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Ín-Between' Uncertainty in Bayesian Neural Networks

Ín-Between' Uncertainty in Bayesian Neural Networks

27 June 2019
Andrew Y. K. Foong
Yingzhen Li
José Miguel Hernández-Lobato
Richard Turner
    BDL
    UQCV
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Papers citing "Ín-Between' Uncertainty in Bayesian Neural Networks"

38 / 38 papers shown
Title
Optimal Subspace Inference for the Laplace Approximation of Bayesian Neural Networks
Optimal Subspace Inference for the Laplace Approximation of Bayesian Neural Networks
Josua Faller
Jörg Martin
BDL
104
0
0
04 Feb 2025
ELBOing Stein: Variational Bayes with Stein Mixture Inference
ELBOing Stein: Variational Bayes with Stein Mixture Inference
Ola Rønning
Eric T. Nalisnick
Christophe Ley
Padhraic Smyth
Thomas Hamelryck
BDL
60
1
0
30 Oct 2024
Manifold Sampling for Differentiable Uncertainty in Radiance Fields
Manifold Sampling for Differentiable Uncertainty in Radiance Fields
Linjie Lyu
Ayush Tewari
Marc Habermann
Shunsuke Saito
Michael Zollhöfer
Thomas Leimkühler
Christian Theobalt
UQCV
65
1
0
19 Sep 2024
Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noise
Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noise
Yilong Hou
Xi’an Li
Jinran Wu
You-Gan Wang
74
1
0
18 Aug 2024
Linearization Turns Neural Operators into Function-Valued Gaussian Processes
Linearization Turns Neural Operators into Function-Valued Gaussian Processes
Emilia Magnani
Marvin Pfortner
Tobias Weber
Philipp Hennig
UQCV
74
1
0
07 Jun 2024
Scalable Bayesian Learning with posteriors
Scalable Bayesian Learning with posteriors
Samuel Duffield
Kaelan Donatella
Johnathan Chiu
Phoebe Klett
Daniel Simpson
BDL
UQCV
65
1
0
31 May 2024
BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian
  Inference
BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference
Siqi Kou
Lei Gan
Dequan Wang
Chongxuan Li
Zhijie Deng
BDL
DiffM
28
7
0
17 Oct 2023
Implicit Variational Inference for High-Dimensional Posteriors
Implicit Variational Inference for High-Dimensional Posteriors
Anshuk Uppal
Kristoffer Stensbo-Smidt
Wouter Boomsma
J. Frellsen
BDL
31
1
0
10 Oct 2023
Discretization-Induced Dirichlet Posterior for Robust Uncertainty
  Quantification on Regression
Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression
Xuanlong Yu
Gianni Franchi
Jindong Gu
Emanuel Aldea
UQCV
44
5
0
17 Aug 2023
FineMorphs: Affine-diffeomorphic sequences for regression
FineMorphs: Affine-diffeomorphic sequences for regression
Michele Lohr
L. Younes
34
0
0
26 May 2023
Deep Anti-Regularized Ensembles provide reliable out-of-distribution
  uncertainty quantification
Deep Anti-Regularized Ensembles provide reliable out-of-distribution uncertainty quantification
Antoine de Mathelin
Francois Deheeger
Mathilde Mougeot
Nicolas Vayatis
OOD
UQCV
39
2
0
08 Apr 2023
Classified as unknown: A novel Bayesian neural network
Classified as unknown: A novel Bayesian neural network
Tianbo Yang
Tianshuo Yang
BDL
UQCV
12
0
0
31 Jan 2023
The Implicit Delta Method
The Implicit Delta Method
Nathan Kallus
James McInerney
28
1
0
11 Nov 2022
Do Bayesian Neural Networks Need To Be Fully Stochastic?
Do Bayesian Neural Networks Need To Be Fully Stochastic?
Mrinank Sharma
Sebastian Farquhar
Eric T. Nalisnick
Tom Rainforth
BDL
31
53
0
11 Nov 2022
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning
Zhijie Deng
Feng Zhou
Jun Zhu
BDL
50
19
0
23 Oct 2022
Constructing Prediction Intervals with Neural Networks: An Empirical
  Evaluation of Bootstrapping and Conformal Inference Methods
Constructing Prediction Intervals with Neural Networks: An Empirical Evaluation of Bootstrapping and Conformal Inference Methods
Alex Contarino
Christine M. Schubert-Kabban
Chancellor Johnstone
Fairul Mohd-Zaid
63
3
0
07 Oct 2022
Scale-invariant Bayesian Neural Networks with Connectivity Tangent
  Kernel
Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel
Sungyub Kim
Si-hun Park
Kyungsu Kim
Eunho Yang
BDL
45
4
0
30 Sep 2022
Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware
