Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks

18 February 2015

José Miguel Hernández-Lobato

Papers citing "Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks"

50 / 109 papers shown

Title
Uncertainty Quantification With Noise Injection in Neural Networks: A Bayesian Perspective Xueqiong Yuan Jipeng Li E. Kuruoglu UQCV BDL 38 0 0 21 Jan 2025
Annealed Multiple Choice Learning: Overcoming limitations of Winner-takes-all with annealing David Perera Victor Letzelter Théo Mariotte Adrien Cortés Mickaël Chen S. Essid Ga¨el Richard 64 2 0 20 Jan 2025
A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation M. Valiuddin R. V. Sloun C.G.A. Viviers Peter H. N. de With Fons van der Sommen UQCV 79 1 0 25 Nov 2024
ELBOing Stein: Variational Bayes with Stein Mixture Inference Ola Rønning Eric T. Nalisnick Christophe Ley Padhraic Smyth Thomas Hamelryck BDL 45 1 0 30 Oct 2024
Uncertainty-aware Reward Model: Teaching Reward Models to Know What is Unknown Xingzhou Lou Dong Yan Wei Shen Yuzi Yan Jian Xie Junge Zhang 45 21 0 01 Oct 2024
Uncertainty Quantification in Seismic Inversion Through Integrated Importance Sampling and Ensemble Methods Luping Qu Mauricio Araya-Polo Laurent Demanet 45 0 0 10 Sep 2024
Gradient-free variational learning with conditional mixture networks Conor Heins Hao Wu Dimitrije Marković Alexander Tschantz Jeff Beck Christopher L. Buckley BDL 27 2 0 29 Aug 2024
Improved off-policy training of diffusion samplers Marcin Sendera Minsu Kim Sarthak Mittal Pablo Lemos Luca Scimeca Jarrid Rector-Brooks Alexandre Adam Yoshua Bengio Nikolay Malkin OffRL 64 17 0 07 Feb 2024
Favour: FAst Variance Operator for Uncertainty Rating Thomas Dybdahl Ahle Sahar Karimi Peter Tak Peter Tang BDL 19 0 0 21 Nov 2023
Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Prediction Qinghui Liu E. Fuster-García I. T. Hovden Donatas Sederevičius Karoline Skogen ... Till Schellhorn P. Brandal A. Bjørnerud K. Emblem Kyrre Eeg Emblem 23 3 0 11 Sep 2023
Likelihood-ratio-based confidence intervals for neural networks Laurens Sluijterman Eric Cator Tom Heskes UQCV 19 0 0 04 Aug 2023
Function-Space Regularization for Deep Bayesian Classification J. Lin Joe Watson Pascal Klink Jan Peters UQCV BDL 28 1 0 12 Jul 2023
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems Andreas Look M. Kandemir Barbara Rakitsch Jan Peters BDL 25 6 0 02 May 2023
UATTA-EB: Uncertainty-Aware Test-Time Augmented Ensemble of BERTs for Classifying Common Mental Illnesses on Social Media Posts Pratinav Seth Mihir Agarwal AI4MH 16 1 0 10 Apr 2023
Dropout Injection at Test Time for Post Hoc Uncertainty Quantification in Neural Networks Emanuele Ledda Giorgio Fumera Fabio Roli BDL UQCV 27 14 0 06 Feb 2023
Client Selection for Federated Bayesian Learning Jiarong Yang Yuan Liu Rahif Kassab FedML 22 11 0 11 Dec 2022
Are you using test log-likelihood correctly? Sameer K. Deshpande Soumya K. Ghosh Tin D. Nguyen Tamara Broderick 19 7 0 01 Dec 2022
ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference N. Durasov Nik Dorndorf Hieu M. Le Pascal Fua UQCV 15 10 0 21 Nov 2022
DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems Nabeel Seedat F. Imrie M. Schaar 27 12 0 09 Nov 2022
Nonparametric Probabilistic Regression with Coarse Learners B. Lucena 22 0 0 28 Oct 2022
On the optimization and pruning for Bayesian deep learning X. Ke Yanan Fan BDL UQCV 22 1 0 24 Oct 2022
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning Zhijie Deng Feng Zhou Jun Zhu BDL 42 19 0 23 Oct 2022
Packed-Ensembles for Efficient Uncertainty Estimation Olivier Laurent Adrien Lafage Enzo Tartaglione Geoffrey Daniel Jean-Marc Martinez Andrei Bursuc Gianni Franchi OODD 30 32 0 17 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 28 3 0 07 Oct 2022
Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel Sungyub Kim Si-hun Park Kyungsu Kim Eunho Yang BDL 24 4 0 30 Sep 2022
Latent Discriminant deterministic Uncertainty Gianni Franchi Xuanlong Yu Andrei Bursuc Emanuel Aldea Séverine Dubuisson David Filliat UQCV 14 18 0 20 Jul 2022
SLOVA: Uncertainty Estimation Using Single Label One-Vs-All Classifier Bartosz Wójcik J. Grela Marek Śmieja Krzysztof Misztal Jacek Tabor UQCV 20 4 0 28 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 25 28 0 17 Jun 2022
CARD: Classification and Regression Diffusion Models Xizewen Han Huangjie Zheng Mingyuan Zhou DiffM 38 107 0 15 Jun 2022
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients Kyurae Kim Jisu Oh J. Gardner Adji Bousso Dieng Hongseok Kim BDL 24 8 0 13 Jun 2022
Density Regression and Uncertainty Quantification with Bayesian Deep Noise Neural Networks Daiwei Zhang Tianci Liu Jian Kang BDL UQCV 22 2 0 12 Jun 2022
Recent Advances in Bayesian Optimization Xilu Wang Yaochu Jin Sebastian Schmitt Markus Olhofer 27 196 0 07 Jun 2022
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification Juan Maroñas Daniel Hernández-Lobato 11 6 0 30 May 2022
Calibrated Bagging Deep Learning for Image Semantic Segmentation: A Case Study on COVID-19 Chest X-ray Image Lucy Nwosu Xiangfang Li Lijun Qian Seungchan Kim Xishuang Dong 29 3 0 27 May 2022
How Tempering Fixes Data Augmentation in Bayesian Neural Networks Gregor Bachmann Lorenzo Noci Thomas Hofmann BDL AAML 72 8 0 27 May 2022
A General Framework for quantifying Aleatoric and Epistemic uncertainty in Graph Neural Networks Sai Munikoti D. Agarwal Laya Das Balasubramaniam Natarajan BDL UD 18 13 0 20 May 2022
Stochastic Neural Networks with Infinite Width are Deterministic Liu Ziyin Hanlin Zhang Xiangming Meng Yuting Lu Eric P. Xing Masakuni Ueda 21 3 0 30 Jan 2022
GradTail: Learning Long-Tailed Data Using Gradient-based Sample Weighting Zhao Chen Vincent Casser Henrik Kretzschmar Dragomir Anguelov 20 5 0 16 Jan 2022
A Kernel-Expanded Stochastic Neural Network Y. Sun F. Liang 10 5 0 14 Jan 2022
Why Calibration Error is Wrong Given Model Uncertainty: Using Posterior Predictive Checks with Deep Learning Achintya Gopal UQCV 21 1 0 02 Dec 2021
The Devil is in the Margin: Margin-based Label Smoothing for Network Calibration Bingyuan Liu Ismail Ben Ayed Adrian Galdran Jose Dolz UQCV 15 65 0 30 Nov 2021
AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification Romain Egele R. Maulik Krishnan Raghavan Bethany Lusch Isabelle M Guyon Prasanna Balaprakash UQCV OOD BDL 20 45 0 26 Oct 2021
Analysis of the first Genetic Engineering Attribution Challenge O. Crook K. L. Warmbrod G. Lipstein Christine Chung Christopher W. Bakerlee ... Shelly R. Holland Jacob Swett K. Esvelt E. C. Alley W. Bradshaw 11 8 0 14 Oct 2021
Reliable Neural Networks for Regression Uncertainty Estimation Tony Tohme Kevin Vanslette K. Youcef-Toumi UQCV BDL 16 15 0 16 Sep 2021
Stein Variational Gradient Descent with Multiple Kernel Qingzhong Ai Shiyu Liu Lirong He Zenglin Xu 9 4 0 20 Jul 2021
The Bayesian Learning Rule Mohammad Emtiyaz Khan Håvard Rue BDL 48 73 0 09 Jul 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 30 1,108 0 07 Jul 2021
Post-hoc loss-calibration for Bayesian neural networks Meet P. Vadera S. Ghosh Kenney Ng Benjamin M. Marlin UQCV BDL 22 7 0 13 Jun 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 24 124 0 14 May 2021
Exploring Uncertainty in Deep Learning for Construction of Prediction Intervals Yuandu Lai Yucheng Shi Yahong Han Yunfeng Shao Meiyu Qi Bingshuai Li UQCV 25 15 0 27 Apr 2021