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Auto-Encoding Variational Bayes

Auto-Encoding Variational Bayes

20 December 2013
Diederik P. Kingma
Max Welling
    BDL
ArXivPDFHTML

Papers citing "Auto-Encoding Variational Bayes"

50 / 3,457 papers shown
Title
A Theory of Generative ConvNet
A Theory of Generative ConvNet
Jianwen Xie
Yang Lu
Song-Chun Zhu
Ying Nian Wu
DiffM
GAN
50
318
0
10 Feb 2016
Pixel Recurrent Neural Networks
Pixel Recurrent Neural Networks
Aaron van den Oord
Nal Kalchbrenner
Koray Kavukcuoglu
SSeg
GAN
275
2,553
0
25 Jan 2016
A Taxonomy of Deep Convolutional Neural Nets for Computer Vision
A Taxonomy of Deep Convolutional Neural Nets for Computer Vision
Suraj Srinivas
Ravi Kiran Sarvadevabhatla
Konda Reddy Mopuri
N. Prabhu
S. Kruthiventi
R. Venkatesh Babu
OOD
35
215
0
25 Jan 2016
Variational Inference: A Review for Statisticians
Variational Inference: A Review for Statisticians
David M. Blei
A. Kucukelbir
Jon D. McAuliffe
BDL
77
4,713
0
04 Jan 2016
Autoencoding beyond pixels using a learned similarity metric
Autoencoding beyond pixels using a learned similarity metric
Anders Boesen Lindbo Larsen
Søren Kaae Sønderby
Hugo Larochelle
Ole Winther
GAN
100
2,054
0
31 Dec 2015
High-Order Stochastic Gradient Thermostats for Bayesian Learning of Deep
  Models
High-Order Stochastic Gradient Thermostats for Bayesian Learning of Deep Models
Chunyuan Li
Changyou Chen
Kai Fan
Lawrence Carin
BDL
39
25
0
23 Dec 2015
Memory-based control with recurrent neural networks
Memory-based control with recurrent neural networks
N. Heess
Jonathan J. Hunt
Timothy Lillicrap
David Silver
35
301
0
14 Dec 2015
Black box variational inference for state space models
Black box variational inference for state space models
Evan Archer
Il Memming Park
Lars Buesing
John P. Cunningham
Liam Paninski
BDL
37
160
0
23 Nov 2015
Recurrent Gaussian Processes
Recurrent Gaussian Processes
C. L. C. Mattos
Zhenwen Dai
Andreas C. Damianou
Jeremy Forth
G. Barreto
Neil D. Lawrence
BDL
32
75
0
20 Nov 2015
The Variational Gaussian Process
The Variational Gaussian Process
Dustin Tran
Rajesh Ranganath
David M. Blei
BDL
37
184
0
20 Nov 2015
Variational Auto-encoded Deep Gaussian Processes
Variational Auto-encoded Deep Gaussian Processes
Zhenwen Dai
Andreas C. Damianou
Javier I. González
Neil D. Lawrence
BDL
24
131
0
19 Nov 2015
Towards Principled Unsupervised Learning
Towards Principled Unsupervised Learning
Ilya Sutskever
Rafal Jozefowicz
Karol Gregor
Danilo Jimenez Rezende
Timothy Lillicrap
Oriol Vinyals
OOD
SSL
27
49
0
19 Nov 2015
Unsupervised Representation Learning with Deep Convolutional Generative
  Adversarial Networks
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Alec Radford
Luke Metz
Soumith Chintala
GAN
OOD
146
13,937
0
19 Nov 2015
Learning to Generate Images with Perceptual Similarity Metrics
Learning to Generate Images with Perceptual Similarity Metrics
Jake C. Snell
Karl Ridgeway
Renjie Liao
Brett D. Roads
Michael C. Mozer
R. Zemel
EGVM
27
177
0
19 Nov 2015
Generating Sentences from a Continuous Space
Generating Sentences from a Continuous Space
Samuel R. Bowman
Luke Vilnis
Oriol Vinyals
Andrew M. Dai
Rafal Jozefowicz
Samy Bengio
DRL
34
2,344
0
19 Nov 2015
Neural Variational Inference for Text Processing
Neural Variational Inference for Text Processing
Yishu Miao
Lei Yu
Phil Blunsom
VLM
DRL
28
616
0
19 Nov 2015
Super-Resolution with Deep Convolutional Sufficient Statistics
Super-Resolution with Deep Convolutional Sufficient Statistics
Joan Bruna
Pablo Sprechmann
Yann LeCun
SupR
28
323
0
18 Nov 2015
MuProp: Unbiased Backpropagation for Stochastic Neural Networks
MuProp: Unbiased Backpropagation for Stochastic Neural Networks
S. Gu
Sergey Levine
Ilya Sutskever
A. Mnih
BDL
16
143
0
16 Nov 2015
Deep Kalman Filters
Deep Kalman Filters
Rahul G. Krishnan
Uri Shalit
David Sontag
BDL
AI4TS
36
370
0
16 Nov 2015
How (not) to Train your Generative Model: Scheduled Sampling,
  Likelihood, Adversary?
