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Exact posterior distributions of wide Bayesian neural networks

Exact posterior distributions of wide Bayesian neural networks

18 June 2020
Jiri Hron
Yasaman Bahri
Roman Novak
Jeffrey Pennington
Jascha Narain Sohl-Dickstein
    UQCV
    BDL
ArXivPDFHTML

Papers citing "Exact posterior distributions of wide Bayesian neural networks"

15 / 15 papers shown
Title
Student-t processes as infinite-width limits of posterior Bayesian neural networks
Student-t processes as infinite-width limits of posterior Bayesian neural networks
Francesco Caporali
Stefano Favaro
Dario Trevisan
BDL
263
0
0
06 Feb 2025
Function-Space MCMC for Bayesian Wide Neural Networks
Function-Space MCMC for Bayesian Wide Neural Networks
Lucia Pezzetti
Stefano Favaro
Stefano Peluchetti
BDL
228
0
0
26 Aug 2024
Bayesian RG Flow in Neural Network Field Theories
Bayesian RG Flow in Neural Network Field Theories
Jessica N. Howard
Marc S. Klinger
Anindita Maiti
A. G. Stapleton
68
1
0
27 May 2024
Sparsity-depth Tradeoff in Infinitely Wide Deep Neural Networks
Sparsity-depth Tradeoff in Infinitely Wide Deep Neural Networks
Chanwoo Chun
Daniel D. Lee
BDL
45
2
0
17 May 2023
Bayes-optimal Learning of Deep Random Networks of Extensive-width
Bayes-optimal Learning of Deep Random Networks of Extensive-width
Hugo Cui
Florent Krzakala
Lenka Zdeborová
BDL
30
35
0
01 Feb 2023
Open Source Vizier: Distributed Infrastructure and API for Reliable and
  Flexible Blackbox Optimization
Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization
Xingyou Song
Sagi Perel
Chansoo Lee
Greg Kochanski
Daniel Golovin
33
26
0
27 Jul 2022
Fast Finite Width Neural Tangent Kernel
Fast Finite Width Neural Tangent Kernel
Roman Novak
Jascha Narain Sohl-Dickstein
S. Schoenholz
AAML
28
54
0
17 Jun 2022
Wide Bayesian neural networks have a simple weight posterior: theory and
  accelerated sampling
Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling
Jiri Hron
Roman Novak
Jeffrey Pennington
Jascha Narain Sohl-Dickstein
UQCV
BDL
48
6
0
15 Jun 2022
Contrasting random and learned features in deep Bayesian linear
  regression
Contrasting random and learned features in deep Bayesian linear regression
Jacob A. Zavatone-Veth
William L. Tong
Cengiz Pehlevan
BDL
MLT
33
27
0
01 Mar 2022
Depth induces scale-averaging in overparameterized linear Bayesian
  neural networks
Depth induces scale-averaging in overparameterized linear Bayesian neural networks
Jacob A. Zavatone-Veth
Cengiz Pehlevan
BDL
UQCV
MDE
41
9
0
23 Nov 2021
The Limitations of Large Width in Neural Networks: A Deep Gaussian
  Process Perspective
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective
Geoff Pleiss
John P. Cunningham
28
24
0
11 Jun 2021
Asymptotics of representation learning in finite Bayesian neural
  networks
Asymptotics of representation learning in finite Bayesian neural networks
Jacob A. Zavatone-Veth
Abdulkadir Canatar
Benjamin S. Ruben
Cengiz Pehlevan
26
28
0
01 Jun 2021
Priors in Bayesian Deep Learning: A Review
Priors in Bayesian Deep Learning: A Review
Vincent Fortuin
UQCV
BDL
38
124
0
14 May 2021
What Are Bayesian Neural Network Posteriors Really Like?
What Are Bayesian Neural Network Posteriors Really Like?
Pavel Izmailov
Sharad Vikram
Matthew D. Hoffman
A. Wilson
UQCV
BDL
28
374
0
29 Apr 2021
Why bigger is not always better: on finite and infinite neural networks
Why bigger is not always better: on finite and infinite neural networks
Laurence Aitchison
175
51
0
17 Oct 2019
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