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Bayesian Deep Ensembles via the Neural Tangent Kernel
v1v2 (latest)

Bayesian Deep Ensembles via the Neural Tangent Kernel

11 July 2020
Bobby He
Balaji Lakshminarayanan
Yee Whye Teh
    BDLUQCV
ArXiv (abs)PDFHTML

Papers citing "Bayesian Deep Ensembles via the Neural Tangent Kernel"

35 / 85 papers shown
Title
Incorporating Prior Knowledge into Neural Networks through an Implicit
  Composite Kernel
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel
Ziyang Jiang
Tongshu Zheng
Yiling Liu
David Carlson
73
4
0
15 May 2022
Deep Ensemble as a Gaussian Process Approximate Posterior
Deep Ensemble as a Gaussian Process Approximate Posterior
Zhijie Deng
Feng Zhou
Jianfei Chen
Guoqiang Wu
Jun Zhu
UQCV
46
5
0
30 Apr 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
BDLMLT
137
28
0
01 Mar 2022
Theoretical Error Analysis of Entropy Approximation for Gaussian Mixture
Theoretical Error Analysis of Entropy Approximation for Gaussian Mixture
Takashi Furuya
Hiroyuki Kusumoto
K. Taniguchi
Naoya Kanno
Kazuma Suetake
88
1
0
26 Feb 2022
Embedded Ensembles: Infinite Width Limit and Operating Regimes
Embedded Ensembles: Infinite Width Limit and Operating Regimes
Maksim Velikanov
Roma Kail
Ivan Anokhin
Roman Vashurin
Maxim Panov
Alexey Zaytsev
Dmitry Yarotsky
49
1
0
24 Feb 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
UQCVBDL
89
24
0
23 Feb 2022
UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural
  Representations for Computed Tomography
UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography
Francisca Vasconcelos
Bobby He
Nalini Singh
Yee Whye Teh
BDLOODUQCV
76
13
0
22 Feb 2022
Deep Ensembles Work, But Are They Necessary?
Deep Ensembles Work, But Are They Necessary?
Taiga Abe
E. Kelly Buchanan
Geoff Pleiss
R. Zemel
John P. Cunningham
OODUQCV
147
65
0
14 Feb 2022
An Overview of Uncertainty Quantification Methods for Infinite Neural
  Networks
An Overview of Uncertainty Quantification Methods for Infinite Neural Networks
Florian Juengermann
Maxime Laasri
Marius Merkle
BDL
36
0
0
13 Jan 2022
Empirical analysis of representation learning and exploration in neural
  kernel bandits
Empirical analysis of representation learning and exploration in neural kernel bandits
Michal Lisicki
Arash Afkanpour
Graham W. Taylor
55
0
0
05 Nov 2021
Quantifying Epistemic Uncertainty in Deep Learning
Quantifying Epistemic Uncertainty in Deep Learning
Ziyi Huang
Henry Lam
Haofeng Zhang
UQCVBDLUDPER
134
14
0
23 Oct 2021
Conditional Variational Autoencoder for Learned Image Reconstruction
Conditional Variational Autoencoder for Learned Image Reconstruction
Chen Zhang
Riccardo Barbano
Bangti Jin
DRL
51
20
0
22 Oct 2021
The Neural Testbed: Evaluating Joint Predictions
The Neural Testbed: Evaluating Joint Predictions
Ian Osband
Zheng Wen
S. Asghari
Vikranth Dwaracherla
Botao Hao
M. Ibrahimi
Dieterich Lawson
Xiuyuan Lu
Brendan O'Donoghue
Benjamin Van Roy
UQCV
94
22
0
09 Oct 2021
Prior and Posterior Networks: A Survey on Evidential Deep Learning
  Methods For Uncertainty Estimation
Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation
Dennis Ulmer
Christian Hardmeier
J. Frellsen
BDLUQCVUDEDLPER
150
55
0
06 Oct 2021
Marginally calibrated response distributions for end-to-end learning in
  autonomous driving
Marginally calibrated response distributions for end-to-end learning in autonomous driving
Clara Hoffmann
Nadja Klein
89
2
0
03 Oct 2021
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks
  with Sparse Gaussian Processes
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes
Jongseo Lee
Jianxiang Feng
Matthias Humt
M. Müller
Rudolph Triebel
UQCV
92
21
0
20 Sep 2021
