ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1601.00670
  4. Cited By
Variational Inference: A Review for Statisticians
v1v2v3v4v5v6v7v8v9 (latest)

Variational Inference: A Review for Statisticians

4 January 2016
David M. Blei
A. Kucukelbir
Jon D. McAuliffe
    BDL
ArXiv (abs)PDFHTML

Papers citing "Variational Inference: A Review for Statisticians"

50 / 1,838 papers shown
Title
Active Learning Solution on Distributed Edge Computing
Active Learning Solution on Distributed Edge Computing
Jia Qian
Sayantani Sengupta
Lars Kai Hansen
56
20
0
25 Jun 2019
Monte Carlo Gradient Estimation in Machine Learning
Monte Carlo Gradient Estimation in Machine Learning
S. Mohamed
Mihaela Rosca
Michael Figurnov
A. Mnih
101
416
0
25 Jun 2019
Divide and Couple: Using Monte Carlo Variational Objectives for
  Posterior Approximation
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation
Justin Domke
Daniel Sheldon
182
18
0
24 Jun 2019
Black-Box Inference for Non-Linear Latent Force Models
Black-Box Inference for Non-Linear Latent Force Models
W. Ward
Tom Ryder
D. Prangle
Mauricio A. Alvarez
DRL
80
14
0
21 Jun 2019
Derivation of the Variational Bayes Equations
Derivation of the Variational Bayes Equations
A. Maren
DRL
27
4
0
20 Jun 2019
Provable Gradient Variance Guarantees for Black-Box Variational
  Inference
Provable Gradient Variance Guarantees for Black-Box Variational Inference
Justin Domke
DRL
59
23
0
19 Jun 2019
Variational Inference with Numerical Derivatives: variance reduction
  through coupling
Variational Inference with Numerical Derivatives: variance reduction through coupling
Alexander Immer
Guillaume P. Dehaene
BDLDRL
20
0
0
17 Jun 2019
A Survey of Optimization Methods from a Machine Learning Perspective
A Survey of Optimization Methods from a Machine Learning Perspective
Shiliang Sun
Zehui Cao
Han Zhu
Jing Zhao
88
566
0
17 Jun 2019
Reweighted Expectation Maximization
Reweighted Expectation Maximization
Adji Bousso Dieng
John Paisley
VLMDRL
70
17
0
13 Jun 2019
Overcoming Mean-Field Approximations in Recurrent Gaussian Process
  Models
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models
Alessandro Davide Ialongo
Mark van der Wilk
J. Hensman
C. Rasmussen
79
30
0
13 Jun 2019
Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical
  Bayes
Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes
R. Krishnan
Mahesh Subedar
Omesh Tickoo
BDL
80
47
0
12 Jun 2019
A Bayesian Approach to In-Game Win Probability in Soccer
A Bayesian Approach to In-Game Win Probability in Soccer
Pieter Robberechts
Jan Van Haaren
Jesse Davis
SyDa
33
19
0
12 Jun 2019
Non-Parametric Calibration for Classification
Non-Parametric Calibration for Classification
Jonathan Wenger
Hedvig Kjellström
Rudolph Triebel
UQCV
120
82
0
12 Jun 2019
Learning Deep Generative Models with Annealed Importance Sampling
Learning Deep Generative Models with Annealed Importance Sampling
Xinqiang Ding
David J. Freedman
VLMBDLGAN
96
11
0
12 Jun 2019
Towards Amortized Ranking-Critical Training for Collaborative Filtering
Towards Amortized Ranking-Critical Training for Collaborative Filtering
Sam Lobel
Chunyuan Li
Jianfeng Gao
Lawrence Carin
56
15
0
10 Jun 2019
Enabling Robust State Estimation through Measurement Error Covariance
  Adaptation
Enabling Robust State Estimation through Measurement Error Covariance Adaptation
Ryan M. Watson
Jason N. Gross
Clark N. Taylor
R. Leishman
21
20
0
10 Jun 2019
Topic-Aware Neural Keyphrase Generation for Social Media Language
