Fetching the paper…
Reading the bibliography…
Normalizing flows provide a general mechanism for defining expressive probability distributions, only requiring the specification of a (usually simple) base distribution and a series of bijective transformations.
Neural density estimation and likelihood-free inference
George Papamakarios · 1910
Earlier work this paper cites.
Remarks on a multivariate transformation
Murray Rosenblatt · 1952
Earlier work this paper cites.
Theory of ordinary differential equations
Earl A. Coddington and Norman Levinson · 1955
Earlier work this paper cites.
Fonctions de Répartition à N Dimensions et Leurs Marges
Abe Sklar · 1959
Earlier work this paper cites.
Mathematical theory of optimal processes
Lev Semenovich Pontryagin · 1962
Earlier work this paper cites.
Foundations of differential geometry , volume 1
Shoshichi Kobayashi and Katsumi Nomizu · 1963
Earlier work this paper cites.
The minimal transformation to orthonormality
Richard M. Johnson · 1966
Earlier work this paper cites.
Principles of mathematical analysis
Walter Rudin · 1976
Earlier work this paper cites.
Monte Carlo methods of inference for implicit statistical models
Peter J. Diggle and Richard J. Gratton · 1984
Earlier work this paper cites.
Hybrid Monte Carlo
Simon Duane, Anthony D. Kennedy, Brian J. Pendleton, and Duncan Roweth · 1987
Earlier work this paper cites.
Exploratory projection pursuit
Jerome H. Friedman · 1987
Earlier work this paper cites.
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Judea Pearl · 1988
Earlier work this paper cites.
Numerical Analysis
Richard L. Burden and J. Douglas Faires · 1989
Earlier work this paper cites.
A stochastic estimator of the trace of the influence matrix for Laplacian smoothing splines
Michael F. Hutchinson · 1990
Earlier work this paper cites.
Fokker–Planck equation
Hannes Risken · 1996
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Topology from the differentiable viewpoint
John W. Milnor and David W. Weaver · 1997
Earlier work this paper cites.
Graphical models for machine learning and digital communication
Brendan J Frey · 1998
Earlier work this paper cites.
Nonlinear independent component analysis: Existence and uniqueness results
Aapo Hyvärinen and Petteri Pajunen · 1999
Earlier work this paper cites.
Modeling high-dimensional discrete data with multi-layer neural networks
Yoshua Bengio and Samy Bengio · 2000
Earlier work this paper cites.
Gaussianization
Scott Saobing Chen and Ramesh A. Gopinath · 2000
Earlier work this paper cites.
Approximate Bayesian computation in population genetics
Mark A. Beaumont, Wenyang Zhang, and David J. Balding · 2002
Earlier work this paper cites.
Triangular transformations of measures
Vladimir I. Bogachev, Alexander V. Kolesnikov, and Kirill V. Medvedev · 2005
Earlier work this paper cites.
Real and complex analysis
Walter Rudin · 2006
Earlier work this paper cites.
Measure Theory
Vladimir I. Bogachev · 2007
Earlier work this paper cites.
Positive polynomials and sums of squares
Murray Marshall · 2008
Earlier work this paper cites.
Optimal transport: Old and new , volume 338
Cédric Villani · 2008
Earlier work this paper cites.
Graphical models, exponential families, and variational inference
Martin J. Wainwright and Michael I. Jordan · 2008
Earlier work this paper cites.
Approximate Bayesian computation in evolution and ecology
Mark A. Beaumont · 2010
Earlier work this paper cites.
From Knothe’s transport to Brenier’s map and a continuation method for optimal transport
Guillaume Carlier, Alfred Galichon, and Filippo Santambrogio · 2010
Earlier work this paper cites.
Recurrent neural network based language model
Tomáš Mikolov, Martin Karafiát, Lukáš Burget, Jan Černockỳ, and Sanjeev Khudanpur · 2010
Earlier work this paper cites.
MCMC using Hamiltonian dynamics
Radford M. Neal · 2010
Earlier work this paper cites.
Lecture notes on numerical solutions of ordinary differential equations, 2010
Endre Süli · 2010
Earlier work this paper cites.
Density estimation by dual ascent of the log-likelihood
Esteban G. Tabak and Eric Vanden-Eijnden · 2010
Earlier work this paper cites.
Iterative Gaussianization: From ICA to random rotations
Valero Laparra, Gustavo Camps-Valls, and Jesús Malo · 2011
Earlier work this paper cites.
The neural autoregressive distribution estimator
Hugo Larochelle and Iain Murray · 2011
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
Earlier work this paper cites.
