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Importance Weighted Autoencoders
v1v2v3v4 (latest)

Importance Weighted Autoencoders

International Conference on Learning Representations (ICLR), 2015
1 September 2015
Yuri Burda
Roger C. Grosse
Ruslan Salakhutdinov
    BDL
ArXiv (abs)PDFHTML

Papers citing "Importance Weighted Autoencoders"

50 / 816 papers shown
Isometric Autoencoders
Isometric Autoencoders
Amos Gropp
Matan Atzmon
Y. Lipman
DRL
229
20
0
16 Jun 2020
Density of States Estimation for Out-of-Distribution Detection
Density of States Estimation for Out-of-Distribution Detection
Warren Morningstar
Cusuh Ham
Andrew Gallagher
Balaji Lakshminarayanan
Alexander A. Alemi
Joshua V. Dillon
OODD
272
92
0
16 Jun 2020
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence
Thomas M. Sutter
Imant Daunhawer
Julia E. Vogt
285
89
0
15 Jun 2020
Hindsight Expectation Maximization for Goal-conditioned Reinforcement
  Learning
Hindsight Expectation Maximization for Goal-conditioned Reinforcement LearningInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2020
Yunhao Tang
A. Kucukelbir
OffRL
176
17
0
13 Jun 2020
Manifold GPLVMs for discovering non-Euclidean latent structure in neural
  data
Manifold GPLVMs for discovering non-Euclidean latent structure in neural dataNeural Information Processing Systems (NeurIPS), 2020
Kristopher T. Jensen
Ta-Chu Kao
Marco Tripodi
Guillaume Hennequin
DRL
222
34
0
12 Jun 2020
Improving GAN Training with Probability Ratio Clipping and Sample
  Reweighting
Improving GAN Training with Probability Ratio Clipping and Sample ReweightingNeural Information Processing Systems (NeurIPS), 2020
Yue Wu
Pan Zhou
A. Wilson
Eric Xing
Zhiting Hu
GAN
329
36
0
12 Jun 2020
Cumulant GAN
Cumulant GANIEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2020
Yannis Pantazis
D. Paul
M. Fasoulakis
Y. Stylianou
Markos A. Katsoulakis
GAN
324
21
0
11 Jun 2020
A Generalised Linear Model Framework for $β$-Variational
  Autoencoders based on Exponential Dispersion Families
A Generalised Linear Model Framework for βββ-Variational Autoencoders based on Exponential Dispersion FamiliesJournal of machine learning research (JMLR), 2020
Robert Sicks
R. Korn
Stefanie Schwaar
249
13
0
11 Jun 2020
Probabilistic Autoencoder
Probabilistic Autoencoder
Vanessa Böhm
U. Seljak
UQCVBDLDRL
229
32
0
09 Jun 2020
VAEs in the Presence of Missing Data
VAEs in the Presence of Missing Data
Mark Collier
A. Nazábal
Christopher K. I. Williams
DRL
191
37
0
09 Jun 2020
Super-resolution Variational Auto-Encoders
Super-resolution Variational Auto-Encoders
Ioannis Gatopoulos
M. Stol
Jakub M. Tomczak
SupRDiffM
118
15
0
09 Jun 2020
AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation
AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation
Jae Hyun Lim
Aaron Courville
C. Pal
Chin-Wei Huang
DRL
169
24
0
09 Jun 2020
MHVAE: a Human-Inspired Deep Hierarchical Generative Model for
  Multimodal Representation Learning
MHVAE: a Human-Inspired Deep Hierarchical Generative Model for Multimodal Representation Learning
Miguel Vasco
Francisco S. Melo
Ana Paiva
DRL
101
11
0
04 Jun 2020
Joint Stochastic Approximation and Its Application to Learning Discrete
  Latent Variable Models
Joint Stochastic Approximation and Its Application to Learning Discrete Latent Variable ModelsConference on Uncertainty in Artificial Intelligence (UAI), 2020
Zhijian Ou
Yunfu Song
BDL
227
11
0
28 May 2020
A Batch Normalized Inference Network Keeps the KL Vanishing Away
A Batch Normalized Inference Network Keeps the KL Vanishing AwayAnnual Meeting of the Association for Computational Linguistics (ACL), 2020
Qile Zhu
Jianlin Su
Wei Bi
Xiaojiang Liu
Xiyao Ma
Xiaolin Li
D. Wu
BDLDRL
142
64
0
27 Apr 2020
VOWEL: A Local Online Learning Rule for Recurrent Networks of
  Probabilistic Spiking Winner-Take-All Circuits
VOWEL: A Local Online Learning Rule for Recurrent Networks of Probabilistic Spiking Winner-Take-All CircuitsInternational Conference on Pattern Recognition (ICPR), 2020
Hyeryung Jang
N. Skatchkovsky
Osvaldo Simeone
269
14
0
20 Apr 2020
Do sequence-to-sequence VAEs learn global features of sentences?
