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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
Scalable Stochastic Gradient Riemannian Langevin Dynamics in
  Non-Diagonal Metrics
Scalable Stochastic Gradient Riemannian Langevin Dynamics in Non-Diagonal Metrics
Hanlin Yu
M. Hartmann
Bernardo Williams
Arto Klami
BDL
323
9
0
09 Mar 2023
Gradient-based Wang-Landau Algorithm: A Novel Sampler for Output
  Distribution of Neural Networks over the Input Space
Gradient-based Wang-Landau Algorithm: A Novel Sampler for Output Distribution of Neural Networks over the Input SpaceInternational Conference on Machine Learning (ICML), 2023
Weitang Liu
Ying-Wai Li
Yi-Zhuang You
Jingbo Shang
119
2
0
19 Feb 2023
Piecewise Deterministic Markov Processes for Bayesian Neural Networks
Piecewise Deterministic Markov Processes for Bayesian Neural NetworksConference on Uncertainty in Artificial Intelligence (UAI), 2023
Ethan Goan
Dimitri Perrin
Kerrie Mengersen
Clinton Fookes
175
0
0
17 Feb 2023
Preconditioned Score-based Generative Models
Preconditioned Score-based Generative ModelsInternational Journal of Computer Vision (IJCV), 2023
He Ma
Xiatian Zhu
Xiatian Zhu
Jianfeng Feng
DiffM
241
7
0
13 Feb 2023
Langevin algorithms for very deep Neural Networks with application to
  image classification
Langevin algorithms for very deep Neural Networks with application to image classification
Pierre Bras
151
6
0
27 Dec 2022
Langevin algorithms for Markovian Neural Networks and Deep Stochastic
  control
Langevin algorithms for Markovian Neural Networks and Deep Stochastic controlIEEE International Joint Conference on Neural Network (IJCNN), 2022
Pierre Bras
Gilles Pagès
156
6
0
22 Dec 2022
Scalable Bayesian Uncertainty Quantification for Neural Network
  Potentials: Promise and Pitfalls
Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and PitfallsJournal of Chemical Theory and Computation (JCTC), 2022
Stephan Thaler
Gregor Doehner
Julija Zavadlav
245
24
0
15 Dec 2022
Effects of Spectral Normalization in Multi-agent Reinforcement Learning
Effects of Spectral Normalization in Multi-agent Reinforcement LearningIEEE International Joint Conference on Neural Network (IJCNN), 2022
K. Mehta
Anuj Mahajan
Kiran Ravish
213
11
0
10 Dec 2022
PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental
  Comparison
PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental ComparisonIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022
H. Flynn
David Reeb
M. Kandemir
Jan Peters
OffRL
274
7
0
29 Nov 2022
Particle-based Variational Inference with Preconditioned Functional
  Gradient Flow
Particle-based Variational Inference with Preconditioned Functional Gradient FlowInternational Conference on Learning Representations (ICLR), 2022
Hanze Dong
Xi Wang
Yong Lin
Tong Zhang
195
21
0
25 Nov 2022
Non-reversible Parallel Tempering for Deep Posterior Approximation
Non-reversible Parallel Tempering for Deep Posterior ApproximationAAAI Conference on Artificial Intelligence (AAAI), 2022
Wei Deng
Qian Zhang
Qi Feng
F. Liang
Guang Lin
217
4
0
20 Nov 2022
Federated Averaging Langevin Dynamics: Toward a unified theory and new
  algorithms
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithmsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Vincent Plassier
Alain Durmus
Eric Moulines
FedML
354
7
0
31 Oct 2022
Reliable amortized variational inference with physics-based latent
  distribution correction
Reliable amortized variational inference with physics-based latent distribution correction
Ali Siahkoohi
G. Rizzuti
Rafael Orozco
Felix J. Herrmann
231
32
0
24 Jul 2022
Automatic differentiation and the optimization of differential equation
  models in biology
Automatic differentiation and the optimization of differential equation models in biologyFrontiers in Ecology and Evolution (Front. Ecol. Evol.), 2022
S. Frank
129
6
0
10 Jul 2022
Accelerating Score-based Generative Models with Preconditioned Diffusion
  Sampling
Accelerating Score-based Generative Models with Preconditioned Diffusion SamplingEuropean Conference on Computer Vision (ECCV), 2022
He Ma
Li Zhang
Xiatian Zhu
Jianfeng Feng
DiffM
189
30
0
05 Jul 2022
A Langevin-like Sampler for Discrete Distributions
