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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 / 3,050 papers shown
Title
Pixel Recurrent Neural Networks
Pixel Recurrent Neural Networks
Aaron van den Oord
Nal Kalchbrenner
Koray Kavukcuoglu
SSeg
GAN
269
2,552
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
44
4,710
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
67
2,052
0
31 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
18
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
23
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
26
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
21
49
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
24
176
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
2,341
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
25
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
8
143
0
16 Nov 2015
Deep Kalman Filters
Deep Kalman Filters
Rahul G. Krishnan
Uri Shalit
David Sontag
BDL
AI4TS
19
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
43
449
0
09 Nov 2015
Hierarchical Variational Models
Hierarchical Variational Models
Rajesh Ranganath
Dustin Tran
David M. Blei
DRL
VLM
23
335
0
07 Nov 2015
The Variational Fair Autoencoder
The Variational Fair Autoencoder
Christos Louizos
Kevin Swersky
Yujia Li
Max Welling
R. Zemel
DRL
19
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 W. Schmidt
Masashi Sugiyama
28
49
0
31 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
16
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
16
50
0
04 Sep 2015
Importance Weighted Autoencoders
Importance Weighted Autoencoders
Yuri Burda
Roger C. Grosse
Ruslan Salakhutdinov
BDL
13
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
21
389
0
17 Jun 2015
Bayesian representation learning with oracle constraints
Bayesian representation learning with oracle constraints
Theofanis Karaletsos
Serge J. Belongie
Gunnar Rätsch
BDL
29
92
0
16 Jun 2015
Data Generation as Sequential Decision Making
Data Generation as Sequential Decision Making
Philip Bachman
Doina Precup
22
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
29
232
0
10 Jun 2015
Neural Adaptive Sequential Monte Carlo
Neural Adaptive Sequential Monte Carlo
S. Gu
Zoubin Ghahramani
Richard Turner
BDL
16
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
61
1,493
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
27
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
202
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
38
4,105
0
21 May 2015
Weight Uncertainty in Neural Networks
Weight Uncertainty in Neural Networks
Charles Blundell
Julien Cornebise
Koray Kavukcuoglu
Daan Wierstra
UQCV
BDL
25
1,871
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
79
6,608
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
30
929
0
11 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
63
2,750
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
50
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
118
10,006
0
10 Feb 2015
Generative Class-conditional Autoencoders
Generative Class-conditional Autoencoders
Jan Rudy
Graham W. Taylor
DRL
25
7
0
22 Dec 2014
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
27
148,878
0
22 Dec 2014
Learning Stochastic Recurrent Networks
Learning Stochastic Recurrent Networks
Justin Bayer
Christian Osendorfer
BDL
34
273
0
27 Nov 2014
Variational Inference for Uncertainty on the Inputs of Gaussian Process
  Models
Variational Inference for Uncertainty on the Inputs of Gaussian Process Models
Andreas C. Damianou
Michalis K. Titsias
Neil D. Lawrence
41
25
0
08 Sep 2014
How Auto-Encoders Could Provide Credit Assignment in Deep Networks via
  Target Propagation
How Auto-Encoders Could Provide Credit Assignment in Deep Networks via Target Propagation
Yoshua Bengio
42
181
0
29 Jul 2014
Techniques for Learning Binary Stochastic Feedforward Neural Networks
Techniques for Learning Binary Stochastic Feedforward Neural Networks
T. Raiko
Mathias Berglund
Guillaume Alain
Laurent Dinh
BDL
65
126
0
11 Jun 2014
Structured Stochastic Variational Inference
Structured Stochastic Variational Inference
Matthew D. Hoffman
David M. Blei
BDL
34
87
0
16 Apr 2014
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