Reliable Neural Networks for Regression Uncertainty Estimation
Reliable Neural Networks for Regression Uncertainty Estimation
Tony Tohme
Kevin Vanslette
K. Youcef-Toumi
UQCVBDL
85
15
0
16 Sep 2021
A framework for benchmarking uncertainty in deep regression
A framework for benchmarking uncertainty in deep regression
F. Schmähling
Jörg Martin
Clemens Elster
UQCV
64
8
0
10 Sep 2021
Epistemic Neural Networks
Epistemic Neural Networks
Ian Osband
Zheng Wen
M. Asghari
Vikranth Dwaracherla
M. Ibrahimi
Xiyuan Lu
Benjamin Van Roy
UQCVBDL
136
109
0
19 Jul 2021
Repulsive Deep Ensembles are Bayesian
Repulsive Deep Ensembles are Bayesian
Francesco DÁngelo
Vincent Fortuin
UQCVBDL
129
101
0
22 Jun 2021
A self consistent theory of Gaussian Processes captures feature learning
  effects in finite CNNs
A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs
Gadi Naveh
Zohar Ringel
SSLMLT
91
33
0
08 Jun 2021
Greedy Bayesian Posterior Approximation with Deep Ensembles
Greedy Bayesian Posterior Approximation with Deep Ensembles
A. Tiulpin
Matthew B. Blaschko
UQCVFedML
91
4
0
29 May 2021
Deep Ensembles from a Bayesian Perspective
Deep Ensembles from a Bayesian Perspective
L. Hoffmann
Clemens Elster
UDBDLUQCV
74
38
0
27 May 2021
Priors in Bayesian Deep Learning: A Review
Priors in Bayesian Deep Learning: A Review
Vincent Fortuin
UQCVBDL
137
134
0
14 May 2021
Uncertainty-aware Remaining Useful Life predictor
Uncertainty-aware Remaining Useful Life predictor
Luca Biggio
Alexander Wieland
M. A. Chao
I. Kastanis
Olga Fink
AI4CE
32
7
0
08 Apr 2021
MixMo: Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks
MixMo: Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks
Alexandre Ramé
Rémy Sun
Matthieu Cord
UQCV
108
60
0
10 Mar 2021
Deep Deterministic Uncertainty: A Simple Baseline
Deep Deterministic Uncertainty: A Simple Baseline
Jishnu Mukhoti
Andreas Kirsch
Joost R. van Amersfoort
Philip Torr
Y. Gal
UDUQCVPERBDL
129
156
0
23 Feb 2021
A Review of Uncertainty Quantification in Deep Learning: Techniques,
  Applications and Challenges
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Moloud Abdar
Farhad Pourpanah
Sadiq Hussain
Dana Rezazadegan
Li Liu
...
Xiaochun Cao
Abbas Khosravi
U. Acharya
V. Makarenkov
S. Nahavandi
BDLUQCV
373
1,951
0
12 Nov 2020
A Bayesian Perspective on Training Speed and Model Selection
A Bayesian Perspective on Training Speed and Model Selection
Clare Lyle
Lisa Schut
Binxin Ru
Y. Gal
Mark van der Wilk
102
24
0
27 Oct 2020
Stable ResNet
Stable ResNet
Soufiane Hayou
Eugenio Clerico
Bo He
George Deligiannidis
Arnaud Doucet
Judith Rousseau
ODLSSeg
103
53
0
24 Oct 2020
BayesAdapter: Being Bayesian, Inexpensively and Reliably, via Bayesian
  Fine-tuning
BayesAdapter: Being Bayesian, Inexpensively and Reliably, via Bayesian Fine-tuning
Zhijie Deng
Jun Zhu
BDL
48
9
0
05 Oct 2020
On Power Laws in Deep Ensembles
On Power Laws in Deep Ensembles
E. Lobacheva
Nadezhda Chirkova
M. Kodryan
Dmitry Vetrov
UQCV
75
40
0
16 Jul 2020
Neural Ensemble Search for Uncertainty Estimation and Dataset Shift
Neural Ensemble Search for Uncertainty Estimation and Dataset Shift
Sheheryar Zaidi
Arber Zela
T. Elsken
Chris Holmes
Frank Hutter
Yee Whye Teh
OODUQCV
130
75
0
15 Jun 2020
PIVEN: A Deep Neural Network for Prediction Intervals with Specific
  Value Prediction
PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value Prediction
Eli Simhayev
Gilad Katz
Lior Rokach
OOD
51
12
0
09 Jun 2020
Universal Statistics of Fisher Information in Deep Neural Networks: Mean
  Field Approach
Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach
Ryo Karakida
S. Akaho
S. Amari
FedML
201
146
0
04 Jun 2018
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