0
30 Sep 2019
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