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Bridging the Gap between Stochastic Gradient MCMC and Stochastic
  Optimization

Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization

25 December 2015
Changyou Chen
David Carlson
Zhe Gan
Chunyuan Li
Lawrence Carin
ArXivPDFHTML

Papers citing "Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization"

20 / 20 papers shown
Title
Bayesian Computation in Deep Learning
Bayesian Computation in Deep Learning
Wenlong Chen
Bolian Li
Ruqi Zhang
Yingzhen Li
BDL
75
0
0
25 Feb 2025
Density Regression and Uncertainty Quantification with Bayesian Deep
  Noise Neural Networks
Density Regression and Uncertainty Quantification with Bayesian Deep Noise Neural Networks
Daiwei Zhang
Tianci Liu
Jian Kang
BDL
UQCV
43
2
0
12 Jun 2022
Approximate Inference via Clustering
Approximate Inference via Clustering
Qianqian Song
38
0
0
28 Nov 2021
Uncertainty quantification in non-rigid image registration via
  stochastic gradient Markov chain Monte Carlo
Uncertainty quantification in non-rigid image registration via stochastic gradient Markov chain Monte Carlo
Daniel Grzech
Mohammad Farid Azampour
Huaqi Qiu
Ben Glocker
Bernhard Kainz
Loic Le Folgoc
MedIm
19
2
0
25 Oct 2021
Is MC Dropout Bayesian?
Is MC Dropout Bayesian?
Loic Le Folgoc
V. Baltatzis
S. Desai
A. Devaraj
S. Ellis
O. M. Manzanera
A. Nair
Huaqi Qiu
Julia A. Schnabel
Ben Glocker
BDL
OOD
UQCV
30
39
0
08 Oct 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 Effect
Lorenzo Noci
Kevin Roth
Gregor Bachmann
Sebastian Nowozin
Thomas Hofmann
CML
30
23
0
11 Jun 2021
A Survey on Large-scale Machine Learning
A Survey on Large-scale Machine Learning
Meng Wang
Weijie Fu
Xiangnan He
Shijie Hao
Xindong Wu
25
110
0
10 Aug 2020
Powering One-shot Topological NAS with Stabilized Share-parameter Proxy
Powering One-shot Topological NAS with Stabilized Share-parameter Proxy
Ronghao Guo
Chen Lin
Chuming Li
Keyu Tian
Ming Sun
Lu Sheng
Junjie Yan
BDL
34
17
0
21 May 2020
Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Learning
Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Learning
Huy N. Chau
M. Rásonyi
43
10
0
25 Mar 2019
Meta-Learning for Stochastic Gradient MCMC
Meta-Learning for Stochastic Gradient MCMC
Wenbo Gong
Yingzhen Li
José Miguel Hernández-Lobato
BDL
38
44
0
12 Jun 2018
Bayesian Pose Graph Optimization via Bingham Distributions and Tempered
  Geodesic MCMC
Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC
Tolga Birdal
Umut Simsekli
M. Eken
Slobodan Ilic
29
38
0
31 May 2018
Deep Rewiring: Training very sparse deep networks
Deep Rewiring: Training very sparse deep networks
G. Bellec
David Kappel
Wolfgang Maass
Robert Legenstein
BDL
29
275
0
14 Nov 2017
Continuous-Time Flows for Efficient Inference and Density Estimation
Continuous-Time Flows for Efficient Inference and Density Estimation
Changyou Chen
Chunyuan Li
Liquan Chen
Wenlin Wang
Yunchen Pu
Lawrence Carin
TPM
51
57
0
04 Sep 2017
Mini-batch Tempered MCMC
Mini-batch Tempered MCMC
Dangna Li
W. Wong
29
5
0
31 Jul 2017
Fractional Langevin Monte Carlo: Exploring Lévy Driven Stochastic
  Differential Equations for Markov Chain Monte Carlo
Fractional Langevin Monte Carlo: Exploring Lévy Driven Stochastic Differential Equations for Markov Chain Monte Carlo
Umut Simsekli
38
45
0
12 Jun 2017
Langevin Dynamics with Continuous Tempering for Training Deep Neural
  Networks
Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks
Nanyang Ye
Zhanxing Zhu
Rafał K. Mantiuk
29
20
0
13 Mar 2017
Scalable Bayesian Learning of Recurrent Neural Networks for Language
  Modeling
Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling
Zhe Gan
Chunyuan Li
Changyou Chen
Yunchen Pu
Qinliang Su
Lawrence Carin
BDL
UQCV
58
41
0
23 Nov 2016
On the Convergence of Stochastic Gradient MCMC Algorithms with
  High-Order Integrators
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators
Changyou Chen
Nan Ding
Lawrence Carin
40
159
0
21 Oct 2016
Predictive Coarse-Graining
Predictive Coarse-Graining
M. Schöberl
N. Zabaras
P. Koutsourelakis
33
34
0
26 May 2016
Adding Gradient Noise Improves Learning for Very Deep Networks
Adding Gradient Noise Improves Learning for Very Deep Networks
Arvind Neelakantan
Luke Vilnis
Quoc V. Le
Ilya Sutskever
Lukasz Kaiser
Karol Kurach
James Martens
AI4CE
ODL
27
541
0
21 Nov 2015
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