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Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural
  Networks

Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks

23 December 2015
Chunyuan Li
Changyou Chen
David Carlson
Lawrence Carin
    ODLBDL
ArXiv (abs)PDFHTML

Papers citing "Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks"

50 / 189 papers shown
Title
An adaptive Hessian approximated stochastic gradient MCMC method
An adaptive Hessian approximated stochastic gradient MCMC methodJournal of Computational Physics (JCP), 2020
Yating Wang
Wei Deng
Guang Lin
BDL
100
5
0
03 Oct 2020
MCMC-Interactive Variational Inference
MCMC-Interactive Variational Inference
Quan Zhang
Huangjie Zheng
Mingyuan Zhou
151
1
0
02 Oct 2020
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC
  via Variance Reduction
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance ReductionInternational Conference on Learning Representations (ICLR), 2020
Wei Deng
Qi Feng
G. Karagiannis
Guang Lin
F. Liang
243
10
0
02 Oct 2020
Stochastic Gradient Langevin Dynamics Algorithms with Adaptive Drifts
Stochastic Gradient Langevin Dynamics Algorithms with Adaptive Drifts
Sehwan Kim
Qifan Song
F. Liang
BDL
104
14
0
20 Sep 2020
Remote sensing image fusion based on Bayesian GAN
Remote sensing image fusion based on Bayesian GAN
Junfu Chen
Yue Pan
Yang Chen
GAN
95
4
0
20 Sep 2020
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
Lingkai Kong
Jimeng Sun
Chao Zhang
UQCV
203
126
0
24 Aug 2020
Uncertainty Estimation in Medical Image Denoising with Bayesian Deep
  Image Prior
Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior
M. Laves
Malte Tolle
T. Ortmaier
UQCVBDLMedIm
83
49
0
20 Aug 2020
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
Non-convex Learning via Replica Exchange Stochastic Gradient MCMCInternational Conference on Machine Learning (ICML), 2020
Wei Deng
Qi Feng
Liyao (Mars) Gao
F. Liang
Guang Lin
BDL
309
48
0
12 Aug 2020
Double-Loop Unadjusted Langevin Algorithm
Double-Loop Unadjusted Langevin Algorithm
Paul Rolland
Armin Eftekhari
Ali Kavis
Volkan Cevher
129
3
0
02 Jul 2020
Bayesian Sparse learning with preconditioned stochastic gradient MCMC
  and its applications
Bayesian Sparse learning with preconditioned stochastic gradient MCMC and its applications
Yating Wang
Wei Deng
Guang Lin
151
13
0
29 Jun 2020
Non-Parametric Graph Learning for Bayesian Graph Neural Networks
Non-Parametric Graph Learning for Bayesian Graph Neural NetworksConference on Uncertainty in Artificial Intelligence (UAI), 2020
Soumyasundar Pal
Saber Malekmohammadi
Florence Regol
Yingxue Zhang
Yishi Xu
Mark Coates
138
13
0
23 Jun 2020
Bayesian Neural Networks: An Introduction and Survey
Bayesian Neural Networks: An Introduction and Survey
Ethan Goan
Clinton Fookes
BDLUQCV
198
251
0
22 Jun 2020
Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference
Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference
Hao Zhang
Bo Chen
Yulai Cong
D. Guo
Hongwei Liu
Mingyuan Zhou
BDL
173
30
0
15 Jun 2020
A benchmark study on reliable molecular supervised learning via Bayesian
  learning
A benchmark study on reliable molecular supervised learning via Bayesian learning
Doyeong Hwang
Grace Lee
Hanseok Jo
Seyoul Yoon
Seongok Ryu
136
9
0
12 Jun 2020
Isotropic SGD: a Practical Approach to Bayesian Posterior Sampling
Isotropic SGD: a Practical Approach to Bayesian Posterior Sampling
Giulio Franzese
Rosa Candela
Dimitrios Milios
Maurizio Filippone
Pietro Michiardi
83
1
0
09 Jun 2020
Secure and Differentially Private Bayesian Learning on Distributed Data
Secure and Differentially Private Bayesian Learning on Distributed Data
Yeongjae Gil
Xiaoqian Jiang
Miran Kim
Junghye Lee
73
2
0
22 May 2020
ESG2Risk: A Deep Learning Framework from ESG News to Stock Volatility
  Prediction
ESG2Risk: A Deep Learning Framework from ESG News to Stock Volatility PredictionSocial Science Research Network (SSRN), 2020
