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Auxiliary Deep Generative Models
v1v2v3v4 (latest)

Auxiliary Deep Generative Models

17 February 2016
Lars Maaløe
C. Sønderby
Søren Kaae Sønderby
Ole Winther
    DRLGAN
ArXiv (abs)PDFHTML

Papers citing "Auxiliary Deep Generative Models"

30 / 230 papers shown
Title
Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities
Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities
Guo-Jun Qi
GAN
173
352
0
23 Jan 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
191
530
0
17 Jan 2017
An Architecture for Deep, Hierarchical Generative Models
An Architecture for Deep, Hierarchical Generative Models
Philip Bachman
AI4CEBDL
96
53
0
08 Dec 2016
Semi-Supervised Learning with the Deep Rendering Mixture Model
Semi-Supervised Learning with the Deep Rendering Mixture Model
M. T. Nguyen
Wanjia Liu
Ethan Perez
Richard G. Baraniuk
Ankit B. Patel
BDL
116
9
0
06 Dec 2016
A Probabilistic Framework for Deep Learning
A Probabilistic Framework for Deep Learning
Ankit B. Patel
M. T. Nguyen
Richard G. Baraniuk
BDL
127
68
0
06 Dec 2016
Piecewise Latent Variables for Neural Variational Text Processing
Piecewise Latent Variables for Neural Variational Text Processing
Iulian Serban
Alexander Ororbia
Joelle Pineau
Aaron Courville
DRLBDL
85
2
0
01 Dec 2016
Bottleneck Conditional Density Estimation
Bottleneck Conditional Density Estimation
Rui Shu
Hung Bui
Mohammad Ghavamzadeh
BDL
69
23
0
25 Nov 2016
Max-Margin Deep Generative Models for (Semi-)Supervised Learning
Max-Margin Deep Generative Models for (Semi-)Supervised Learning
Chongxuan Li
Jun Zhu
Bo Zhang
AI4CE
99
42
0
22 Nov 2016
Variational Boosting: Iteratively Refining Posterior Approximations
Variational Boosting: Iteratively Refining Posterior Approximations
Andrew C. Miller
N. Foti
Ryan P. Adams
101
124
0
20 Nov 2016
Variational Deep Embedding: An Unsupervised and Generative Approach to
  Clustering
Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering
Zhuxi Jiang
Yin Zheng
Huachun Tan
Bangsheng Tang
Hanning Zhou
BDLDRL
123
737
0
16 Nov 2016
Learning Sparse, Distributed Representations using the Hebbian Principle
Learning Sparse, Distributed Representations using the Hebbian Principle
Aseem Wadhwa
Upamanyu Madhow
62
9
0
14 Nov 2016
Normalizing Flows on Riemannian Manifolds
Normalizing Flows on Riemannian Manifolds
Mevlana Gemici
Danilo Jimenez Rezende
S. Mohamed
BDL
101
106
0
07 Nov 2016
Reparameterization trick for discrete variables
Reparameterization trick for discrete variables
Seiya Tokui
Issei Sato
53
11
0
04 Nov 2016
Operator Variational Inference
Operator Variational Inference
Rajesh Ranganath
Jaan Altosaar
Dustin Tran
David M. Blei
118
116
0
27 Oct 2016
Reparameterization Gradients through Acceptance-Rejection Sampling
  Algorithms
Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms
C. A. Naesseth
Francisco J. R. Ruiz
Scott W. Linderman
David M. Blei
BDL
152
107
0
18 Oct 2016
Temporal Ensembling for Semi-Supervised Learning
Temporal Ensembling for Semi-Supervised Learning
S. Laine
Timo Aila
UQCV
227
2,572
0
07 Oct 2016
Neural Photo Editing with Introspective Adversarial Networks
Neural Photo Editing with Introspective Adversarial Networks
Andrew Brock
Theodore Lim
J. Ritchie
Nick Weston
GAN
84
459
0
22 Sep 2016
Inverting Variational Autoencoders for Improved Generative Accuracy
Inverting Variational Autoencoders for Improved Generative Accuracy
I. Gemp
Ishan Durugkar
M. Parente
M. Dyar
Sridhar Mahadevan
DRLGAN
46
3
0
21 Aug 2016
InfoGAN: Interpretable Representation Learning by Information Maximizing
  Generative Adversarial Nets
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
Xi Chen
Yan Duan
Rein Houthooft
John Schulman
Ilya Sutskever
Pieter Abbeel
GAN
205
4,248
0
12 Jun 2016
Improved Techniques for Training GANs
Improved Techniques for Training GANs
Tim Salimans
Ian Goodfellow
Wojciech Zaremba
Vicki Cheung
Alec Radford
Xi Chen
GAN
648
9,091
0
10 Jun 2016
Towards a Neural Statistician
Towards a Neural Statistician
Harrison Edwards
Amos Storkey
BDL
166
428
0
07 Jun 2016
Adversarially Learned Inference
Adversarially Learned Inference
Vincent Dumoulin
Ishmael Belghazi
Ben Poole
Olivier Mastropietro
Alex Lamb
Martín Arjovsky
Aaron Courville
GAN
249
1,316
0
02 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
3,731
0
26 May 2016
Adversarial Training Methods for Semi-Supervised Text Classification
Adversarial Training Methods for Semi-Supervised Text Classification
Takeru Miyato
Andrew M. Dai
Ian Goodfellow
GAN
78
1,064
0
25 May 2016
Stick-Breaking Variational Autoencoders
Stick-Breaking Variational Autoencoders
Marco Cote
Padhraic Smyth
BDLDRL
132
163
0
20 May 2016
Variational Autoencoders for Semi-supervised Text Classification
Variational Autoencoders for Semi-supervised Text Classification
Weidi Xu
Haoze Sun
C. Deng
Y. Tan
DRL
64
7
0
08 Mar 2016
Note on the equivalence of hierarchical variational models and auxiliary
  deep generative models
Note on the equivalence of hierarchical variational models and auxiliary deep generative models
Niko Brummer
BDLGAN
15
1
0
08 Mar 2016
Ladder Variational Autoencoders
Ladder Variational Autoencoders
C. Sønderby
T. Raiko
Lars Maaløe
Søren Kaae Sønderby
Ole Winther
BDLDRL
185
917
0
06 Feb 2016
Adversarial Autoencoders
Adversarial Autoencoders
Alireza Makhzani
Jonathon Shlens
Navdeep Jaitly
Ian Goodfellow
Brendan J. Frey
GAN
154
2,231
0
18 Nov 2015
Hierarchical Variational Models
Hierarchical Variational Models
Rajesh Ranganath
Dustin Tran
David M. Blei
DRLVLM
121
337
0
07 Nov 2015
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