Copulas in machine learning
Gal Elidan · 2013
Earlier work this paper cites.
Generating sequences with recurrent neural networks
Alex Graves · 2013
Earlier work this paper cites.
Rectifier nonlinearities improve neural network acoustic models
Andrew L. Maas, Awni Y. Hannun, and Andrew Y. Ng · 2013
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.
RNADE: The real-valued neural autoregressive density-estimator
Benigno Uria, Iain Murray, and Hugo Larochelle · 2013
Earlier work this paper cites.
Learning phrase representations using RNN encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, 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.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le · 2014
Earlier work this paper cites.
A deep and tractable density estimator
Benigno Uria, Iain Murray, and Hugo Larochelle · 2014
Cited alongside, same era.
NICE: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2015
Cited alongside, same era.
MADE: Masked autoencoder for distribution estimation
Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Cited alongside, same era.
Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
Cited alongside, same era.
The representation and parametrization of orthogonal matrices
Ron Shepard, Scott R. Brozell, and Gergely Gidofalvi · 2015
Cited alongside, same era.
Residual flows for invertible generative modeling
Ricky T. Q. Chen, Jens Behrmann, David K. Duvenaud, and Jörn-Henrik Jacobsen · 2019
Closest in time.
Rob Cornish, Anthony L. Caterini, George Deligiannidis, and Arnaud Doucet · 2019
Closest in time.
Block neural autoregressive flow
Nicola De Cao, Ivan Titov, and Wilker Aziz · 2019
Closest in time.
Zhiwei Deng, Megha Nawhal, Lili Meng, and Greg Mori · 2019
Closest in time.
A RAD approach to deep mixture models
Laurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu, and Hugo Larochelle · 2019
Closest in time.
Augmented neural ODEs
Emilien Dupont, Arnaud Doucet, and Yee Whye Teh · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Generative image modeling using spatial LSTMs
Lucas Theis and Matthias Bethge · 2015
Cited alongside, same era.
Deep learning and the information bottleneck principle
Naftali Tishby and Noga Zaslavsky · 2015
Cited alongside, same era.
Normalizing flows on Riemannian manifolds
Mevlana C. Gemici, Danilo Jimenez Rezende, and Shakir Mohamed · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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
Cited alongside, same era.
Learning in implicit generative models
Shakir Mohamed and Balaji Lakshminarayanan · 2016
Cited alongside, same era.
Closest in time.
Reparameterizing distributions on Lie groups
Luca Falorsi, Pim de Haan, Tim R. Davidson, and Patrick Forré · 2019
Closest in time.
Improving normalizing flows via better orthogonal parameterizations
Adam Golinski, Mario Lezcano-Casado, and Tom Rainforth · 2019
Closest in time.
FFJORD: Free-form continuous dynamics for scalable reversible generative models
Will Grathwohl, Ricky T. Q. Chen, Jesse Betterncourt, Ilya Sutskever, and David K. Duvenaud · 2019
Closest in time.
Automatic posterior transformation for likelihood-free inference
David S. Greenberg, Marcel Nonnenmacher, and Jakob H. Macke · 2019
Closest in time.
MoGlow: Probabilistic and controllable motion synthesis using normalising flows
Gustav Eje Henter, Simon Alexanderson, and Jonas Beskow · 2019
Closest in time.
Flow++: Improving flow-based generative models with variational dequantization and architecture design
Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, and Pieter Abbeel · 2019
Closest in time.
NeuTra-lizing bad geometry in Hamiltonian Monte Carlo using neural transport
Matthew Hoffman, Pavel Sountsov, Joshua V. Dillon, Ian Langmore, Dustin Tran, and Srinivas Vasudevan · 2019
Closest in time.
Graph residual flow for molecular graph generation
Shion Honda, Hirotaka Akita, Katsuhiko Ishiguro, Toshiki Nakanishi, and Kenta Oono · 2019
Closest in time.
Excessive invariance causes adversarial vulnerability
Jörn-Henrik Jacobsen, Jens Behrmann, Richard Zemel, and Matthias Bethge · 2019
Closest in time.
Sum-of-squares polynomial flow
Priyank Jaini, Kira A. Selby, and Yaoliang Yu · 2019
Closest in time.
Unsupervised learning of PCFGs with normalizing flow
Lifeng Jin, Finale Doshi-Velez, Timothy Miller, Lane Schwartz, and William Schuler · 2019
Closest in time.
FloWaveNet : A generative flow for raw audio
Sungwon Kim, Sang-Gil Lee, Jongyoon Song, Jaehyeon Kim, and Sungroh Yoon · 2019
Closest in time.
