Ín-Between' Uncertainty in Bayesian Neural Networks

27 June 2019

Andrew Y. K. Foong

Yingzhen Li

José Miguel Hernández-Lobato

Papers citing "Ín-Between' Uncertainty in Bayesian Neural Networks"

31 / 31 papers shown

Title
ELBOing Stein: Variational Bayes with Stein Mixture Inference Ola Rønning Eric T. Nalisnick Christophe Ley Padhraic Smyth Thomas Hamelryck BDL 52 1 0 30 Oct 2024
Manifold Sampling for Differentiable Uncertainty in Radiance Fields Linjie Lyu Ayush Tewari Marc Habermann Shunsuke Saito Michael Zollhöfer Thomas Leimkühler Christian Theobalt UQCV 40 1 0 19 Sep 2024
Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noise Yilong Hou Xi’an Li Jinran Wu You-Gan Wang 66 1 0 18 Aug 2024
Linearization Turns Neural Operators into Function-Valued Gaussian Processes Emilia Magnani Marvin Pfortner Tobias Weber Philipp Hennig UQCV 66 1 0 07 Jun 2024
Scalable Bayesian Learning with posteriors Samuel Duffield Kaelan Donatella Johnathan Chiu Phoebe Klett Daniel Simpson BDL UQCV 62 1 0 31 May 2024
BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference Siqi Kou Lei Gan Dequan Wang Chongxuan Li Zhijie Deng BDL DiffM 23 7 0 17 Oct 2023
Implicit Variational Inference for High-Dimensional Posteriors Anshuk Uppal Kristoffer Stensbo-Smidt Wouter Boomsma J. Frellsen BDL 18 1 0 10 Oct 2023
Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression Xuanlong Yu Gianni Franchi Jindong Gu Emanuel Aldea UQCV 16 4 0 17 Aug 2023
Deep Anti-Regularized Ensembles provide reliable out-of-distribution uncertainty quantification Antoine de Mathelin Francois Deheeger Mathilde Mougeot Nicolas Vayatis OOD UQCV 19 2 0 08 Apr 2023
Classified as unknown: A novel Bayesian neural network Tianbo Yang Tianshuo Yang BDL UQCV 10 0 0 31 Jan 2023
Do Bayesian Neural Networks Need To Be Fully Stochastic? Mrinank Sharma Sebastian Farquhar Eric T. Nalisnick Tom Rainforth BDL 18 52 0 11 Nov 2022
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning Zhijie Deng Feng Zhou Jun Zhu BDL 47 19 0 23 Oct 2022
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 36 3 0 07 Oct 2022
Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel Sungyub Kim Si-hun Park Kyungsu Kim Eunho Yang BDL 26 4 0 30 Sep 2022
Challenges and Pitfalls of Bayesian Unlearning Ambrish Rawat James Requeima W. Bruinsma Richard E. Turner BDL MU 42 5 0 07 Jul 2022
Distributional Gaussian Processes Layers for Out-of-Distribution Detection S. Popescu D. Sharp James H. Cole Konstantinos Kamnitsas Ben Glocker OOD 23 0 0 27 Jun 2022
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 27 28 0 17 Jun 2022
NeuralEF: Deconstructing Kernels by Deep Neural Networks Zhijie Deng Jiaxin Shi Jun Zhu 16 18 0 30 Apr 2022
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 31 44 0 22 Feb 2022
Transformer Uncertainty Estimation with Hierarchical Stochastic Attention Jiahuan Pei Cheng-Yu Wang Gyuri Szarvas 24 22 0 27 Dec 2021
Generalized Out-of-Distribution Detection: A Survey Jingkang Yang Kaiyang Zhou Yixuan Li Ziwei Liu 185 877 0 21 Oct 2021
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes Jongseo Lee Jianxiang Feng Matthias Humt M. Müller Rudolph Triebel UQCV 48 21 0 20 Sep 2021
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 32 1,109 0 07 Jul 2021
Laplace Redux -- Effortless Bayesian Deep Learning Erik A. Daxberger Agustinus Kristiadi Alexander Immer Runa Eschenhagen Matthias Bauer Philipp Hennig BDL UQCV 38 288 0 28 Jun 2021
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 25 4 0 21 Jun 2020
Estimating Model Uncertainty of Neural Networks in Sparse Information Form Jongseo Lee Matthias Humt Jianxiang Feng Rudolph Triebel BDL UQCV 33 46 0 20 Jun 2020
Depth Uncertainty in Neural Networks Javier Antorán J. Allingham José Miguel Hernández-Lobato UQCV OOD BDL 38 100 0 15 Jun 2020
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes Sebastian W. Ober Laurence Aitchison BDL 18 60 0 17 May 2020
Subspace Inference for Bayesian Deep Learning Pavel Izmailov Wesley J. Maddox Polina Kirichenko T. Garipov Dmitry Vetrov A. Wilson UQCV BDL 30 142 0 17 Jul 2019
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,660 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,138 0 06 Jun 2015