Fetching the paper…
Reading the bibliography…
The variational autoencoder (VAE) is a popular model for density estimation and representation learning.
Clustering With Bregman Divergences
Arindam Banerjee, Srujana Merugu, Inderjit S Dhillon, and Joydeep Ghosh · 2005
Earlier work this paper cites.
Posterior Regularization For Structured Latent Variable Models
Kuzman Ganchev, Jennifer Gillenwater, Ben Taskar, et al · 2010
Earlier work this paper cites.
Auto-Encoding Variational Bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Stochastic Variational Inference
Matthew D Hoffman, David M Blei, Chong Wang, and John Paisley · 2013
Earlier work this paper cites.
Semi-Supervised Learning With Deep Generative Models
Diederik P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
Earlier work this paper cites.
Stochastic Backpropagation And Approximate Inference In Deep Generative Models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
Earlier work this paper cites.
Bayesian Inference With Posterior Regularization And Applications To Infinite Latent Svms
Jun Zhu, Ning Chen, and Eric P Xing · 2014
Earlier work this paper cites.
Adam: A method For Stochastic Optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Explaining And Harnessing Adversarial Examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Importance Weighted Autoencoders
Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov · 2015
Earlier work this paper cites.
Improved Variational Inference With Inverse Autoregressive Flow
Diederik P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
Earlier work this paper cites.
Ladder Variational Autoencoders
Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, and Ole Winther · 2016
Cited alongside, same era.
On The Quantitative Analysis Of Decoder-Based Generative Models
Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, and Roger Grosse · 2016
Cited alongside, same era.
Auxiliary Deep Generative Models
Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, and Ole Winther · 2016
Cited alongside, same era.
Hierarchical Variational Models
Rajesh Ranganath, Dustin Tran, and David Blei · 2016
Cited alongside, same era.
Weight Normalization: A Simple Reparameterization To Accelerate Training Of Deep Neural Networks
Tim Salimans and Diederik P Kingma · 2016
Cited alongside, same era.
Learning Deep Latent Gaussian Models With Markov Chain Monte Carlo
Matthew D Hoffman · 2017
Later among the works it cites.
Jakub M Tomczak and Max Welling · 2017
Later among the works it cites.
Sharp Minima Can Generalize For Deep Nets
Laurent Dinh, Razvan Pascanu, Samy Bengio, and Yoshua Bengio · 2017
Later among the works it cites.
Hyunjik Kim and Andriy Mnih · 2018
Closest in time.
Isolating Sources Of Disentanglement In Variational Autoencoders
Tian Qi Chen, Xuechen Li, Roger Grosse, and David Duvenaud · 2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Yingzhen Li and Richard E Turner · 2016
Cited alongside, same era.
Understanding Deep Learning Requires Rethinking Generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
Cited alongside, same era.
On the challenges of learning with inference networks on sparse, high-dimensional data
Rahul G Krishnan, Dawen Liang, and Matthew Hoffman · 2017
Cited alongside, same era.
Denoising Criterion For Variational Auto-Encoding Framework
Daniel Jiwoong Im, Sungjin Ahn, Roland Memisevic, Yoshua Bengio, et al · 2017
Cited alongside, same era.
From Optimal Transport To Generative Modeling: The VEGAN Cookbook
Olivier Bousquet, Sylvain Gelly, Ilya Tolstikhin, Carl-Johann Simon-Gabriel, and Bernhard Schoelkopf · 2017
Cited alongside, same era.
Reinterpreting Importance-Weighted Autoencoders
Chris Cremer, Quaid Morris, and David Duvenaud · 2017
Cited alongside, same era.
Semi-Amortized Variational Autoencoders
Yoon Kim, Sam Wiseman, Andrew C Miller, David Sontag, and Alexander M Rush · 2018
Closest in time.
Inference Suboptimality In Variational Autoencoders
Chris Cremer, Xuechen Li, and David Duvenaud · 2018
Closest in time.
Tighter Variational Bounds Are Not Necessarily Better
Tom Rainforth, Adam R Kosiorek, Tuan Anh Le, Chris J Maddison, Maximilian Igl, Frank Wood, and Yee Whye Teh · 2018
Closest in time.
Spectral Normalization For Generative Adversarial Networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
Closest in time.
A bayesian Perspective On Generalization And Stochastic Gradient Descent
Samuel L. Smith and Quoc V. Le · 2018
Closest in time.
Revisiting Small Batch Training For Deep Neural Networks
Dominic Masters and Carlo Luschi · 2018
Closest in time.