Do sequence-to-sequence VAEs learn global features of sentences?Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020
Tom Bosc
Pascal Vincent
SSL
189
9
0
16 Apr 2020
Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and
  Data Augmentation
Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation
Sajad Norouzi
David J. Fleet
Mohammad Norouzi
VLMDRL
155
3
0
09 Apr 2020
Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space
Optimus: Organizing Sentences via Pre-trained Modeling of a Latent SpaceConference on Empirical Methods in Natural Language Processing (EMNLP), 2020
Chunyuan Li
Xiang Gao
Yuan Li
Baolin Peng
Xiujun Li
Yizhe Zhang
Jianfeng Gao
SSLDRL
462
194
0
05 Apr 2020
Epitomic Variational Graph Autoencoder
Epitomic Variational Graph AutoencoderInternational Conference on Pattern Recognition (ICPR), 2020
R. A. Khan
Muhammad Umer Anwaar
M. Kleinsteuber
BDL
253
11
0
03 Apr 2020
SUMO: Unbiased Estimation of Log Marginal Probability for Latent
  Variable Models
SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable ModelsInternational Conference on Learning Representations (ICLR), 2020
Yucen Luo
Alex Beatson
Mohammad Norouzi
Jun Zhu
David Duvenaud
Ryan P. Adams
Ricky T. Q. Chen
256
30
0
01 Apr 2020
MCFlow: Monte Carlo Flow Models for Data Imputation
MCFlow: Monte Carlo Flow Models for Data ImputationComputer Vision and Pattern Recognition (CVPR), 2020
Trevor W. Richardson
Wencheng Wu
Lei Lin
Beilei Xu
Edgar A. Bernal
OOD
158
53
0
27 Mar 2020
Unpacking Information Bottlenecks: Unifying Information-Theoretic
  Objectives in Deep Learning
Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning
Andreas Kirsch
Clare Lyle
Y. Gal
281
17
0
27 Mar 2020
Markovian Score Climbing: Variational Inference with KL(p||q)
Markovian Score Climbing: Variational Inference with KL(p||q)Neural Information Processing Systems (NeurIPS), 2020
C. A. Naesseth
Fredrik Lindsten
David M. Blei
299
59
0
23 Mar 2020
Social Navigation with Human Empowerment driven Deep Reinforcement
  Learning
Social Navigation with Human Empowerment driven Deep Reinforcement Learning
T. V. D. Heiden
Christian Weiss
H. V. Hoof
279
15
0
18 Mar 2020
Autoencoders
Autoencoders
Dor Bank
Noam Koenigstein
Raja Giryes
HAI
128
0
0
12 Mar 2020
VMLoc: Variational Fusion For Learning-Based Multimodal Camera
  Localization
VMLoc: Variational Fusion For Learning-Based Multimodal Camera LocalizationAAAI Conference on Artificial Intelligence (AAAI), 2020
Kaichen Zhou
Changhao Chen
Bing Wang
Muhamad Risqi U. Saputra
Niki Trigoni
Andrew Markham
269
25
0
12 Mar 2020
Variational Learning of Individual Survival Distributions
Variational Learning of Individual Survival DistributionsACM Conference on Health, Inference, and Learning (CHIL), 2020
Zidi Xiu
Chenyang Tao
Benjamin A. Goldstein
Ricardo Henao
OOD
182
17
0
09 Mar 2020
Scalable Approximate Inference and Some Applications
Scalable Approximate Inference and Some Applications
Jun Han
BDL
155
1
0
07 Mar 2020
Likelihood Regret: An Out-of-Distribution Detection Score For
  Variational Auto-encoder
Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoderNeural Information Processing Systems (NeurIPS), 2020
Zhisheng Xiao
Qing Yan
Y. Amit
OODD
390
213
0
06 Mar 2020
Automatic Differentiation Variational Inference with Mixtures
Automatic Differentiation Variational Inference with MixturesInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2020
Warren Morningstar
Sharad M. Vikram
Cusuh Ham
Andrew Gallagher
Joshua V. Dillon
DRLBDL
238
25
0
03 Mar 2020
Curriculum By Smoothing
Curriculum By Smoothing
Samarth Sinha
Animesh Garg
Hugo Larochelle
303
7
0
03 Mar 2020
Predictive Coding for Locally-Linear Control
Predictive Coding for Locally-Linear ControlInternational Conference on Machine Learning (ICML), 2020
Rui Shu
Tung D. Nguyen
Yinlam Chow
Tu Pham
Khoat Than
Mohammad Ghavamzadeh
Stefano Ermon
