Papers
Communities
Events
Blog
Pricing
Search
Open menu
Home
Papers
1512.07666
Cited By
Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks
23 December 2015
Chunyuan Li
Changyou Chen
David Carlson
Lawrence Carin
ODL
BDL
Re-assign community
ArXiv
PDF
HTML
Papers citing
"Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks"
50 / 51 papers shown
Title
Cooperative Bayesian and variance networks disentangle aleatoric and epistemic uncertainties
Jiaxiang Yi
Miguel A. Bessa
UD
PER
UQCV
39
0
0
05 May 2025
Bayesian Computation in Deep Learning
Wenlong Chen
Bolian Li
Ruqi Zhang
Yingzhen Li
BDL
75
0
0
25 Feb 2025
Particle Semi-Implicit Variational Inference
Jen Ning Lim
A. M. Johansen
41
3
0
30 Jun 2024
Reparameterization invariance in approximate Bayesian inference
Hrittik Roy
M. Miani
Carl Henrik Ek
Philipp Hennig
Marvin Pfortner
Lukas Tatzel
Søren Hauberg
BDL
42
8
0
05 Jun 2024
Scalable Bayesian Learning with posteriors
Samuel Duffield
Kaelan Donatella
Johnathan Chiu
Phoebe Klett
Daniel Simpson
BDL
UQCV
54
1
0
31 May 2024
Scalable Bayesian inference for the generalized linear mixed model
S. Berchuck
Felipe A. Medeiros
Sayan Mukherjee
Andrea Agazzi
24
0
0
05 Mar 2024
Neural parameter calibration and uncertainty quantification for epidemic forecasting
Thomas Gaskin
Tim Conrad
G. Pavliotis
Christof Schütte
13
1
0
05 Dec 2023
High-Rate Phase Association with Travel Time Neural Fields
Chengzhi Shi
Maarten V. de Hoop
Ivan Dokmanić
19
1
0
14 Jul 2023
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo
Haque Ishfaq
Qingfeng Lan
Pan Xu
A. R. Mahmood
Doina Precup
Anima Anandkumar
Kamyar Azizzadenesheli
BDL
OffRL
18
20
0
29 May 2023
Particle Mean Field Variational Bayes
Minh-Ngoc Tran
Paco Tseng
Robert Kohn
24
3
0
24 Mar 2023
Preconditioned Score-based Generative Models
He Ma
Xiatian Zhu
Xiatian Zhu
Jianfeng Feng
DiffM
30
4
0
13 Feb 2023
Langevin algorithms for Markovian Neural Networks and Deep Stochastic control
Pierre Bras
Gilles Pagès
14
3
0
22 Dec 2022
Effects of Spectral Normalization in Multi-agent Reinforcement Learning
K. Mehta
Anuj Mahajan
Priyesh Kumar
16
7
0
10 Dec 2022
Particle-based Variational Inference with Preconditioned Functional Gradient Flow
Hanze Dong
Xi Wang
Yong Lin
Tong Zhang
22
19
0
25 Nov 2022
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms
Vincent Plassier
Alain Durmus
Eric Moulines
FedML
14
6
0
31 Oct 2022
Automatic differentiation and the optimization of differential equation models in biology
S. Frank
14
6
0
10 Jul 2022
How Tempering Fixes Data Augmentation in Bayesian Neural Networks
Gregor Bachmann
Lorenzo Noci
Thomas Hofmann
BDL
AAML
74
8
0
27 May 2022
The Sufficiency of Off-Policyness and Soft Clipping: PPO is still Insufficient according to an Off-Policy Measure
Xing Chen
Dongcui Diao
Hechang Chen
Hengshuai Yao
Haiyin Piao
Zhixiao Sun
Zhiwei Yang
Randy Goebel
Bei Jiang
Yi-Ju Chang
OffRL
15
8
0
20 May 2022
Optimizing differential equations to fit data and predict outcomes
S. Frank
11
4
0
16 Apr 2022
PAC-Bayesian Lifelong Learning For Multi-Armed Bandits
H. Flynn
David Reeb
M. Kandemir
Jan Peters
12
7
0
07 Mar 2022
Interacting Contour Stochastic Gradient Langevin Dynamics
Wei Deng
Siqi Liang
Botao Hao
Guang Lin
F. Liang
BDL
21
10
0
20 Feb 2022
Approximate Inference via Clustering
Qianqian Song
29
0
0
28 Nov 2021
Unsupervised PET Reconstruction from a Bayesian Perspective
Chenyu Shen
Wenjun Xia
H. Ye
Mingzheng Hou
Hu Chen
Yan Liu
Jiliu Zhou
Yi Zhang
31
3
0
29 Oct 2021
