Fetching the paper…
Reading the bibliography…
Normalizing flows model complex probability distributions by combining a base distribution with a series of bijective neural networks.
Normalizing flows for probabilistic modeling and inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 1912
Earlier work this paper cites.
d-separation: From theorems to algorithms
Dan Geiger, Thomas Verma, and Judea Pearl · 1990
Earlier work this paper cites.
DIAVAL, a Bayesian expert system for echocardiography
F.J. Díez, J. Mira, E. Iturralde, and S. Zubillaga · 1997
Earlier work this paper cites.
Construction of a Bayesian network for mammographic diagnosis of breast cancer
Charles E. Kahn, Linda M. Roberts, Katherine A. Shaffer, and Peter Haddawy · 1997
Earlier work this paper cites.
Causation, Prediction, and Search
Peter Spirtes, Clark Glymour, and Richard Scheines · 2001
Earlier work this paper cites.
Large-sample learning of Bayesian networks is NP-hard
David Maxwell Chickering, David Heckerman, and Christopher Meek · 2004
Earlier work this paper cites.
Causal protein-signaling networks derived from multiparameter single-cell data
K. Sachs, O. Perez, D. Pe’er, D. A. Lauffenburger, and G. P. Nolan · 2005
Earlier work this paper cites.
Probabilistic graphical models: Principles and techniques
Daphne Koller and Nir Friedman · 2009
Earlier work this paper cites.
Density estimation by dual ascent of the log-likelihood
Esteban G Tabak, Eric Vanden-Eijnden, et al · 2010
Earlier work this paper cites.
Bayesian networks
Judea Pearl and Stuart Russell · 2011
Earlier work this paper cites.
Algorithms
Robert Sedgewick and Kevin Wayne · 2011
Earlier work this paper cites.
High-dimensional probability estimation with deep density models
Oren Rippel and Ryan Prescott Adams · 2013
Earlier work this paper cites.
A family of nonparametric density estimation algorithms
Esteban G Tabak and Cristina V Turner · 2013
Earlier work this paper cites.
A Bayesian network decision model for supporting the diagnosis of dementia, alzheimer’s disease and mild cognitive impairment
Flávio Luiz Seixas, Bianca Zadrozny, Jerson Laks, Aura Conci, and Débora Christina Muchaluat Saade · 2014
Earlier work this paper cites.
Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2015
Earlier work this paper cites.
Made: Masked autoencoder for distribution estimation
Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle · 2015
Earlier work this paper cites.
Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
Cited alongside, same era.
Characterizing dag-depth of directed graphs
Matúš Bezek · 2016
Cited alongside, same era.
Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
Cited alongside, same era.
Composing graphical models with neural networks for structured representations and fast inference
Matthew J Johnson, David K Duvenaud, Alex Wiltschko, Ryan P Adams, and Sandeep R Datta · 2016
Cited alongside, same era.
Improved variational inference with inverse autoregressive flow
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
Cited alongside, same era.
DAGs with NO TEARS: Continuous optimization for structure learning
Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, and Eric P Xing · 2018
Later among the works it cites.
Block neural autoregressive flow
Nicola De Cao, Ivan Titov, and Wilker Aziz · 2019
Later among the works it cites.
Neural spline flows
Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 2019
Later among the works it cites.
Automatic posterior transformation for likelihood-free inference
David S Greenberg, Marcel Nonnenmacher, and Jakob H Macke · 2019
Later among the works it cites.
Sum-of-squares polynomial flow
Priyank Jaini, Kira A Selby, and Yaoliang Yu · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Chris J Maddison, Andriy Mnih, and Yee Whye Teh · 2016
Cited alongside, same era.
Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
Cited alongside, same era.
Masked autoregressive flow for density estimation
George Papamakarios, Theo Pavlakou, and Iain Murray · 2017
Cited alongside, same era.
FFJORD: Free-form continuous dynamics for scalable reversible generative models
Will Grathwohl, Ricky TQ Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud · 2018
Cited alongside, same era.
Chin-Wei Huang, David Krueger, Alexandre Lacoste, and Aaron Courville · 2018
Cited alongside, same era.
Flowavenet: A generative flow for raw audio
Sungwon Kim, Sang-gil Lee, Jongyoon Song, Jaehyeon Kim, and Sungroh Yoon · 2018
Cited alongside, same era.
Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
Cited alongside, same era.
Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, and Simon Lacoste-Julien · 2019
Later among the works it cites.
Waveglow: A flow-based generative network for speech synthesis
Ryan Prenger, Rafael Valle, and Bryan Catanzaro · 2019
Later among the works it cites.
Unconstrained monotonic neural networks
Antoine Wehenkel and Gilles Louppe · 2019
Later among the works it cites.
DAG-GNN: Dag structure learning with graph neural networks
Yue Yu, Jie Chen, Tian Gao, and Mo Yu · 2019
Later among the works it cites.
Block neural autoregressive flow
Nicola De Cao, Wilker Aziz, and Ivan Titov · 2020
Closest in time.
Ilyes Khemakhem, Ricardo Pio Monti, Robert Leech, and Aapo Hyvärinen · 2020
Closest in time.
Normalizing flows: An introduction and review of current methods
Ivan Kobyzev, Simon Prince, and Marcus Brubaker · 2020
Closest in time.
You say normalizing flows i see bayesian networks
Antoine Wehenkel and Gilles Louppe · 2020
Closest in time.
Structured conditional continuous normalizing flows for efficient amortized inference in graphical models
Christian Weilbach, Boyan Beronov, Frank Wood, and William Harvey · 2020
Closest in time.
Learning sparse nonparametric dags
Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, and Eric Xing · 2020
Closest in time.