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Variational Inference with Normalizing Flows
v1v2v3v4v5v6 (latest)

Variational Inference with Normalizing Flows

21 May 2015
Danilo Jimenez Rezende
S. Mohamed
    DRLBDL
ArXiv (abs)PDFHTML

Papers citing "Variational Inference with Normalizing Flows"

50 / 2,268 papers shown
Title
Convergence of Langevin MCMC in KL-divergence
Convergence of Langevin MCMC in KL-divergence
Xiang Cheng
Peter L. Bartlett
97
194
0
25 May 2017
Proximity Variational Inference
Proximity Variational Inference
Jaan Altosaar
Rajesh Ranganath
David M. Blei
BDL
43
22
0
24 May 2017
Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in
  Generative Models
Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models
Aditya Grover
Manik Dhar
Stefano Ermon
GAN
88
24
0
24 May 2017
Causal Effect Inference with Deep Latent-Variable Models
Causal Effect Inference with Deep Latent-Variable Models
Christos Louizos
Uri Shalit
Joris Mooij
David Sontag
R. Zemel
Max Welling
CMLBDL
242
750
0
24 May 2017
Reducing Reparameterization Gradient Variance
Reducing Reparameterization Gradient Variance
Andrew C. Miller
N. Foti
Alexander DÁmour
Ryan P. Adams
70
84
0
22 May 2017
Gradient Estimators for Implicit Models
Gradient Estimators for Implicit Models
Yingzhen Li
Richard Turner
157
108
0
19 May 2017
Masked Autoregressive Flow for Density Estimation
Masked Autoregressive Flow for Density Estimation
George Papamakarios
Theo Pavlakou
Iain Murray
238
1,361
0
19 May 2017
VAE with a VampPrior
VAE with a VampPrior
Jakub M. Tomczak
Max Welling
GANBDL
139
638
0
19 May 2017
Learning Multimodal Transition Dynamics for Model-Based Reinforcement
  Learning
Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning
Thomas M. Moerland
Joost Broekens
Catholijn M. Jonker
OffRL
96
31
0
01 May 2017
Importance Sampled Stochastic Optimization for Variational Inference
Importance Sampled Stochastic Optimization for Variational Inference
J. Sakaya
Arto Klami
BDL
48
7
0
19 Apr 2017
Stein Variational Adaptive Importance Sampling
Stein Variational Adaptive Importance Sampling
J. Han
Qiang Liu
150
28
0
18 Apr 2017
VAE Learning via Stein Variational Gradient Descent
VAE Learning via Stein Variational Gradient Descent
Yunchen Pu
Zhe Gan
Ricardo Henao
Chunyuan Li
Shaobo Han
Lawrence Carin
DRL
74
6
0
18 Apr 2017
$k$-means as a variational EM approximation of Gaussian mixture models
kkk-means as a variational EM approximation of Gaussian mixture models
Jörg Lücke
D. Forster
DRLVLM
64
49
0
16 Apr 2017
Reinterpreting Importance-Weighted Autoencoders
Reinterpreting Importance-Weighted Autoencoders
Chris Cremer
Q. Morris
David Duvenaud
BDLFAtt
135
94
0
10 Apr 2017
Learning Approximately Objective Priors
Learning Approximately Objective Priors
Eric T. Nalisnick
Padhraic Smyth
40
11
0
04 Apr 2017
Semi-Supervised Generation with Cluster-aware Generative Models
Semi-Supervised Generation with Cluster-aware Generative Models
Lars Maaløe
Marco Fraccaro
Ole Winther
117
28
0
03 Apr 2017
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for
  Variational Inference
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference
Geoffrey Roeder
Yuhuai Wu
David Duvenaud
BDL
167
202
0
27 Mar 2017
Inference via low-dimensional couplings
Inference via low-dimensional couplings
Alessio Spantini
Daniele Bigoni
Youssef Marzouk
145
119
0
17 Mar 2017
Sharp Minima Can Generalize For Deep Nets
Sharp Minima Can Generalize For Deep Nets
Laurent Dinh
Razvan Pascanu
Samy Bengio
Yoshua Bengio
ODL
147
774
0
15 Mar 2017
Multiplicative Normalizing Flows for Variational Bayesian Neural
  Networks
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks
Christos Louizos
Max Welling
BDL
181
461
0
06 Mar 2017
Hierarchical Implicit Models and Likelihood-Free Variational Inference
Hierarchical Implicit Models and Likelihood-Free Variational Inference
Dustin Tran
Rajesh Ranganath
David M. Blei
VLMGAN
115
99
0
28 Feb 2017
Learning Hierarchical Features from Generative Models
Learning Hierarchical Features from Generative Models
Shengjia Zhao
Jiaming Song
Stefano Ermon
BDLGANOODDRL
89
74
0
27 Feb 2017
Approximate Inference with Amortised MCMC
Approximate Inference with Amortised MCMC
Yingzhen Li
Richard Turner
Qiang Liu
BDL
96
62
0
27 Feb 2017
Variational Inference using Implicit Distributions
Variational Inference using Implicit Distributions
Ferenc Huszár
DRLGAN
175
135
0
27 Feb 2017
Improved Variational Autoencoders for Text Modeling using Dilated
  Convolutions
Improved Variational Autoencoders for Text Modeling using Dilated Convolutions
Zichao Yang
Zhiting Hu
Ruslan Salakhutdinov
Taylor Berg-Kirkpatrick
122
389
0
27 Feb 2017
Generative Temporal Models with Memory