How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary?
Ferenc Huszár
OOD
DiffM
GAN
28
296
0
16 Nov 2015
Black-box $α$-divergence Minimization
Black-box ααα-divergence Minimization
José Miguel Hernández-Lobato
Yingzhen Li
Mark Rowland
Daniel Hernández-Lobato
T. Bui
Richard Turner
23
137
0
10 Nov 2015
Generating Images from Captions with Attention
Generating Images from Captions with Attention
Elman Mansimov
Emilio Parisotto
Jimmy Lei Ba
Ruslan Salakhutdinov
VLM
55
450
0
09 Nov 2015
Hierarchical Variational Models
Hierarchical Variational Models
Rajesh Ranganath
Dustin Tran
David M. Blei
DRL
VLM
28
335
0
07 Nov 2015
The Variational Fair Autoencoder
The Variational Fair Autoencoder
Christos Louizos
Kevin Swersky
Yujia Li
Max Welling
R. Zemel
DRL
51
632
0
03 Nov 2015
Faster Stochastic Variational Inference using Proximal-Gradient Methods
  with General Divergence Functions
Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions
Mohammad Emtiyaz Khan
Reza Babanezhad
Wu Lin
Mark Schmidt
Masashi Sugiyama
33
49
0
31 Oct 2015
Learning Continuous Control Policies by Stochastic Value Gradients
Learning Continuous Control Policies by Stochastic Value Gradients
N. Heess
Greg Wayne
David Silver
Timothy Lillicrap
Yuval Tassa
Tom Erez
45
558
0
30 Oct 2015
Learning FRAME Models Using CNN Filters
Learning FRAME Models Using CNN Filters
Yang Lu
Song-Chun Zhu
Ying Nian Wu
GAN
28
66
0
28 Sep 2015
Deep Temporal Sigmoid Belief Networks for Sequence Modeling
Deep Temporal Sigmoid Belief Networks for Sequence Modeling
Zhe Gan
Chunyuan Li
Ricardo Henao
David Carlson
Lawrence Carin
BDL
32
85
0
23 Sep 2015
Learning Wake-Sleep Recurrent Attention Models
Learning Wake-Sleep Recurrent Attention Models
Jimmy Ba
Roger C. Grosse
Ruslan Salakhutdinov
B. Frey
BDL
32
65
0
22 Sep 2015
Stochastic gradient variational Bayes for gamma approximating
  distributions
Stochastic gradient variational Bayes for gamma approximating distributions
David A. Knowles
BDL
27
50
0
04 Sep 2015
Importance Weighted Autoencoders
Importance Weighted Autoencoders
Yuri Burda
Roger C. Grosse
Ruslan Salakhutdinov
BDL
83
1,236
0
01 Sep 2015
Gradient Estimation Using Stochastic Computation Graphs
Gradient Estimation Using Stochastic Computation Graphs
John Schulman
N. Heess
T. Weber
Pieter Abbeel
OffRL
41
390
0
17 Jun 2015
Bayesian representation learning with oracle constraints
Bayesian representation learning with oracle constraints
Theofanis Karaletsos
Serge J. Belongie
Gunnar Rätsch
BDL
35
92
0
16 Jun 2015
Data Generation as Sequential Decision Making
Data Generation as Sequential Decision Making
Philip Bachman
Doina Precup
30
57
0
10 Jun 2015
Automatic Variational Inference in Stan
Automatic Variational Inference in Stan
A. Kucukelbir
Rajesh Ranganath
Andrew Gelman
David M. Blei
BDL
37