A Langevin-like Sampler for Discrete DistributionsInternational Conference on Machine Learning (ICML), 2022
Ruqi Zhang
Xingchao Liu
Qiang Liu
BDL
155
46
0
20 Jun 2022
Benchmarking Bayesian neural networks and evaluation metrics for
  regression tasks
Benchmarking Bayesian neural networks and evaluation metrics for regression tasks
B. Staber
Sébastien Da Veiga
UQCVBDL
226
3
0
08 Jun 2022
Masked Bayesian Neural Networks : Computation and Optimality
Insung Kong
Dongyoon Yang
Jongjin Lee
Ilsang Ohn
Yongdai Kim
TPM
250
1
0
02 Jun 2022
How Tempering Fixes Data Augmentation in Bayesian Neural Networks
How Tempering Fixes Data Augmentation in Bayesian Neural NetworksInternational Conference on Machine Learning (ICML), 2022
Gregor Bachmann
Lorenzo Noci
Thomas Hofmann
BDLAAML
229
11
0
27 May 2022
The Sufficiency of Off-Policyness and Soft Clipping: PPO is still
  Insufficient according to an Off-Policy Measure
The Sufficiency of Off-Policyness and Soft Clipping: PPO is still Insufficient according to an Off-Policy MeasureAAAI Conference on Artificial Intelligence (AAAI), 2022
Xing Chen
Dongcui Diao
Hechang Chen
Hengshuai Yao
Haiyin Piao
Zhixiao Sun
Zhiwei Yang
Randy Goebel
Bei Jiang
Yi-Ju Chang
OffRL
369
23
0
20 May 2022
A Survey on Uncertainty Toolkits for Deep Learning
A Survey on Uncertainty Toolkits for Deep Learning
Maximilian Pintz
Joachim Sicking
Maximilian Poretschkin
Maram Akila
ELM
245
7
0
02 May 2022
Optimizing differential equations to fit data and predict outcomes
Optimizing differential equations to fit data and predict outcomesEcology and Evolution (Ecol Evol), 2022
S. Frank
80
4
0
16 Apr 2022
VFDS: Variational Foresight Dynamic Selection in Bayesian Neural
  Networks for Efficient Human Activity Recognition
VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity RecognitionInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Randy Ardywibowo
Shahin Boluki
Zinan Lin
Bobak J. Mortazavi
Shuai Huang
Xiaoning Qian
157
2
0
31 Mar 2022
DiSECt: A Differentiable Simulator for Parameter Inference and Control
  in Robotic Cutting
DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic CuttingAutonomous Robots (AR), 2022
Eric Heiden
Lukasz Wawrzyniak
Yashraj S. Narang
Dieter Fox
Animesh Garg
Fabio Ramos
253
13
0
19 Mar 2022
PAC-Bayesian Lifelong Learning For Multi-Armed Bandits
PAC-Bayesian Lifelong Learning For Multi-Armed BanditsData mining and knowledge discovery (DMKD), 2022
H. Flynn
David Reeb
M. Kandemir
Jan Peters
182
8
0
07 Mar 2022
Interacting Contour Stochastic Gradient Langevin Dynamics
Interacting Contour Stochastic Gradient Langevin DynamicsInternational Conference on Learning Representations (ICLR), 2022
Wei Deng
Siqi Liang
Botao Hao
Guang Lin
F. Liang
BDL
174
12
0
20 Feb 2022
Nonlinear MCMC for Bayesian Machine Learning
Nonlinear MCMC for Bayesian Machine LearningNeural Information Processing Systems (NeurIPS), 2022
James Vuckovic
175
3
0
11 Feb 2022
Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class
  Annealing
Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class AnnealingInternational Conference on Learning Representations (ICLR), 2021
Renyu Zhang
Aly A. Khan
Robert L. Grossman
Yuxin Chen
BDL
209
4
0
27 Dec 2021
Approximate Inference via Clustering
Approximate Inference via Clustering
Qianqian Song
174
0
0
28 Nov 2021
Unsupervised PET Reconstruction from a Bayesian Perspective
Unsupervised PET Reconstruction from a Bayesian PerspectiveIEEE International Symposium on Biomedical Imaging (ISBI), 2021
Chenyu Shen
Wenjun Xia
H. Ye
Mingzheng Hou
Hu Chen
Yan Liu
Jiliu Zhou
Yi Zhang
187
3
0
29 Oct 2021
Deep Bayesian inference for seismic imaging with tasks
Deep Bayesian inference for seismic imaging with tasks
Ali Siahkoohi
G. Rizzuti
Felix J. Herrmann
BDLUQCV
333
23
0
10 Oct 2021
A fast asynchronous MCMC sampler for sparse Bayesian inference
A fast asynchronous MCMC sampler for sparse Bayesian inference
Yves F. Atchadé
Liwei Wang
126
3
0
14 Aug 2021
Batch Normalization Preconditioning for Neural Network Training
Batch Normalization Preconditioning for Neural Network Training
Susanna Lange
Kyle E. Helfrich
Qiang Ye
197
13
0
02 Aug 2021
Structured Stochastic Gradient MCMC
Structured Stochastic Gradient MCMCInternational Conference on Machine Learning (ICML), 2021
