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Wasserstein Auto-Encoders

Wasserstein Auto-Encoders

5 November 2017
Ilya O. Tolstikhin
Olivier Bousquet
Sylvain Gelly
B. Schölkopf
    DRL
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Papers citing "Wasserstein Auto-Encoders"

29 / 229 papers shown
Title
LOSSGRAD: automatic learning rate in gradient descent
LOSSGRAD: automatic learning rate in gradient descent
B. Wójcik
Lukasz Maziarka
Jacek Tabor
ODL
40
4
0
20 Feb 2019
2-Wasserstein Approximation via Restricted Convex Potentials with
  Application to Improved Training for GANs
2-Wasserstein Approximation via Restricted Convex Potentials with Application to Improved Training for GANs
Amirhossein Taghvaei
Amin Jalali
33
43
0
19 Feb 2019
(q,p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs
(q,p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs
Anton Mallasto
J. Frellsen
Wouter Boomsma
Aasa Feragen
22
15
0
10 Feb 2019
Deep Generative Learning via Variational Gradient Flow
Deep Generative Learning via Variational Gradient Flow
Yuan Gao
Yuling Jiao
Yang Wang
Yao Wang
Can Yang
Shunkang Zhang
27
36
0
24 Jan 2019
Continuous Hierarchical Representations with Poincaré Variational
  Auto-Encoders
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders
Emile Mathieu
Charline Le Lan
Chris J. Maddison
Ryota Tomioka
Yee Whye Teh
BDL
DRL
34
172
0
17 Jan 2019
Lagging Inference Networks and Posterior Collapse in Variational
  Autoencoders
Lagging Inference Networks and Posterior Collapse in Variational Autoencoders
Junxian He
Daniel M. Spokoyny
Graham Neubig
Taylor Berg-Kirkpatrick
BDL
DRL
25
272
0
16 Jan 2019
Conditional deep surrogate models for stochastic, high-dimensional, and
  multi-fidelity systems
Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems
Yibo Yang
P. Perdikaris
SyDa
BDL
AI4CE
29
55
0
15 Jan 2019
Conditional Recurrent Flow: Conditional Generation of Longitudinal
  Samples with Applications to Neuroimaging
Conditional Recurrent Flow: Conditional Generation of Longitudinal Samples with Applications to Neuroimaging
Seong Jae Hwang
Zirui Tao
Won Hwa Kim
Vikas Singh
MedIm
17
12
0
24 Nov 2018
Spread Divergence
Spread Divergence
Mingtian Zhang
Peter Hayes
Thomas Bird
Raza Habib
David Barber
MedIm
UD
30
20
0
21 Nov 2018
Deep Knockoffs
Deep Knockoffs
Yaniv Romano
Matteo Sesia
Emmanuel J. Candès
BDL
18
139
0
16 Nov 2018
Gaussian AutoEncoder
Gaussian AutoEncoder
J. Duda
DRL
20
1
0
12 Nov 2018
Adversarial Uncertainty Quantification in Physics-Informed Neural
  Networks
Adversarial Uncertainty Quantification in Physics-Informed Neural Networks
Yibo Yang
P. Perdikaris
AI4CE
PINN
32
355
0
09 Nov 2018
Sparsemax and Relaxed Wasserstein for Topic Sparsity
Sparsemax and Relaxed Wasserstein for Topic Sparsity
Tianyi Lin
Zhibo Hu
Xin Guo
14
37
0
22 Oct 2018
Point Cloud GAN
Point Cloud GAN
Chun-Liang Li
Manzil Zaheer
Yang Zhang
Barnabás Póczós
Ruslan Salakhutdinov
3DPC
42
209
0
13 Oct 2018
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample
  Likelihoods in GANs
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs
Yogesh Balaji
Hamed Hassani
Rama Chellappa
S. Feizi
GAN
DRL
41
20
0
09 Oct 2018
DT-LET: Deep Transfer Learning by Exploring where to Transfer
DT-LET: Deep Transfer Learning by Exploring where to Transfer
Jianzhe Lin
Qi. Wang
Rabab Ward
Z. J. Wang
16
27
0
23 Sep 2018
Variational Autoencoder with Implicit Optimal Priors
Variational Autoencoder with Implicit Optimal Priors
Hiroshi Takahashi
Tomoharu Iwata
Yuki Yamanaka
Masanori Yamada
Satoshi Yagi
DRL
34
61
0
14 Sep 2018
Analyzing Inverse Problems with Invertible Neural Networks
Analyzing Inverse Problems with Invertible Neural Networks
Lynton Ardizzone
Jakob Kruse
Sebastian J. Wirkert
D. Rahner
E. Pellegrini
R. Klessen
Lena Maier-Hein
Carsten Rother
Ullrich Kothe
21
483
0
14 Aug 2018
A Review of Learning with Deep Generative Models from Perspective of
  Graphical Modeling
A Review of Learning with Deep Generative Models from Perspective of Graphical Modeling
Zhijian Ou
31
16
0
05 Aug 2018
Learning Implicit Generative Models by Teaching Explicit Ones
Learning Implicit Generative Models by Teaching Explicit Ones
Chao Du
Kun Xu
Chongxuan Li
Jun Zhu
Bo Zhang
DRL
GAN
14
9
0
10 Jul 2018
Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation
Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation
Hareesh Bahuleyan
Lili Mou
Hao Zhou
Olga Vechtomova
BDL
DRL
27
6
0
22 Jun 2018
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal
  Transport and Diffusions
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions
Antoine Liutkus
Umut Simsekli
Szymon Majewski
Alain Durmus
Fabian-Robert Stöter
DiffM
35
120
0
21 Jun 2018
Learning Factorized Multimodal Representations
Learning Factorized Multimodal Representations
Yao-Hung Hubert Tsai
Paul Pu Liang
Amir Zadeh
Louis-Philippe Morency
Ruslan Salakhutdinov
DRL
63
402
0
16 Jun 2018
DialogWAE: Multimodal Response Generation with Conditional Wasserstein
  Auto-Encoder
DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder
Xiaodong Gu
Kyunghyun Cho
Jung-Woo Ha
Sunghun Kim
DRL
41
129
0
31 May 2018
On representation power of neural network-based graph embedding and
  beyond
On representation power of neural network-based graph embedding and beyond
Akifumi Okuno
Hidetoshi Shimodaira
21
2
0
31 May 2018
Wasserstein Variational Inference
Wasserstein Variational Inference
L. Ambrogioni
Umut Güçlü
Yağmur Güçlütürk
Max Hinne
E. Maris
Marcel van Gerven
BDL
DRL
19
42
0
29 May 2018
Deep Generative Models for Distribution-Preserving Lossy Compression
Deep Generative Models for Distribution-Preserving Lossy Compression
Michael Tschannen
E. Agustsson
Mario Lucic
16
130
0
28 May 2018
Primal-Dual Wasserstein GAN
Primal-Dual Wasserstein GAN
Mevlana Gemici
Zeynep Akata
Max Welling
41
13
0
24 May 2018
Generating Natural Adversarial Examples
Generating Natural Adversarial Examples
Zhengli Zhao
Dheeru Dua
Sameer Singh
GAN
AAML
38
596
0
31 Oct 2017
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