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Noisy Natural Gradient as Variational Inference

6 December 2017

Papers citing "Noisy Natural Gradient as Variational Inference"

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Title
Addressing the Inconsistency in Bayesian Deep Learning via Generalized Laplace Approximation Yinsong Chen Samson S. Yu Zhong Li Chee Peng Lim BDL 78 0 0 01 Jul 2025
Spectral-factorized Positive-definite Curvature Learning for NN Training Wu Lin Felix Dangel Runa Eschenhagen Juhan Bae Richard E. Turner Roger B. Grosse 171 0 0 10 Feb 2025
Learning Hyperparameters via a Data-Emphasized Variational Objective Ethan Harvey Mikhail Petrov Michael C. Hughes 116 0 0 03 Feb 2025
Improving Generalization with Flat Hilbert Bayesian Inference Tuan Truong Quyen Tran Quan Pham-Ngoc Nhat Ho Dinh Q. Phung T. Le 71 1 0 05 Oct 2024
Linearization Turns Neural Operators into Function-Valued Gaussian Processes Emilia Magnani Marvin Pfortner Tobias Weber Philipp Hennig UQCV 131 1 0 07 Jun 2024
Scalable Bayesian Learning with posteriors Samuel Duffield Kaelan Donatella Johnathan Chiu Phoebe Klett Daniel Simpson BDL UQCV 178 2 0 31 May 2024
Affine Invariant Ensemble Transform Methods to Improve Predictive Uncertainty in Neural Networks Diksha Bhandari Jakiw Pidstrigach Sebastian Reich 79 1 0 09 Sep 2023
Target Detection on Hyperspectral Images Using MCMC and VI Trained Bayesian Neural Networks Daniel Ries Jason Adams J. Zollweg BDL 69 1 0 11 Aug 2023
Prediction-Oriented Bayesian Active Learning Freddie Bickford-Smith Andreas Kirsch Sebastian Farquhar Y. Gal Adam Foster Tom Rainforth 87 36 0 17 Apr 2023
Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space Michael Diao Krishnakumar Balasubramanian Sinho Chewi Adil Salim BDL 68 29 0 10 Apr 2023
Exploration via Epistemic Value Estimation Simon Schmitt John Shawe-Taylor Hado van Hasselt OffRL 43 3 0 07 Mar 2023
Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples Qizhang Li Yiwen Guo W. Zuo Hao Chen AAML 125 37 0 10 Feb 2023
Hierarchically Structured Task-Agnostic Continual Learning Heinke Hihn Daniel A. Braun BDL CLL 89 9 0 14 Nov 2022
The Implicit Delta Method Nathan Kallus James McInerney 52 2 0 11 Nov 2022
On the optimization and pruning for Bayesian deep learning X. Ke Yanan Fan BDL UQCV 77 1 0 24 Oct 2022
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs Emilia Magnani Nicholas Kramer Runa Eschenhagen Lorenzo Rosasco Philipp Hennig UQCV BDL 62 13 0 02 Aug 2022
Variational inference via Wasserstein gradient flows Marc Lambert Sinho Chewi Francis R. Bach Silvère Bonnabel Philippe Rigollet BDL DRL 101 77 0 31 May 2022
Deep Ensemble as a Gaussian Process Approximate Posterior Zhijie Deng Feng Zhou Jianfei Chen Guoqiang Wu Jun Zhu UQCV 41 5 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 147 48 0 22 Feb 2022
Gradient Descent on Neurons and its Link to Approximate Second-Order Optimization Frederik Benzing ODL 127 26 0 28 Jan 2022
Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning Runa Eschenhagen Erik A. Daxberger Philipp Hennig Agustinus Kristiadi UQCV BDL 73 23 0 05 Nov 2021
A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications Xinlei Zhou Han Liu Farhad Pourpanah T. Zeng Xizhao Wang UQCV UD 124 61 0 03 Nov 2021
Mixture-of-Variational-Experts for Continual Learning Y. Yin Yu Wang CLL FedML 55 6 0 25 Oct 2021
Analytic natural gradient updates for Cholesky factor in Gaussian variational approximation Linda S. L. Tan 108 13 0 01 Sep 2021
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise Guodong Zhang Kyle Hsu Jianing Li Chelsea Finn Roger C. Grosse 87 40 0 21 Jul 2021