Topic-Aware Neural Keyphrase Generation for Social Media Language
Yue Wang
Jing Li
Hou Pong Chan
Irwin King
Michael R. Lyu
Shuming Shi
BDL
71
82
0
10 Jun 2019
Physics-Informed Probabilistic Learning of Linear Embeddings of
  Non-linear Dynamics With Guaranteed Stability
Physics-Informed Probabilistic Learning of Linear Embeddings of Non-linear Dynamics With Guaranteed Stability
Shaowu Pan
Karthik Duraisamy
140
138
0
09 Jun 2019
DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep
  Networks
DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networks
Aryan Mobiny
H. Nguyen
S. Moulik
Naveen Garg
Carol C. Wu
UQCVBDL
76
163
0
07 Jun 2019
Learning Representations of Graph Data -- A Survey
Learning Representations of Graph Data -- A Survey
Mital Kinderkhedia
GNN
79
12
0
07 Jun 2019
Semi-supervised Stochastic Multi-Domain Learning using Variational
  Inference
Semi-supervised Stochastic Multi-Domain Learning using Variational Inference
Yitong Li
Timothy Baldwin
Trevor Cohn
BDL
38
9
0
07 Jun 2019
Deep Compositional Spatial Models
Deep Compositional Spatial Models
A. Zammit‐Mangion
T. L. J. Ng
Quan Vu
Maurizio Filippone
126
57
0
06 Jun 2019
Counterfactual Inference for Consumer Choice Across Many Product
  Categories
Counterfactual Inference for Consumer Choice Across Many Product Categories
Rob Donnelly
Francisco J. R. Ruiz
David M. Blei
Susan Athey
CML
107
33
0
06 Jun 2019
Uncertainty-guided Continual Learning with Bayesian Neural Networks
Uncertainty-guided Continual Learning with Bayesian Neural Networks
Sayna Ebrahimi
Mohamed Elhoseiny
Trevor Darrell
Marcus Rohrbach
CLLBDL
68
197
0
06 Jun 2019
Streaming Variational Monte Carlo
Streaming Variational Monte Carlo
Yuan Zhao
Josue Nassar
I. Jordan
M. Bugallo
Il Memming Park
BDL
287
21
0
04 Jun 2019
A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence
  Matching
A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching
Jihun Choi
Taeuk Kim
Sang-goo Lee
BDL
77
6
0
04 Jun 2019
Coupled VAE: Improved Accuracy and Robustness of a Variational
  Autoencoder
Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder
Shichen Cao
Jingjing Li
Kenric P. Nelson
Mark A. Kon
BDLDRL
59
16
0
03 Jun 2019
Variational Langevin Hamiltonian Monte Carlo for Distant Multi-modal
  Sampling
Variational Langevin Hamiltonian Monte Carlo for Distant Multi-modal Sampling
Minghao Gu
Shiliang Sun
BDL
40
0
0
01 Jun 2019
Bayesian Deconditional Kernel Mean Embeddings
Bayesian Deconditional Kernel Mean Embeddings
Kelvin Hsu
F. Ramos
CMLBDL
54
9
0
01 Jun 2019
Dirichlet Simplex Nest and Geometric Inference
Dirichlet Simplex Nest and Geometric Inference
Mikhail Yurochkin
Aritra Guha
Yuekai Sun
X. Nguyen
80
4
0
27 May 2019
Variational Bayes under Model Misspecification
Variational Bayes under Model Misspecification
Yixin Wang
David M. Blei
67
45
0
26 May 2019
A Flexible Generative Framework for Graph-based Semi-supervised Learning
A Flexible Generative Framework for Graph-based Semi-supervised Learning
Jiaqi Ma
Weijing Tang
Ji Zhu
Qiaozhu Mei
BDL
62
63
0
26 May 2019
An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric
  Retrieval
An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
Adam D. Cobb
Michael D. Himes
Frank Soboczenski
Simone Zorzan
Molly D. O'Beirne
A. G. Baydin
Y. Gal
S. Domagal‐Goldman
Giada N. Arney
Daniel Angerhausen
61
57
0
25 May 2019
Neural Stochastic Differential Equations: Deep Latent Gaussian Models in
  the Diffusion Limit
Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit
Belinda Tzen
Maxim Raginsky
DiffM
197
211
0
23 May 2019
Gaussbock: Fast parallel-iterative cosmological parameter estimation
  with Bayesian nonparametrics
Gaussbock: Fast parallel-iterative cosmological parameter estimation with Bayesian nonparametrics
Ben Moews
J. Zuntz
45
2
0
23 May 2019
Multi-Class Gaussian Process Classification Made Conjugate: Efficient
  Inference via Data Augmentation
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
Théo Galy-Fajou
F. Wenzel
Christian Donner
Manfred Opper
62
30
0
23 May 2019
Unsupervised Linear and Nonlinear Channel Equalization and Decoding
  using Variational Autoencoders
Unsupervised Linear and Nonlinear Channel Equalization and Decoding using Variational Autoencoders
Avi Caciularu
D. Burshtein
71
50
0
21 May 2019
Leveraging Bayesian Analysis To Improve Accuracy of Approximate Models
Leveraging Bayesian Analysis To Improve Accuracy of Approximate Models
Balasubramanya T. Nadiga
C. Jiang
Daniel Livescu
25
12
0
20 May 2019
Recommendation from Raw Data with Adaptive Compound Poisson
  Factorization
Recommendation from Raw Data with Adaptive Compound Poisson Factorization
Olivier Gouvert
Thomas Oberlin
Cédric Févotte
46
12
0
20 May 2019
Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling
Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling
Hao Zhang
Bo Chen
Long Tian
Zhengjue Wang
Mingyuan Zhou
DRL
67
7
0
18 May 2019
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data
  Approximations
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations
Brian L. Trippe
Jonathan H. Huggins
Raj Agrawal
Tamara Broderick
BDL
75
9
0
17 May 2019
On Variational Bounds of Mutual Information
On Variational Bounds of Mutual Information
Ben Poole
Sherjil Ozair
Aaron van den Oord
Alexander A. Alemi
George Tucker
SSL
135
816
0
16 May 2019
When random initializations help: a study of variational inference for
  community detection
When random initializations help: a study of variational inference for community detection
Purnamrita Sarkar
Y. X. R. Wang
Soumendu Sundar Mukherjee
BDL
75
6
0
16 May 2019
Neutron Transmission Strain Tomography for Non-Constant Stress-Free
  Lattice Spacing
Neutron Transmission Strain Tomography for Non-Constant Stress-Free Lattice Spacing
J. Hendriks
Carl Jidling
Thomas B. Schon
A. Wills
C. Wensrich
E. Kisi
33
6
0
15 May 2019
Moment-Based Variational Inference for Markov Jump Processes
Moment-Based Variational Inference for Markov Jump Processes
C. Wildner
Heinz Koeppl
65
10
0
14 May 2019
Variational approximations using Fisher divergence
Variational approximations using Fisher divergence
Yue Yang
Ryan Martin
H. Bondell
42
15
0
13 May 2019
Random Function Priors for Correlation Modeling
Random Function Priors for Correlation Modeling
Aonan Zhang
John Paisley
50
2
0
09 May 2019
Monte Carlo Co-Ordinate Ascent Variational Inference
Monte Carlo Co-Ordinate Ascent Variational Inference
Lifeng Ye
A. Beskos
Maria de Iorio
Jie Hao
DRLBDL
18
1
0
09 May 2019
Interpretable Subgroup Discovery in Treatment Effect Estimation with
  Application to Opioid Prescribing Guidelines
Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines
Chirag Nagpal
Dennis L. Wei
B. Vinzamuri
Monica Shekhar
Sara E. Berger
Subhro Das
Kush R. Varshney
CML
69
25
0
08 May 2019
Bayesian leave-one-out cross-validation for large data
Bayesian leave-one-out cross-validation for large data
Måns Magnusson
Michael Riis Andersen
J. Jonasson
Aki Vehtari
111
26
0
24 Apr 2019
Previous
123...303132...353637
Next