Equivariant flows: Sampling configurations for multi-body systems with symmetric energies
Jonas Köhler, Leon Klein, and Frank Noé · 2019
Closest in time.
VideoFlow: A flow-based generative model for video
Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, and Diederik P. Kingma · 2019
Closest in time.
Cheap orthogonal constraints in neural networks: A simple parametrization of the orthogonal and unitary group
Mario Lezcano-Casado and David Martínez-Rubio · 2019
Closest in time.
MaCow: Masked convolutional generative flow
Xuezhe Ma, Xiang Kong, Shanghang Zhang, and Eduard Hovy · 2019
Closest in time.
GraphNVP: An invertible flow model for generating molecular graphs
Kaushalya Madhawa, Katushiko Ishiguro, Kosuke Nakago, and Motoki Abe · 2019
Closest in time.
Neural importance sampling
Thomas Müller, Brian McWilliams, Fabrice Rousselle, Markus Gross, and Jan Novák · 2019
Closest in time.
Hybrid models with deep and invertible features
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan · 2019
Closest in time.
Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Frank Noé, Simon Olsson, Jonas Köhler, and Hao Wu · 2019
Closest in time.
Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows
George Papamakarios, David Sterratt, and Iain Murray · 2019
Closest in time.
WaveGlow: A flow-based generative network for speech synthesis
Ryan Prenger, Rafael Valle, and Bryan Catanzaro · 2019
Closest in time.
Danilo Jimenez Rezende, Sébastien Racanière, Irina Higgins, and Peter Toth · 2019
Closest in time.
Generative predecessor models for sample-efficient imitation learning
Yannick Schroecker, Mel Vecerik, and Jon Scholz · 2019
Closest in time.
MintNet: Building invertible neural networks with masked convolutions
Yang Song, Chenlin Meng, and Stefano Ermon · 2019
Closest in time.
Discrete flows: Invertible generative models of discrete data
Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, and Ben Poole · 2019
Closest in time.
Riemannian normalizing flow on variational Wasserstein autoencoder for text modeling
Prince Zizhuang Wang and William Yang Wang · 2019
Closest in time.
Improving exploration in soft-actor-critic with normalizing flows policies
Patrick Nadeem Ward, Ariella Smofsky, and Avishek Joey Bose · 2019
Closest in time.
Unconstrained monotonic neural networks
Antoine Wehenkel and Gilles Louppe · 2019
Closest in time.
Learning likelihoods with conditional normalizing flows
Christina Winkler, Daniel E. Worrall, Emiel Hoogeboom, and Max Welling · 2019
Closest in time.
PointFlow: 3D point cloud generation with continuous normalizing flows
Guandao Yang, Xun Huang, Zekun Hao, Ming-Yu Liu, Serge J. Belongie, and Bharath Hariharan · 2019
Closest in time.
Density matching for bilingual word embedding
Chunting Zhou, Xuezhe Ma, Di Wang, and Graham Neubig · 2019
Closest in time.
Latent normalizing flows for discrete sequences
Zachary Ziegler and Alexander Rush · 2019
Closest in time.
The frontier of simulation-based inference
Kyle Cranmer, Johann Brehmer, and Gilles Louppe · 2020
Closest in time.
Invertible generative modeling using linear rational splines
Hadi M. Dolatabadi, Sarah Erfani, and Christopher Leckie · 2020
Closest in time.
Training deep neural density estimators to identify mechanistic models of neural dynamics
Pedro J. Gonçalves, Jan-Matthis Lueckmann, Michael Deistler, Marcel Nonnenmacher, Kaan Öcal, Giacomo Bassetto, Chaitanya Chintaluri, William F. Podlaski, Sara A. Haddad, Tim P. Vogels, David S. Greenberg, and Jakob H. Macke · 2020
Closest in time.
Normalizing flows: An introduction and review of current methods
Ivan Kobyzev, Simon Prince, and Marcus A. Brubaker · 2020
Closest in time.
IDF++: Analyzing and improving integer discrete flows for lossless compression
Rianne van den Berg, Alexey A. Gritsenko, Mostafa Dehghani, Casper Kaae Sønderby, and Tim Salimans · 2020
Closest in time.
Targeted free energy estimation via learned mappings
Peter Wirnsberger, Andrew J. Ballard, George Papamakarios, Stuart Abercrombie, Sébastien Racanière, Alexander Pritzel, Danilo Jimenez Rezende, and Charles Blundell · 2020
Closest in time.