The Bayesian Learning Rule Mohammad Emtiyaz Khan Håvard Rue BDL 162 83 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 242 1,177 0 07 Jul 2021
KAISA: An Adaptive Second-Order Optimizer Framework for Deep Neural Networks J. G. Pauloski Qi Huang Lei Huang Shivaram Venkataraman Kyle Chard Ian Foster Zhao-jie Zhang 86 29 0 04 Jul 2021
On the Practicality of Deterministic Epistemic Uncertainty Janis Postels Mattia Segu Tao Sun Luca Sieber Luc Van Gool Feng Yu Federico Tombari UQCV 95 61 0 01 Jul 2021
Being a Bit Frequentist Improves Bayesian Neural Networks Agustinus Kristiadi Matthias Hein Philipp Hennig BDL UQCV 93 16 0 18 Jun 2021
Model Selection for Bayesian Autoencoders Ba-Hien Tran Simone Rossi Dimitrios Milios Pietro Michiardi Edwin V. Bonilla Maurizio Filippone BDL 82 13 0 11 Jun 2021
Data augmentation in Bayesian neural networks and the cold posterior effect Seth Nabarro Stoil Ganev Adrià Garriga-Alonso Vincent Fortuin Mark van der Wilk Laurence Aitchison BDL 92 41 0 10 Jun 2021
Sparse Uncertainty Representation in Deep Learning with Inducing Weights H. Ritter Martin Kukla Chen Zhang Yingzhen Li UQCV BDL 90 17 0 30 May 2021
Learning Uncertainty For Safety-Oriented Semantic Segmentation In Autonomous Driving Victor Besnier David Picard Alexandre Briot UQCV 55 11 0 28 May 2021
Aleatoric uncertainty for Errors-in-Variables models in deep regression J. Martin Clemens Elster UQCV UD BDL 79 9 0 19 May 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 137 134 0 14 May 2021
What Are Bayesian Neural Network Posteriors Really Like? Pavel Izmailov Sharad Vikram Matthew D. Hoffman A. Wilson UQCV BDL 81 389 0 29 Apr 2021
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning Alexander Immer Matthias Bauer Vincent Fortuin Gunnar Rätsch Mohammad Emtiyaz Khan BDL UQCV 150 109 0 11 Apr 2021
Accurate and Reliable Forecasting using Stochastic Differential Equations Peng Cui Zhijie Deng Wenbo Hu Jun Zhu UQCV 75 1 0 28 Mar 2021
Tractable structured natural gradient descent using local parameterizations Wu Lin Frank Nielsen Mohammad Emtiyaz Khan Mark Schmidt 146 30 0 15 Feb 2021
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations Winnie Xu Ricky T. Q. Chen Xuechen Li David Duvenaud BDL UQCV 92 49 0 12 Feb 2021
Bayesian Inference with Certifiable Adversarial Robustness Matthew Wicker Luca Laurenti A. Patané Zhoutong Chen Zheng Zhang Marta Z. Kwiatkowska AAML BDL 126 30 0 10 Feb 2021
Network Automatic Pruning: Start NAP and Take a Nap Wenyuan Zeng Yuwen Xiong R. Urtasun 50 9 0 17 Jan 2021
The Hidden Uncertainty in a Neural Networks Activations Janis Postels Hermann Blum Yannick Strümpler Cesar Cadena Roland Siegwart Luc Van Gool Federico Tombari UQCV 187 22 0 05 Dec 2020
Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantification Gianni Franchi Andrei Bursuc Emanuel Aldea Séverine Dubuisson Isabelle Bloch BDL UQCV 94 27 0 04 Dec 2020
Semi-Supervised Learning with Variational Bayesian Inference and Maximum Uncertainty Regularization Kien Do T. Tran Svetha Venkatesh BDL 53 3 0 03 Dec 2020
Eigenvalue-corrected Natural Gradient Based on a New Approximation Kai-Xin Gao Xiaolei Liu Zheng-Hai Huang Min Wang Shuangling Wang Zidong Wang Dachuan Xu F. Yu ODL 38 7 0 27 Nov 2020
Generalized Variational Continual Learning Noel Loo S. Swaroop Richard Turner BDL CLL 93 60 0 24 Nov 2020
A Trace-restricted Kronecker-Factored Approximation to Natural Gradient Kai-Xin Gao Xiaolei Liu Zheng-Hai Huang Min Wang Zidong Wang Dachuan Xu F. Yu 65 12 0 21 Nov 2020
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians Juhan Bae Roger C. Grosse 73 24 0 26 Oct 2020