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Improved uncertainty quantification for neural networks with Bayesian
  last layer
v1v2v3 (latest)

Improved uncertainty quantification for neural networks with Bayesian last layer

21 February 2023
F. Fiedler
S. Lucia
    UQCVBDL
ArXiv (abs)PDFHTML

Papers citing "Improved uncertainty quantification for neural networks with Bayesian last layer"

11 / 11 papers shown
Title
Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference
Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference
Philipp Reiser
Javier Enrique Aguilar
A. Guthke
Paul-Christian Bürkner
135
3
0
08 Dec 2023
Learning in High Dimension Always Amounts to Extrapolation
Learning in High Dimension Always Amounts to Extrapolation
Randall Balestriero
J. Pesenti
Yann LeCun
117
104
0
18 Oct 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
346
1,932
0
12 Nov 2020
Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users
Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users
Laurent Valentin Jospin
Wray Buntine
F. Boussaïd
Hamid Laga
Bennamoun
OODBDLUQCV
86
631
0
14 Jul 2020
Deep Latent-Variable Kernel Learning
Deep Latent-Variable Kernel Learning
Haitao Liu
Yew-Soon Ong
Xiaomo Jiang
Xiaofang Wang
BDL
51
8
0
18 May 2020
The k-tied Normal Distribution: A Compact Parameterization of Gaussian
  Mean Field Posteriors in Bayesian Neural Networks
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
J. Swiatkowski
Kevin Roth
Bastiaan S. Veeling
Linh-Tam Tran
Joshua V. Dillon
Jasper Snoek
Stephan Mandt
Tim Salimans
Rodolphe Jenatton
Sebastian Nowozin
BDL
54
46
0
07 Feb 2020
Benchmarking the Neural Linear Model for Regression
Benchmarking the Neural Linear Model for Regression
Sebastian W. Ober
C. Rasmussen
BDL
72
43
0
18 Dec 2019
When Gaussian Process Meets Big Data: A Review of Scalable GPs
When Gaussian Process Meets Big Data: A Review of Scalable GPs
Haitao Liu
Yew-Soon Ong
Xiaobo Shen
Jianfei Cai
GP
101
693
0
03 Jul 2018
Deep Kernel Learning
Deep Kernel Learning
A. Wilson
Zhiting Hu
Ruslan Salakhutdinov
Eric Xing
BDL
256
889
0
06 Nov 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCVBDL
860
9,353
0
06 Jun 2015
Batch Normalization: Accelerating Deep Network Training by Reducing
  Internal Covariate Shift
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe
Christian Szegedy
OOD
467
43,347
0
11 Feb 2015
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