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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
    ODL
    BDL
ArXivPDFHTML

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

13 / 13 papers shown
Title
JaxSGMC: Modular stochastic gradient MCMC in JAX
JaxSGMC: Modular stochastic gradient MCMC in JAX
Stephan Thaler
Paul Fuchs
Ana Cukarska
Julija Zavadlav
BDL
121
2
0
16 May 2025
Stochastic Process Learning via Operator Flow Matching
Stochastic Process Learning via Operator Flow Matching
Yaozhong Shi
Zachary E. Ross
D. Asimaki
Kamyar Azizzadenesheli
96
2
0
10 Jan 2025
Particle Semi-Implicit Variational Inference
Particle Semi-Implicit Variational Inference
Jen Ning Lim
A. M. Johansen
69
5
0
30 Jun 2024
Reparameterization invariance in approximate Bayesian inference
Reparameterization invariance in approximate Bayesian inference
Hrittik Roy
M. Miani
Carl Henrik Ek
Philipp Hennig
Marvin Pfortner
Lukas Tatzel
Søren Hauberg
BDL
70
8
0
05 Jun 2024
Scalable Bayesian Learning with posteriors
Scalable Bayesian Learning with posteriors
Samuel Duffield
Kaelan Donatella
Johnathan Chiu
Phoebe Klett
Daniel Simpson
BDL
UQCV
90
1
0
31 May 2024
Preconditioned Score-based Generative Models
Preconditioned Score-based Generative Models
He Ma
Xiatian Zhu
Xiatian Zhu
Jianfeng Feng
DiffM
65
6
0
13 Feb 2023
An Adaptive Empirical Bayesian Method for Sparse Deep Learning
An Adaptive Empirical Bayesian Method for Sparse Deep Learning
Wei Deng
Xiao Zhang
F. Liang
Guang Lin
BDL
91
44
0
23 Oct 2019
Distributed Bayesian Learning with Stochastic Natural-gradient
  Expectation Propagation and the Posterior Server
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server
Leonard Hasenclever
Stefan Webb
Thibaut Lienart
Sebastian J. Vollmer
Balaji Lakshminarayanan
Charles Blundell
Yee Whye Teh
BDL
79
70
0
31 Dec 2015
Variational Dropout and the Local Reparameterization Trick
Variational Dropout and the Local Reparameterization Trick
Diederik P. Kingma
Tim Salimans
Max Welling
BDL
151
1,500
0
08 Jun 2015
Deeply-Supervised Nets
Deeply-Supervised Nets
Chen-Yu Lee
Saining Xie
Patrick W. Gallagher
Zhengyou Zhang
Zhuowen Tu
250
2,229
0
18 Sep 2014
Stochastic Pooling for Regularization of Deep Convolutional Neural
  Networks
Stochastic Pooling for Regularization of Deep Convolutional Neural Networks
Matthew D. Zeiler
Rob Fergus
113
990
0
16 Jan 2013
ADADELTA: An Adaptive Learning Rate Method
ADADELTA: An Adaptive Learning Rate Method
Matthew D. Zeiler
ODL
108
6,619
0
22 Dec 2012
Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring
Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring
S. Ahn
Anoop Korattikara Balan
Max Welling
59
305
0
27 Jun 2012
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