Fetching the paper…
Reading the bibliography…
Deep latent variable models have become a popular model choice due to the scalable learning algorithms introduced by (Kingma & Welling, 2013; Rezende et al., 2014).
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
Earlier work this paper cites.
The” wake-sleep” algorithm for unsupervised neural networks
Geoffrey E Hinton, Peter Dayan, Brendan J Frey, and Radford M Neal · 1995
Earlier work this paper cites.
An introduction to variational methods for graphical models
Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul · 1999
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Reweighted wake-sleep
Jörg Bornschein and Yoshua Bengio · 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.
Learning Wake-Sleep Recurrent Attention Models
Jimmy Ba, Ruslan R Salakhutdinov, Roger B Grosse, and Brendan J Frey · 2015
Earlier work this paper cites.
Importance weighted autoencoders
Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov · 2015
Earlier work this paper cites.
A recurrent latent variable model for sequential data
Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C Courville, and Yoshua Bengio · 2015
Earlier work this paper cites.
Neural adaptive sequential monte carlo
Shixiang Gu, Zoubin Ghahramani, and Richard E Turner · 2015
Earlier work this paper cites.
Rahul G Krishnan, Uri Shalit, and David Sontag · 2015
Earlier work this paper cites.
Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
Cited alongside, same era.
Variational lossy autoencoder
Xi Chen, Diederik P Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
Cited alongside, same era.
Sequential neural models with stochastic layers
Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, and Ole Winther · 2016
Cited alongside, same era.
Stochastic backpropagation through mixture density distributions
Alex Graves · 2016
Cited alongside, same era.
Pixelvae: A latent variable model for natural images
Ishaan Gulrajani, Kundan Kumar, Faruk Ahmed, Adrien Ali Taiga, Francesco Visin, David Vazquez, and Aaron Courville · 2016
Cited alongside, same era.
Improved variational inference with inverse autoregressive flow
Diederik P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
Sticking the landing: An asymptotically zero-variance gradient estimator for variational inference
Geoffrey Roeder, Yuhuai Wu, and David Duvenaud · 2017
Later among the works it cites.
Stochastic video generation with a learned prior
Emily Denton and Rob Fergus · 2018
Closest in time.
Implicit reparameterization gradients
Michael Figurnov, Shakir Mohamed, and Andriy Mnih · 2018
Closest in time.
World models
David Ha and Jürgen Schmidhuber · 2018
Closest in time.
Pathwise derivatives for multivariate distributions
Martin Jankowiak and Theofanis Karaletsos · 2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Variational inference for monte carlo objectives
Andriy Mnih and Danilo J Rezende · 2016
Cited alongside, same era.
Stochastic variational video prediction
Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H Campbell, and Sergey Levine · 2017
Cited alongside, same era.
Online learning rate adaptation with hypergradient descent
Atilim Gunes Baydin, Robert Cornish, David Martinez Rubio, Mark Schmidt, and Frank Wood · 2017
Cited alongside, same era.
Variational inference: A review for statisticians
David M Blei, Alp Kucukelbir, and Jon D McAuliffe · 2017
Cited alongside, same era.
Filtering variational objectives
Chris J Maddison, John Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, and Yee Teh · 2017
Cited alongside, same era.
Statistical inference , volume 2
George Casella and Roger L Berger
Cited in the paper.
Martin Jankowiak and Fritz Obermeyer · 2018
Closest in time.
Revisiting reweighted wake-sleep
Tuan Anh Le, Adam R Kosiorek, N Siddharth, Yee Whye Teh, and Frank Wood · 2018
Closest in time.
Variational sequential monte carlo
Christian Naesseth, Scott Linderman, Rajesh Ranganath, and David Blei · 2018
Closest in time.
Debiasing evidence approximations: On importance-weighted autoencoders and jackknife variational inference
Sebastian Nowozin · 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.