Tian Guo
N. Jamet
Valentin Betrix
Louis-Alexandre Piquet
E. Hauptmann
AIFin
94
34
0
05 May 2020
Estimating Motion Uncertainty with Bayesian ICP
Estimating Motion Uncertainty with Bayesian ICPIEEE International Conference on Robotics and Automation (ICRA), 2020
F. A. Maken
F. Ramos
Lionel Ott
3DPC
96
12
0
16 Apr 2020
Uncertainty quantification in imaging and automatic horizon tracking: a
  Bayesian deep-prior based approach
Uncertainty quantification in imaging and automatic horizon tracking: a Bayesian deep-prior based approachSEG technical program expanded abstracts (STPEA), 2020
Ali Siahkoohi
G. Rizzuti
Felix J. Herrmann
194
20
0
01 Apr 2020
AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic
  Gradient MCMC
AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMCInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2020
Ruqi Zhang
A. Feder Cooper
Christopher De Sa
184
23
0
29 Feb 2020
Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via
  Non-uniform Subsampling of Gradients
Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of GradientsDiscrete and Continuous Dynamical Systems. Series A (DCDS-A), 2020
Ruilin Li
Xin Wang
H. Zha
Molei Tao
156
4
0
20 Feb 2020
DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR
  Predictions in Ad Serving
DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving
Wei Deng
Junwei Pan
Tian Zhou
Deguang Kong
Aaron Flores
Guang Lin
192
4
0
17 Feb 2020
Robust Reinforcement Learning via Adversarial training with Langevin
  Dynamics
Robust Reinforcement Learning via Adversarial training with Langevin DynamicsNeural Information Processing Systems (NeurIPS), 2020
Parameswaran Kamalaruban
Yu-ting Huang
Ya-Ping Hsieh
Paul Rolland
C. Shi
Volkan Cevher
242
61
0
14 Feb 2020
Estimating Uncertainty Intervals from Collaborating Networks
Estimating Uncertainty Intervals from Collaborating NetworksJournal of machine learning research (JMLR), 2020
Tianhui Zhou
Yitong Li
Yuan Wu
David Carlson
UQCV
348
17
0
12 Feb 2020
Extended Stochastic Gradient MCMC for Large-Scale Bayesian Variable
  Selection
Extended Stochastic Gradient MCMC for Large-Scale Bayesian Variable SelectionBiometrika (Biometrika), 2020
Qifan Song
Y. Sun
Mao Ye
F. Liang
BDL
136
19
0
07 Feb 2020
An Equivalence between Bayesian Priors and Penalties in Variational
  Inference
An Equivalence between Bayesian Priors and Penalties in Variational Inference
Pierre Wolinski
Guillaume Charpiat
Yann Ollivier
BDL
149
1
0
01 Feb 2020
Recurrent Hierarchical Topic-Guided RNN for Language Generation
Recurrent Hierarchical Topic-Guided RNN for Language Generation
D. Guo
Bo Chen
Ruiying Lu
Mingyuan Zhou
BDLLRM
196
8
0
21 Dec 2019
Differential Bayesian Neural Nets
Differential Bayesian Neural Nets
Andreas Look
M. Kandemir
BDLUQCV
269
8
0
02 Dec 2019
Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo
Laplacian Smoothing Stochastic Gradient Markov Chain Monte CarloSIAM Journal on Scientific Computing (SISC), 2019
Bao Wang
Difan Zou
Quanquan Gu
Stanley Osher
BDL
92
9
0
02 Nov 2019
Thompson Sampling via Local Uncertainty
Thompson Sampling via Local UncertaintyInternational Conference on Machine Learning (ICML), 2019
Zhendong Wang
Mingyuan Zhou
167
21
0
30 Oct 2019
Stein Variational Gradient Descent With Matrix-Valued Kernels
Stein Variational Gradient Descent With Matrix-Valued KernelsNeural Information Processing Systems (NeurIPS), 2019
Dilin Wang
Ziyang Tang
Minh Nguyen
Qiang Liu
294
65
0
28 Oct 2019
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph
  Learning
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning
Soumyasundar Pal
Florence Regol
Mark Coates
BDLGNN
108
14
0
26 Oct 2019
An Adaptive Empirical Bayesian Method for Sparse Deep Learning
An Adaptive Empirical Bayesian Method for Sparse Deep LearningNeural Information Processing Systems (NeurIPS), 2019
Wei Deng
Xiao Zhang
F. Liang
Guang Lin
BDL
228
50
0
23 Oct 2019