Antonios Alexos
Alex Boyd
Stephan Mandt
BDL
223
13
0
19 Jul 2021
Differentially Private Bayesian Neural Networks on Accuracy, Privacy and
  Reliability
Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability
Qiyiwen Zhang
Zhiqi Bu
Kan Chen
Qi Long
BDLUQCV
194
12
0
18 Jul 2021
A Survey of Uncertainty in Deep Neural Networks
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
BDLUQCVOOD
527
1,459
0
07 Jul 2021
Sampling with Mirrored Stein Operators
Sampling with Mirrored Stein Operators
Jiaxin Shi
Chang-rui Liu
Lester W. Mackey
334
21
0
23 Jun 2021
Disentangling the Roles of Curation, Data-Augmentation and the Prior in
  the Cold Posterior Effect
Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior EffectNeural Information Processing Systems (NeurIPS), 2021
Lorenzo Noci
Kevin Roth
Gregor Bachmann
Sebastian Nowozin
Thomas Hofmann
CML
198
26
0
11 Jun 2021
When in Doubt: Neural Non-Parametric Uncertainty Quantification for
  Epidemic Forecasting
When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic ForecastingNeural Information Processing Systems (NeurIPS), 2021
Harshavardhan Kamarthi
Lingkai Kong
Alexander Rodríguez
Chao Zhang
B. Prakash
AI4TSBDL
227
25
0
07 Jun 2021
Asymptotics of representation learning in finite Bayesian neural
  networks
Asymptotics of representation learning in finite Bayesian neural networksNeural Information Processing Systems (NeurIPS), 2021
Jacob A. Zavatone-Veth
Abdulkadir Canatar
Benjamin S. Ruben
Cengiz Pehlevan
357
36
0
01 Jun 2021
Efficient and Generalizable Tuning Strategies for Stochastic Gradient
  MCMC
Efficient and Generalizable Tuning Strategies for Stochastic Gradient MCMCStatistics and computing (Stat Comput), 2021
Jeremie Coullon
Leah F. South
Christopher Nemeth
222
13
0
27 May 2021
DiSECt: A Differentiable Simulation Engine for Autonomous Robotic
  Cutting
DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting
Eric Heiden
Lukasz Wawrzyniak
Yashraj S. Narang
Dieter Fox
Animesh Garg
Fabio Ramos
210
107
0
25 May 2021
Contextual Dropout: An Efficient Sample-Dependent Dropout Module
Contextual Dropout: An Efficient Sample-Dependent Dropout ModuleInternational Conference on Learning Representations (ICLR), 2021
Xinjie Fan
Shujian Zhang
Korawat Tanwisuth
Xiaoning Qian
Mingyuan Zhou
OODBDLUQCV
160
31
0
06 Mar 2021
Generalization Bounds for Noisy Iterative Algorithms Using Properties of
  Additive Noise Channels
Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise ChannelsJournal of machine learning research (JMLR), 2021
Hao Wang
Rui Gao
Flavio du Pin Calmon
274
20
0
05 Feb 2021
Unadjusted Langevin algorithm with multiplicative noise: Total variation
  and Wasserstein bounds
Unadjusted Langevin algorithm with multiplicative noise: Total variation and Wasserstein boundsThe Annals of Applied Probability (Ann. Appl. Probab.), 2020
Gilles Pagès
Fabien Panloup
120
28
0
28 Dec 2020
EVA: Generating Longitudinal Electronic Health Records Using Conditional
  Variational Autoencoders
EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational AutoencodersMachine Learning in Health Care (MLHC), 2020
Siddharth Biswal
S. Ghosh
J. Duke
B. Malin
Walter F. Stewart
Jimeng Sun
SyDaBDLDRL
227
45
0
18 Dec 2020
Bayesian Neural Ordinary Differential Equations
Bayesian Neural Ordinary Differential Equations
Raj Dandekar
Karen Chung
Vaibhav Dixit
Mohamed Tarek
Aslan Garcia-Valadez
Krishna Vishal Vemula
Chris Rackauckas
UQCVOODBDL
325
60
0
14 Dec 2020
Efficient and Transferable Adversarial Examples from Bayesian Neural
  Networks
Efficient and Transferable Adversarial Examples from Bayesian Neural Networks
Martin Gubri
Maxime Cordy
Mike Papadakis
Yves Le Traon
Koushik Sen
AAML
319
12
0
10 Nov 2020
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint
  Sampling Method
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method
Ye He
Krishnakumar Balasubramanian
Murat A. Erdogdu
217
40
0
06 Nov 2020
A Contour Stochastic Gradient Langevin Dynamics Algorithm for
  Simulations of Multi-modal Distributions
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal DistributionsNeural Information Processing Systems (NeurIPS), 2020
Wei Deng
Guang Lin
F. Liang
BDL
292
33
0
19 Oct 2020
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