Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning

11 February 2019

Jianyi Zhang

Papers citing "Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning"

37 / 87 papers shown

Title
Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer Shiye Lei Zhuozhuo Tu Leszek Rutkowski Feng Zhou Li Shen Fengxiang He Dacheng Tao BDL 28 2 0 12 Dec 2021
Why Calibration Error is Wrong Given Model Uncertainty: Using Posterior Predictive Checks with Deep Learning Achintya Gopal UQCV 30 1 0 02 Dec 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 29 58 0 03 Nov 2021
Uncertainty quantification in non-rigid image registration via stochastic gradient Markov chain Monte Carlo Daniel Grzech Mohammad Farid Azampour Huaqi Qiu Ben Glocker Bernhard Kainz Loic Le Folgoc MedIm 19 2 0 25 Oct 2021
Pathologies in priors and inference for Bayesian transformers Tristan Cinquin Alexander Immer Max Horn Vincent Fortuin UQCV BDL MedIm 39 9 0 08 Oct 2021
Robust Contrastive Active Learning with Feature-guided Query Strategies R. Krishnan Nilesh A. Ahuja Alok Sinha Mahesh Subedar Omesh Tickoo Ravi Iyer 26 1 0 13 Sep 2021
Bayesian Active Meta-Learning for Few Pilot Demodulation and Equalization K. Cohen Sangwoo Park Osvaldo Simeone S. Shamai 32 12 0 02 Aug 2021
Stein Variational Gradient Descent with Multiple Kernel Qingzhong Ai Shiyu Liu Lirong He Zenglin Xu 22 4 0 20 Jul 2021
Laplace Redux -- Effortless Bayesian Deep Learning Erik A. Daxberger Agustinus Kristiadi Alexander Immer Runa Eschenhagen Matthias Bauer Philipp Hennig BDL UQCV 63 290 0 28 Jun 2021
Repulsive Deep Ensembles are Bayesian Francesco DÁngelo Vincent Fortuin UQCV BDL 64 95 0 22 Jun 2021
Schr{ö}dinger-F{ö}llmer Sampler: Sampling without Ergodicity Jian Huang Yuling Jiao Lican Kang Xu Liao Jin Liu Yanyan Liu 40 27 0 21 Jun 2021
Being a Bit Frequentist Improves Bayesian Neural Networks Agustinus Kristiadi Matthias Hein Philipp Hennig BDL UQCV 23 15 0 18 Jun 2021
Post-hoc loss-calibration for Bayesian neural networks Meet P. Vadera S. Ghosh Kenney Ng Benjamin M. Marlin UQCV BDL 43 7 0 13 Jun 2021
Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect Lorenzo Noci Kevin Roth Gregor Bachmann Sebastian Nowozin Thomas Hofmann CML 30 23 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 32 37 0 10 Jun 2021
Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks Dong-Young Lim Sotirios Sabanis 39 11 0 28 May 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 38 124 0 14 May 2021
What Are Bayesian Neural Network Posteriors Really Like? Pavel Izmailov Sharad Vikram Matthew D. Hoffman A. Wilson UQCV BDL 28 374 0 29 Apr 2021
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure Samuel Kim Peter Y. Lu Charlotte Loh Jamie Smith Jasper Snoek M. Soljavcić BDL AI4CE 160 17 0 23 Apr 2021
The Promises and Pitfalls of Deep Kernel Learning Sebastian W. Ober C. Rasmussen Mark van der Wilk UQCV BDL 21 107 0 24 Feb 2021
Learning Neural Network Subspaces Mitchell Wortsman Maxwell Horton Carlos Guestrin Ali Farhadi Mohammad Rastegari UQCV 27 85 0 20 Feb 2021
DEUP: Direct Epistemic Uncertainty Prediction Salem Lahlou Moksh Jain Hadi Nekoei V. Butoi Paul Bertin Jarrid Rector-Brooks Maksym Korablyov Yoshua Bengio PER UQLM UQCV UD 212 81 0 16 Feb 2021
Bayesian Inference Forgetting Shaopeng Fu Fengxiang He Yue Xu Dacheng Tao MU 28 12 0 16 Jan 2021
DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation Alexandre Ramé Matthieu Cord FedML 56 51 0 14 Jan 2021
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions Wei Deng Guang Lin F. Liang BDL 47 27 0 19 Oct 2020
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction Wei Deng Qi Feng G. Karagiannis Guang Lin F. Liang 38 9 0 02 Oct 2020
URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks Meet P. Vadera Adam D. Cobb B. Jalaeian Benjamin M. Marlin BDL UQCV 27 16 0 08 Jul 2020
Drug discovery with explainable artificial intelligence José Jiménez-Luna F. Grisoni G. Schneider 30 627 0 01 Jul 2020
Hyperparameter Ensembles for Robustness and Uncertainty Quantification F. Wenzel Jasper Snoek Dustin Tran Rodolphe Jenatton UQCV 33 204 0 24 Jun 2020
AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC Ruqi Zhang A. Feder Cooper Christopher De Sa 21 18 0 29 Feb 2020
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning Arsenii Ashukha Alexander Lyzhov Dmitry Molchanov Dmitry Vetrov UQCV FedML 38 314 0 15 Feb 2020
Deep Ensembles: A Loss Landscape Perspective Stanislav Fort Huiyi Hu Balaji Lakshminarayanan OOD UQCV 41 620 0 05 Dec 2019
Implicit Posterior Variational Inference for Deep Gaussian Processes Haibin Yu Yizhou Chen Zhongxiang Dai K. H. Low Patrick Jaillet 27 42 0 26 Oct 2019
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,695 0 05 Dec 2016
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators Changyou Chen Nan Ding Lawrence Carin 40 159 0 21 Oct 2016
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 310 2,896 0 15 Sep 2016
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 186 1,186 0 30 Nov 2014