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1908.03491
Cited By
Bayesian Inference for Large Scale Image Classification
9 August 2019
Jonathan Heek
Nal Kalchbrenner
UQCV
BDL
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Papers citing
"Bayesian Inference for Large Scale Image Classification"
25 / 25 papers shown
A Context-Aware Dual-Metric Framework for Confidence Estimation in Large Language Models
Mingruo Yuan
Shuyi Zhang
Ben Kao
223
0
0
01 Aug 2025
Addressing the Inconsistency in Bayesian Deep Learning via Generalized Laplace Approximation
Yinsong Chen
Samson S. Yu
Zhong Li
Chee Peng Lim
BDL
535
0
0
22 May 2024
Piecewise Deterministic Markov Processes for Bayesian Neural Networks
Conference on Uncertainty in Artificial Intelligence (UAI), 2023
Ethan Goan
Dimitri Perrin
Kerrie Mengersen
Clinton Fookes
220
0
0
17 Feb 2023
Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls
Journal of Chemical Theory and Computation (JCTC), 2022
Stephan Thaler
Gregor Doehner
Julija Zavadlav
378
28
0
15 Dec 2022
On the optimization and pruning for Bayesian deep learning
X. Ke
Yanan Fan
BDL
UQCV
285
2
0
24 Oct 2022
Low-Precision Stochastic Gradient Langevin Dynamics
International Conference on Machine Learning (ICML), 2022
Ruqi Zhang
A. Wilson
Chris De Sa
BDL
215
18
0
20 Jun 2022
Masked Bayesian Neural Networks : Computation and Optimality
Insung Kong
Dongyoon Yang
Jongjin Lee
Ilsang Ohn
Yongdai Kim
TPM
352
1
0
02 Jun 2022
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification
Neural Information Processing Systems (NeurIPS), 2022
Sanyam Kapoor
Wesley J. Maddox
Pavel Izmailov
A. Wilson
BDL
UD
305
59
0
30 Mar 2022
A deep mixture density network for outlier-corrected interpolation of crowd-sourced weather data
Charlie Kirkwood
T. Economou
H. Odbert
N. Pugeault
210
0
0
25 Jan 2022
Structured Stochastic Gradient MCMC
International Conference on Machine Learning (ICML), 2021
Antonios Alexos
Alex Boyd
Stephan Mandt
BDL
375
14
0
19 Jul 2021
Quantifying Uncertainty in Deep Spatiotemporal Forecasting
Knowledge Discovery and Data Mining (KDD), 2021
Dongxian Wu
Liyao (Mars) Gao
X. Xiong
Matteo Chinazzi
Alessandro Vespignani
Yi-An Ma
Rose Yu
AI4TS
261
85
0
25 May 2021
Sampling-free Variational Inference for Neural Networks with Multiplicative Activation Noise
German Conference on Pattern Recognition (DAGM), 2021
Jannik Schmitt
Stefan Roth
UQCV
242
6
0
15 Mar 2021
The Promises and Pitfalls of Deep Kernel Learning
Conference on Uncertainty in Artificial Intelligence (UAI), 2021
Sebastian W. Ober
C. Rasmussen
Mark van der Wilk
UQCV
BDL
449
122
0
24 Feb 2021
DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19 forecasting
Dongxian Wu
Liyao (Mars) Gao
X. Xiong
Matteo Chinazzi
Alessandro Vespignani
Yi-An Ma
Rose Yu
FedML
231
32
0
12 Feb 2021
Bayesian Neural Network Priors Revisited
International Conference on Learning Representations (ICLR), 2021
Vincent Fortuin
Adrià Garriga-Alonso
Sebastian W. Ober
F. Wenzel
Gunnar Rätsch
Richard Turner
Mark van der Wilk
Laurence Aitchison
BDL
UQCV
450
159
0
12 Feb 2021
All You Need is a Good Functional Prior for Bayesian Deep Learning
Journal of machine learning research (JMLR), 2020
Ba-Hien Tran
Simone Rossi
Dimitrios Milios
Maurizio Filippone
OOD
BDL
539
77
0
25 Nov 2020
Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning
Luiz F. O. Chamon
Santiago Paternain
Alejandro Ribeiro
AAML
124
1
0
24 Nov 2020
Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting
Adam D. Cobb
Brian Jalaian
BDL
293
88
0
14 Oct 2020
Langevin Cooling for Domain Translation
Vignesh Srinivasan
Klaus-Robert Muller
Wojciech Samek
Shinichi Nakajima
258
1
0
31 Aug 2020
Predictive Complexity Priors
Eric T. Nalisnick
Jonathan Gordon
José Miguel Hernández-Lobato
BDL
UQCV
462
19
0
18 Jun 2020
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
Michael W. Dusenberry
Ghassen Jerfel
Yeming Wen
Yi-An Ma
Jasper Snoek
Katherine A. Heller
Balaji Lakshminarayanan
Dustin Tran
UQCV
BDL
523
236
0
14 May 2020
Being Bayesian about Categorical Probability
International Conference on Machine Learning (ICML), 2020
Taejong Joo
U. Chung
Minji Seo
UQCV
BDL
361
68
0
19 Feb 2020
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning
International Conference on Learning Representations (ICLR), 2020
Arsenii Ashukha
Alexander Lyzhov
Dmitry Molchanov
Dmitry Vetrov
UQCV
FedML
617
349
0
15 Feb 2020
The reproducing Stein kernel approach for post-hoc corrected sampling
Liam Hodgkinson
R. Salomone
Fred Roosta
328
29
0
25 Jan 2020
Distance-Based Learning from Errors for Confidence Calibration
International Conference on Learning Representations (ICLR), 2019
Chen Xing
Sercan O. Arik
Zizhao Zhang
Tomas Pfister
FedML
249
42
0
03 Dec 2019
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