Deep Bayesian inference for seismic imaging with tasks
Ali Siahkoohi
G. Rizzuti
Felix J. Herrmann
BDL
UQCV
24
21
0
10 Oct 2021
Batch Normalization Preconditioning for Neural Network Training
Susanna Lange
Kyle E. Helfrich
Qiang Ye
19
9
0
02 Aug 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
Sampling with Mirrored Stein Operators
Jiaxin Shi
Chang-rui Liu
Lester W. Mackey
37
19
0
23 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
22
23
0
11 Jun 2021
Contextual Dropout: An Efficient Sample-Dependent Dropout Module
Xinjie Fan
Shujian Zhang
Korawat Tanwisuth
Xiaoning Qian
Mingyuan Zhou
OOD
BDL
UQCV
19
27
0
06 Mar 2021
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions
Wei Deng
Guang Lin
F. Liang
BDL
34
27
0
19 Oct 2020
Bayesian Neural Networks: An Introduction and Survey
Ethan Goan
Clinton Fookes
BDL
UQCV
21
199
0
22 Jun 2020
Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference
Hao Zhang
Bo Chen
Yulai Cong
D. Guo
Hongwei Liu
Mingyuan Zhou
BDL
18
27
0
15 Jun 2020
A benchmark study on reliable molecular supervised learning via Bayesian learning
Doyeong Hwang
Grace Lee
Hanseok Jo
Seyoul Yoon
Seongok Ryu
9
9
0
12 Jun 2020
Estimating Motion Uncertainty with Bayesian ICP
F. A. Maken
F. Ramos
Lionel Ott
3DPC
6
9
0
16 Apr 2020
Uncertainty quantification in imaging and automatic horizon tracking: a Bayesian deep-prior based approach
Ali Siahkoohi
G. Rizzuti
Felix J. Herrmann
25
20
0
01 Apr 2020
Robust Reinforcement Learning via Adversarial training with Langevin Dynamics
Parameswaran Kamalaruban
Yu-ting Huang
Ya-Ping Hsieh
Paul Rolland
C. Shi
V. Cevher
15
59
0
14 Feb 2020
Thompson Sampling via Local Uncertainty
Zhendong Wang
Mingyuan Zhou
16
18
0
30 Oct 2019
Stein Variational Gradient Descent With Matrix-Valued Kernels
Dilin Wang
Ziyang Tang
Chandrajit L. Bajaj
Qiang Liu
17
62
0
28 Oct 2019
Bayesian graph convolutional neural networks for semi-supervised classification
Yingxue Zhang
Soumyasundar Pal
Mark J. Coates
Deniz Üstebay
GNN
BDL
19
227
0
27 Nov 2018
Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory
Jianyi Zhang
Ruiyi Zhang
Lawrence Carin
Changyou Chen
10
46
0
05 Sep 2018
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors
S. Ghosh
Jiayu Yao
Finale Doshi-Velez
BDL
UQCV
13
77
0
13 Jun 2018
Meta-Learning for Stochastic Gradient MCMC
Wenbo Gong
Yingzhen Li
José Miguel Hernández-Lobato
BDL
16
44
0
12 Jun 2018
A Unified Particle-Optimization Framework for Scalable Bayesian Sampling
Changyou Chen
Ruiyi Zhang
Wenlin Wang
Bai Li
Liqun Chen
13
86
0
29 May 2018
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
C. Riquelme
George Tucker
Jasper Snoek
BDL
22
366
0
26 Feb 2018
On Connecting Stochastic Gradient MCMC and Differential Privacy
Bai Li
Changyou Chen
Hao Liu
Lawrence Carin
25
38
0
25 Dec 2017
Continuous-Time Flows for Efficient Inference and Density Estimation
Changyou Chen
Chunyuan Li
Liquan Chen
Wenlin Wang
Yunchen Pu
Lawrence Carin
TPM
32
57
0
04 Sep 2017
Fractional Langevin Monte Carlo: Exploring Lévy Driven Stochastic Differential Equations for Markov Chain Monte Carlo
Umut Simsekli
14
45
0
12 Jun 2017
Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling
Zhe Gan
Chunyuan Li
Changyou Chen
Yunchen Pu
Qinliang Su
Lawrence Carin
BDL
UQCV
34
41
0
23 Nov 2016
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators
Changyou Chen
Nan Ding
Lawrence Carin
32
158
0
21 Oct 2016
Stochastic Quasi-Newton Langevin Monte Carlo
Umut Simsekli
Roland Badeau
A. Cemgil
G. Richard
BDL
12
59
0
10 Feb 2016
1
2
Next