Characterizing Membership Privacy in Stochastic Gradient Langevin
  Dynamics
Characterizing Membership Privacy in Stochastic Gradient Langevin DynamicsAAAI Conference on Artificial Intelligence (AAAI), 2019
Abeer Alshehri
Chaochao Chen
Shiwan Zhao
Cen Chen
Xingtai Lv
Guangyu Sun
L. Sonenberg
Xiaolu Zhang
Jun Zhou
BDL
142
23
0
05 Oct 2019
Deep Learning Theory Review: An Optimal Control and Dynamical Systems
  Perspective
Deep Learning Theory Review: An Optimal Control and Dynamical Systems Perspective
Guan-Horng Liu
Evangelos A. Theodorou
AI4CE
266
74
0
28 Aug 2019
Variationally Inferred Sampling Through a Refined Bound for
  Probabilistic Programs
Variationally Inferred Sampling Through a Refined Bound for Probabilistic Programs
Víctor Gallego
D. Insua
BDL
265
1
0
26 Aug 2019
Icebreaker: Element-wise Active Information Acquisition with Bayesian
  Deep Latent Gaussian Model
Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model
Wenbo Gong
Sebastian Tschiatschek
Richard Turner
Sebastian Nowozin
José Miguel Hernández-Lobato
Cheng Zhang
BDL
139
19
0
13 Aug 2019
Bayesian Inference for Large Scale Image Classification
Bayesian Inference for Large Scale Image Classification
Jonathan Heek
Nal Kalchbrenner
UQCVBDL
193
36
0
09 Aug 2019
Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal
Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal
Tung-Yu Wu
Y. X. R. Wang
W. Wong
249
14
0
08 Aug 2019
Probabilistic Approximate Logic and its Implementation in the Logical
  Imagination Engine
Probabilistic Approximate Logic and its Implementation in the Logical Imagination Engine
Mark-Oliver Stehr
Minyoung Kim
C. Talcott
M. Knapp
A. Vertes
100
2
0
25 Jul 2019
Adaptively Preconditioned Stochastic Gradient Langevin Dynamics
Adaptively Preconditioned Stochastic Gradient Langevin Dynamics
C. A. Bhardwaj
ODL
89
11
0
10 Jun 2019
A Bayesian Perspective on the Deep Image Prior
A Bayesian Perspective on the Deep Image Prior
Zezhou Cheng
Matheus Gadelha
Subhransu Maji
Daniel Sheldon
BDLUQCV
125
145
0
16 Apr 2019
TATi-Thermodynamic Analytics ToolkIt: TensorFlow-based software for
  posterior sampling in machine learning applications
TATi-Thermodynamic Analytics ToolkIt: TensorFlow-based software for posterior sampling in machine learning applications
Frederik Heber
Zofia Trstanova
Benedict Leimkuhler
140
0
0
20 Mar 2019
On Transformations in Stochastic Gradient MCMC
On Transformations in Stochastic Gradient MCMC
Soma Yokoi
Takuma Otsuka
Issei Sato
179
1
0
07 Mar 2019
Function Space Particle Optimization for Bayesian Neural Networks
Function Space Particle Optimization for Bayesian Neural NetworksInternational Conference on Learning Representations (ICLR), 2019
Ziyu Wang
Zhaolin Ren
Jun Zhu
Bo Zhang
BDL
169
68
0
26 Feb 2019
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
Cyclical Stochastic Gradient MCMC for Bayesian Deep LearningInternational Conference on Learning Representations (ICLR), 2019
Ruqi Zhang
Chunyuan Li
Jianyi Zhang
Changyou Chen
A. Wilson
BDL
239
290
0
11 Feb 2019
Semantic Image Networks for Human Action Recognition
Semantic Image Networks for Human Action Recognition
Sunder Ali Khowaja
Seok-Lyong Lee
97
35
0
21 Jan 2019
Sampling-based Bayesian Inference with gradient uncertainty
Sampling-based Bayesian Inference with gradient uncertainty
Chanwoo Park
Jae Myung Kim
Seokhyeon Ha
Jungwook Lee
UQCVBDL
182
8
0
08 Dec 2018
Bayesian graph convolutional neural networks for semi-supervised
  classification
Bayesian graph convolutional neural networks for semi-supervised classification
Yingxue Zhang
Soumyasundar Pal
Mark Coates
Deniz Üstebay
GNNBDL
185
244
0
27 Nov 2018
The promises and pitfalls of Stochastic Gradient Langevin Dynamics
The promises and pitfalls of Stochastic Gradient Langevin DynamicsNeural Information Processing Systems (NeurIPS), 2018
N. Brosse
Alain Durmus
Eric Moulines
217
87
0
25 Nov 2018
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