Generative Temporal Models with Memory
Mevlana Gemici
Chia-Chun Hung
Adam Santoro
Greg Wayne
S. Mohamed
Danilo Jimenez Rezende
David Amos
Timothy Lillicrap
72
57
0
15 Feb 2017
A Hybrid Convolutional Variational Autoencoder for Text Generation
A Hybrid Convolutional Variational Autoencoder for Text Generation
Stanislau Semeniuta
Aliaksei Severyn
Erhardt Barth
103
253
0
08 Feb 2017
Adversarial Variational Bayes: Unifying Variational Autoencoders and
  Generative Adversarial Networks
Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
L. Mescheder
Sebastian Nowozin
Andreas Geiger
GANBDL
175
530
0
17 Jan 2017
Deep Probabilistic Programming
Deep Probabilistic Programming
Dustin Tran
Matthew D. Hoffman
Rif A. Saurous
E. Brevdo
Kevin Patrick Murphy
David M. Blei
BDL
144
193
0
13 Jan 2017
Maximum Entropy Flow Networks
Maximum Entropy Flow Networks
Gabriel Loaiza-Ganem
Yuanjun Gao
John P. Cunningham
92
27
0
12 Jan 2017
NIPS 2016 Tutorial: Generative Adversarial Networks
NIPS 2016 Tutorial: Generative Adversarial Networks
Ian Goodfellow
GAN
190
1,729
0
31 Dec 2016
Two Methods For Wild Variational Inference
Two Methods For Wild Variational Inference
Qiang Liu
Yihao Feng
BDL
138
19
0
30 Nov 2016
Improving Variational Auto-Encoders using Householder Flow
Improving Variational Auto-Encoders using Householder Flow
Jakub M. Tomczak
Max Welling
BDLDRL
126
175
0
29 Nov 2016
Variational Boosting: Iteratively Refining Posterior Approximations
Variational Boosting: Iteratively Refining Posterior Approximations
Andrew C. Miller
N. Foti
Ryan P. Adams
92
124
0
20 Nov 2016
Deep Variational Inference Without Pixel-Wise Reconstruction
Deep Variational Inference Without Pixel-Wise Reconstruction
Siddharth Agrawal
Ambedkar Dukkipati
DRL3DVBDL
80
13
0
16 Nov 2016
PixelVAE: A Latent Variable Model for Natural Images
PixelVAE: A Latent Variable Model for Natural Images
Ishaan Gulrajani
Kundan Kumar
Faruk Ahmed
Adrien Ali Taïga
Francesco Visin
David Vazquez
Aaron Courville
DRLSSLBDL
95
340
0
15 Nov 2016
Variational Lossy Autoencoder
Variational Lossy Autoencoder
Xi Chen
Diederik P. Kingma
Tim Salimans
Yan Duan
Prafulla Dhariwal
John Schulman
Ilya Sutskever
Pieter Abbeel
DRLSSLGAN
217
676
0
08 Nov 2016
Learning to Draw Samples: With Application to Amortized MLE for
  Generative Adversarial Learning
Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning
Dilin Wang
Qiang Liu
GANBDL
151
119
0
06 Nov 2016
Conditional Image Synthesis With Auxiliary Classifier GANs
Conditional Image Synthesis With Auxiliary Classifier GANs
Augustus Odena
C. Olah
Jonathon Shlens
GAN
503
3,227
0
30 Oct 2016
Operator Variational Inference
Operator Variational Inference
Rajesh Ranganath
Jaan Altosaar
Dustin Tran
David M. Blei
106
116
0
27 Oct 2016
Deep Variational Canonical Correlation Analysis
Deep Variational Canonical Correlation Analysis
Weiran Wang
Xinchen Yan
Honglak Lee
Karen Livescu
DRLBDL
86
146
0
11 Oct 2016
Structured Inference Networks for Nonlinear State Space Models
Structured Inference Networks for Nonlinear State Space Models
Rahul G. Krishnan
Uri Shalit
David Sontag
BDL
121
460
0
30 Sep 2016
Variational Inference with Hamiltonian Monte Carlo
Variational Inference with Hamiltonian Monte Carlo
Christopher Wolf
Maximilian Karl
Patrick van der Smagt
BDL
66
36
0
26 Sep 2016
Discrete Variational Autoencoders
Discrete Variational Autoencoders
J. Rolfe
BDLDRL
219
260
0
07 Sep 2016
Stein Variational Gradient Descent: A General Purpose Bayesian Inference
  Algorithm
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
Qiang Liu
Dilin Wang
BDL
136
1,095
0
16 Aug 2016
Improving Variational Inference with Inverse Autoregressive Flow
Improving Variational Inference with Inverse Autoregressive Flow
Diederik P. Kingma
Tim Salimans
Rafal Jozefowicz
Xi Chen
Ilya Sutskever
Max Welling
BDLDRL
162
1,828
0
15 Jun 2016
Faster Eigenvector Computation via Shift-and-Invert Preconditioning
Faster Eigenvector Computation via Shift-and-Invert Preconditioning
Dan Garber
Laurent Dinh
Chi Jin
Jascha Narain Sohl-Dickstein
Samy Bengio
Praneeth Netrapalli
Aaron Sidford
279
3,730
0
26 May 2016
Deep Variational Bayes Filters: Unsupervised Learning of State Space
  Models from Raw Data
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data
Maximilian Karl
Maximilian Sölch
Justin Bayer
Patrick van der Smagt
BDL
65
375
0
20 May 2016
Stick-Breaking Variational Autoencoders
Stick-Breaking Variational Autoencoders
Marco Cote
Padhraic Smyth
BDLDRL
132
163
0
20 May 2016
Gaussian variational approximation with sparse precision matrices
Gaussian variational approximation with sparse precision matrices
Linda S. L. Tan
David J. Nott
140
76
0
18 May 2016
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