Hung Bui
OffRLBDL
288
27
0
02 Mar 2020
Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
Modeling Continuous Stochastic Processes with Dynamic Normalizing FlowsNeural Information Processing Systems (NeurIPS), 2020
Ruizhi Deng
B. Chang
Marcus A. Brubaker
Greg Mori
Andreas M. Lehrmann
294
58
0
24 Feb 2020
Variational Hyper RNN for Sequence Modeling
Variational Hyper RNN for Sequence Modeling
Ruizhi Deng
Yanshuai Cao
B. Chang
Leonid Sigal
Greg Mori
Marcus A. Brubaker
BDLDRL
83
2
0
24 Feb 2020
Variance Loss in Variational Autoencoders
Variance Loss in Variational AutoencodersInternational Conference on Machine Learning, Optimization, and Data Science (MOD), 2020
Andrea Asperti
DRL
159
17
0
23 Feb 2020
Amortised Learning by Wake-Sleep
Amortised Learning by Wake-SleepInternational Conference on Machine Learning (ICML), 2020
W. Li
Theodore H. Moskovitz
Heishiro Kanagawa
M. Sahani
OOD
307
7
0
22 Feb 2020
Balancing reconstruction error and Kullback-Leibler divergence in
  Variational Autoencoders
Balancing reconstruction error and Kullback-Leibler divergence in Variational AutoencodersIEEE Access (IEEE Access), 2020
Andrea Asperti
Matteo Trentin
DRL
209
125
0
18 Feb 2020
Decision-Making with Auto-Encoding Variational Bayes
Decision-Making with Auto-Encoding Variational BayesNeural Information Processing Systems (NeurIPS), 2020
Romain Lopez
Pierre Boyeau
Nir Yosef
Michael I. Jordan
Jeffrey Regier
BDL
1.5K
19,430
0
17 Feb 2020
Augmented Normalizing Flows: Bridging the Gap Between Generative Flows
  and Latent Variable Models
Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models
Chin-Wei Huang
Laurent Dinh
Aaron Courville
DRL
299
92
0
17 Feb 2020
Latent Variable Modelling with Hyperbolic Normalizing Flows
Latent Variable Modelling with Hyperbolic Normalizing FlowsInternational Conference on Machine Learning (ICML), 2020
A. Bose
Ariella Smofsky
Renjie Liao
Prakash Panangaden
William L. Hamilton
DRL
252
74
0
15 Feb 2020
Stochastic Approximate Gradient Descent via the Langevin Algorithm
Stochastic Approximate Gradient Descent via the Langevin AlgorithmAAAI Conference on Artificial Intelligence (AAAI), 2020
Yixuan Qiu
Tianlin Li
157
4
0
13 Feb 2020
Deep Learning for Source Code Modeling and Generation: Models,
  Applications and Challenges
Deep Learning for Source Code Modeling and Generation: Models, Applications and ChallengesACM Computing Surveys (ACM CSUR), 2020
T. H. Le
Hao Chen
Muhammad Ali Babar
VLM
249
171
0
13 Feb 2020
Learning Flat Latent Manifolds with VAEs
Learning Flat Latent Manifolds with VAEsInternational Conference on Machine Learning (ICML), 2020
Nutan Chen
Alexej Klushyn
Francesco Ferroni
Justin Bayer
Patrick van der Smagt
DRL
213
48
0
12 Feb 2020
Missing Data Imputation using Optimal Transport
Missing Data Imputation using Optimal TransportInternational Conference on Machine Learning (ICML), 2020
Boris Muzellec
Julie Josse
Claire Boyer
Marco Cuturi
OT
175
152
0
10 Feb 2020
Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow
Closing the Dequantization Gap: PixelCNN as a Single-Layer FlowNeural Information Processing Systems (NeurIPS), 2020
Didrik Nielsen
Ole Winther
MQ
426
13
0
06 Feb 2020
Learning Discrete Distributions by Dequantization
Learning Discrete Distributions by Dequantization
Emiel Hoogeboom
Taco S. Cohen
Jakub M. Tomczak
DRL
165
36
0
30 Jan 2020
A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor
  Analysis
A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysis
Christopher J. Urban
Daniel J. Bauer
BDL
332
37
0
22 Jan 2020
Joint Distributions for TensorFlow Probability
Joint Distributions for TensorFlow Probability
Dan Piponi
Dave Moore
Joshua V. Dillon
GP
168
17
0
22 Jan 2020
Approximating Activation Functions
Approximating Activation Functions
Nicholas Gerard Timmons
Andrew Rice
112
17
0
17 Jan 2020
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