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Variational Auto-encoders (VAEs) are deep generative latent variable models that are widely used for a number of downstream tasks.
Lagging Inference Networks and Posterior Collapse in Variational Autoencoders
Junxian He, Daniel Spokoyny, Graham Neubig, and Taylor Berg-Kirkpatrick · 1901
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On importance-weighted autoencoders
Axel Finke and Alexandre H. Thiery · 1907
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The ”unusual episode” and a second statistics course
Jeffrey Simonoff · 1997
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Probabilistic principal component analysis
Michael E Tipping and Christopher M Bishop · 1999
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Local minima, symmetry-breaking, and model pruning in variational free energy minimization
David MacKay · 2001
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Estimating mutual information
Alexander Kraskov, Harald Stögbauer, and Peter Grassberger · 2004
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SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models
Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, and Ricky T. Q. Chen · 2004
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Keel data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework
Jesus Alcala-Fdez, Alberto Fernández, Julián Luengo, J. Derrac, S Garcia, Luciano Sanchez, and Francisco Herrera · 2010
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Two problems with variational expectation maximisation for time series models , page 104–124
Richard Eric Turner and Maneesh Sahani · 2011
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Auto-Encoding Variational Bayes
Diederik P Kingma and Max Welling · 2013
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An ensemble-based system for automatic screening of diabetic retinopathy
Bálint Antal and András Hajdu · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Semi-supervised learning with deep generative models
Durk P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
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Importance Weighted Autoencoders
Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov · 2016
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Categorical Reparameterization with Gumbel-Softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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Improved variational inference with inverse autoregressive flow
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Variational autoencoder for deep learning of images, labels and captions
Yunchen Pu, Zhe Gan, Ricardo Henao, Xin Yuan, Chunyuan Li, Andrew Stevens, and Lawrence Carin · 2016
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A Survey of Inductive Biases for Factorial Representation-Learning
Karl Ridgeway · 2016
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A note on the evaluation of generative models
Lucas Theis, Aaron van den Oord, and Matthias Bethge · 2016
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Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2017
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Reinterpreting Importance-Weighted Autoencoders
Chris Cremer, Quaid Morris, and David Duvenaud · 2017
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Learning Implicit Generative Models Using Differentiable Graph Tests
Josip Djolonga and Andreas Krause · 2017
Cited alongside, same era.
UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
Cited alongside, same era.
The Robust Manifold Defense: Adversarial Training using Generative Models
Ajil Jalal, Andrew Ilyas, Constantinos Daskalakis, and Alexandros G. Dimakis · 2017
Cited alongside, same era.
Magnet: a two-pronged defense against adversarial examples
Dongyu Meng and Hao Chen · 2017
Cited alongside, same era.
Sticking the landing: Simple, lower-variance gradient estimators for variational inference
Geoffrey Roeder, Yuhuai Wu, and David K Duvenaud · 2017
Cited alongside, same era.
Vae with a vampprior
Jakub Tomczak and Max Welling · 2018
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Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives
George Tucker, Dieterich Lawson, Shixiang Gu, and Chris J. Maddison · 2018
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Resampled priors for variational autoencoders
Matthias Bauer and Andriy Mnih · 2019
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Unsupervised Model Selection for Variational Disentangled Representation Learning
Sunny Duan, Loic Matthey, Andre Saraiva, Nicholas Watters, Christopher P. Burgess, Alexander Lerchner, and Irina Higgins · 2019
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Resisting adversarial attacks using gaussian mixture variational autoencoders
Partha Ghosh, Arpan Losalka, and Michael J Black · 2019
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Learning disentangled representations with semi-supervised deep generative models
N. Siddharth, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah D. Goodman, Pushmeet Kohli, Frank Wood, and Philip H. S. Torr · 2017
Cited alongside, same era.
Neural discrete representation learning
Aaron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu · 2017
Cited alongside, same era.
On the Quantitative Analysis of Decoder-Based Generative Models
Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, and Roger Grosse · 2017
Cited alongside, same era.
Fixing a broken elbo
Alexander Alemi, Ben Poole, Ian Fischer, Joshua Dillon, Rif A Saurous, and Kevin Murphy · 2018
Cited alongside, same era.
Isolating sources of disentanglement in variational autoencoders
Ricky TQ Chen, Xuechen Li, Roger B Grosse, and David K Duvenaud · 2018
Cited alongside, same era.
Inference suboptimality in variational autoencoders
Chris Cremer, Xuechen Li, and David Duvenaud · 2018
Cited alongside, same era.
Hyperspherical Variational Auto-Encoders
Tim R. Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, and Jakub M. Tomczak · 2018
Cited alongside, same era.
Puvae: A variational autoencoder to purify adversarial examples
Uiwon Hwang, Jaewoo Park, Hyemi Jang, Sungroh Yoon, and Nam Ik Cho · 2019
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On the Need for Topology-Aware Generative Models for Manifold-Based Defenses
Uyeong Jang, Susmit Jha, and Somesh Jha · 2019
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Challenging common assumptions in the unsupervised learning of disentangled representations
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem · 2019
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Don’t blame the elbo! a linear vae perspective on posterior collapse
James Lucas, George Tucker, Roger B Grosse, and Mohammad Norouzi · 2019
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Counterfactual reasoning for fair clinical risk prediction
Stephen R Pfohl, Tony Duan, Daisy Yi Ding, and Nigam H Shah · 2019
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Variational autoencoders pursue pca directions (by accident)
Michal Rolinek, Dominik Zietlow, and Georg Martius · 2019
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Interactive visual exploration of latent space (ivels) for peptide auto-encoder model selection
Tom Sercu, Sebastian Gehrmann, Hendrik Strobelt, Payel Das, Inkit Padhi, Cicero Dos Santos, Kahini Wadhawan, and Vijil Chenthamarakshan · 2019
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Towards Deeper Understanding of Variational Autoencoding Models
Shengjia Zhao, Jiaming Song, and Stefano Ermon · 2019
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Advances in black-box vi: Normalizing flows, importance weighting, and optimization
Abhinav Agrawal, Daniel R Sheldon, and Justin Domke · 2020
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The usual suspects? reassessing blame for vae posterior collapse
Bin Dai, Ziyu Wang, and David Wipf · 2020
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On the expressiveness of approximate inference in bayesian neural networks
Andrew Foong, David Burt, Yingzhen Li, and Richard Turner · 2020
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Independent subspace analysis for unsupervised learning of disentangled representations
Jan Stühmer, Richard Turner, and Sebastian Nowozin · 2020
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Characterizing and avoiding problematic global optima of variational autoencoders
Yaniv Yacoby, Weiwei Pan, and Finale Doshi-Velez · 2020
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On incorporating inductive biases into vaes
Ning Miao, Emile Mathieu, N Siddharth, Yee Whye Teh, and Tom Rainforth · 2021
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Lack of consistency of mean field and variational bayes approximations for state space models
Bo Wang and D.M. Titterington · 2024
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