  Priors
Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware Priors
Gianluca Detommaso
Alberto Gasparin
A. Wilson
Cédric Archambeau
UQCV
BDL
39
3
0
17 Jul 2022
Challenges and Pitfalls of Bayesian Unlearning
Challenges and Pitfalls of Bayesian Unlearning
Ambrish Rawat
James Requeima
W. Bruinsma
Richard Turner
BDL
MU
54
5
0
07 Jul 2022
Distributional Gaussian Processes Layers for Out-of-Distribution
  Detection
Distributional Gaussian Processes Layers for Out-of-Distribution Detection
S. Popescu
D. Sharp
James H. Cole
Konstantinos Kamnitsas
Ben Glocker
OOD
54
0
0
27 Jun 2022
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
Javier Antorán
David Janz
J. Allingham
Erik A. Daxberger
Riccardo Barbano
Eric T. Nalisnick
José Miguel Hernández-Lobato
UQCV
BDL
48
29
0
17 Jun 2022
NeuralEF: Deconstructing Kernels by Deep Neural Networks
NeuralEF: Deconstructing Kernels by Deep Neural Networks
Zhijie Deng
Jiaxin Shi
Jun Zhu
42
18
0
30 Apr 2022
Invariance Learning in Deep Neural Networks with Differentiable Laplace
  Approximations
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations
Alexander Immer
Tycho F. A. van der Ouderaa
Gunnar Rätsch
Vincent Fortuin
Mark van der Wilk
BDL
57
45
0
22 Feb 2022
Transformer Uncertainty Estimation with Hierarchical Stochastic
  Attention
Transformer Uncertainty Estimation with Hierarchical Stochastic Attention
Jiahuan Pei
Cheng-Yu Wang
Gyuri Szarvas
31
22
0
27 Dec 2021
Depth Uncertainty Networks for Active Learning
Depth Uncertainty Networks for Active Learning
Chelsea Murray
J. Allingham
Javier Antorán
José Miguel Hernández-Lobato
UQCV
AI4CE
38
2
0
13 Dec 2021
Generalized Out-of-Distribution Detection: A Survey
Generalized Out-of-Distribution Detection: A Survey
Jingkang Yang
Kaiyang Zhou
Yixuan Li
Ziwei Liu
200
893
0
21 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
61
21
0
20 Sep 2021
A Survey of Uncertainty in Deep Neural Networks
A Survey of Uncertainty in Deep Neural Networks
J. Gawlikowski
Cedrique Rovile Njieutcheu Tassi
Mohsin Ali
Jongseo Lee
Matthias Humt
...
R. Roscher
Muhammad Shahzad
Wen Yang
R. Bamler
Xiaoxiang Zhu
BDL
UQCV
OOD
89
1,122
0
07 Jul 2021
Laplace Redux -- Effortless Bayesian Deep Learning
Laplace Redux -- Effortless Bayesian Deep Learning
Erik A. Daxberger
Agustinus Kristiadi
Alexander Immer
Runa Eschenhagen
Matthias Bauer
Philipp Hennig
BDL
UQCV
63
299
0
28 Jun 2021
Bayesian Neural Networks: Essentials
Bayesian Neural Networks: Essentials
Daniel T. Chang
UQCV
BDL
38
12
0
22 Jun 2021
NOMU: Neural Optimization-based Model Uncertainty
NOMU: Neural Optimization-based Model Uncertainty
Jakob Heiss
Jakob Weissteiner
Hanna Wutte
Sven Seuken
Josef Teichmann
BDL
54
19
0
26 Feb 2021
Uncertainty-Aware (UNA) Bases for Deep Bayesian Regression Using
  Multi-Headed Auxiliary Networks
Uncertainty-Aware (UNA) Bases for Deep Bayesian Regression Using Multi-Headed Auxiliary Networks
Sujay Thakur
Cooper Lorsung
Yaniv Yacoby
Finale Doshi-Velez
Weiwei Pan
BDL
UQCV
40
4
0
21 Jun 2020
Estimating Model Uncertainty of Neural Networks in Sparse Information
  Form
Estimating Model Uncertainty of Neural Networks in Sparse Information Form
Jongseo Lee
Matthias Humt
Jianxiang Feng
Rudolph Triebel
BDL
UQCV
49
46
0
20 Jun 2020
Depth Uncertainty in Neural Networks
Depth Uncertainty in Neural Networks
Javier Antorán
J. Allingham
José Miguel Hernández-Lobato
UQCV
OOD
BDL
46
101
0
15 Jun 2020
Global inducing point variational posteriors for Bayesian neural
  networks and deep Gaussian processes
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes
Sebastian W. Ober
Laurence Aitchison
BDL
31
60
0
17 May 2020
Subspace Inference for Bayesian Deep Learning
Subspace Inference for Bayesian Deep Learning
Pavel Izmailov
Wesley J. Maddox
Polina Kirichenko
T. Garipov
Dmitry Vetrov
A. Wilson
UQCV
BDL
45
144
0
17 Jul 2019
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
295
5,726
0
05 Dec 2016
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
UQCV
BDL
299
9,202
0
06 Jun 2015
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