232
0
10 Jun 2015
Neural Adaptive Sequential Monte Carlo
Neural Adaptive Sequential Monte Carlo
S. Gu
Zoubin Ghahramani
Richard Turner
BDL
24
145
0
10 Jun 2015
Variational Dropout and the Local Reparameterization Trick
Variational Dropout and the Local Reparameterization Trick
Diederik P. Kingma
Tim Salimans
Max Welling
BDL
69
1,494
0
08 Jun 2015
A Recurrent Latent Variable Model for Sequential Data
A Recurrent Latent Variable Model for Sequential Data
Junyoung Chung
Kyle Kastner
Laurent Dinh
Kratarth Goel
Aaron Courville
Yoshua Bengio
DRL
BDL
42
1,242
0
07 Jun 2015
Bayesian Convolutional Neural Networks with Bernoulli Approximate
  Variational Inference
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
Y. Gal
Zoubin Ghahramani
UQCV
BDL
213
745
0
06 Jun 2015
Automatic Relevance Determination For Deep Generative Models
Automatic Relevance Determination For Deep Generative Models
Theofanis Karaletsos
Gunnar Rätsch
33
8
0
28 May 2015
Variational Inference with Normalizing Flows
Variational Inference with Normalizing Flows
Danilo Jimenez Rezende
S. Mohamed
DRL
BDL
95
4,114
0
21 May 2015
Weight Uncertainty in Neural Networks
Weight Uncertainty in Neural Networks
Charles Blundell
Julien Cornebise
Koray Kavukcuoglu
Daan Wierstra
UQCV
BDL
38
1,872
0
20 May 2015
Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Jascha Narain Sohl-Dickstein
Eric A. Weiss
Niru Maheswaranathan
Surya Ganguli
SyDa
DiffM
106
6,650
0
12 Mar 2015
Deep Convolutional Inverse Graphics Network
Deep Convolutional Inverse Graphics Network
Tejas D. Kulkarni
William F. Whitney
Pushmeet Kohli
J. Tenenbaum
DRL
BDL
50
929
0
11 Mar 2015
Latent Gaussian Processes for Distribution Estimation of Multivariate
  Categorical Data
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data
Y. Gal
Yutian Chen
Zoubin Ghahramani
SyDa
37
41
0
07 Mar 2015
Automatic differentiation in machine learning: a survey
Automatic differentiation in machine learning: a survey
A. G. Baydin
Barak A. Pearlmutter
Alexey Radul
J. Siskind
PINN
AI4CE
ODL
75
2,754
0
20 Feb 2015
DRAW: A Recurrent Neural Network For Image Generation
DRAW: A Recurrent Neural Network For Image Generation
Karol Gregor
Ivo Danihelka
Alex Graves
Danilo Jimenez Rezende
Daan Wierstra
GAN
DRL
79
1,955
0
16 Feb 2015
Show, Attend and Tell: Neural Image Caption Generation with Visual
  Attention
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Ke Xu
Jimmy Ba
Ryan Kiros
Kyunghyun Cho
Aaron Courville
Ruslan Salakhutdinov
R. Zemel
Yoshua Bengio
DiffM
142
10,011
0
10 Feb 2015
Generative Class-conditional Autoencoders
Generative Class-conditional Autoencoders
Jan Rudy
Graham W. Taylor
DRL
31
7
0
22 Dec 2014
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
74
149,056
0
22 Dec 2014
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