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Normalizing flows are deep generative models that allow efficient likelihood calculation and sampling.
Emerging convolutions for generative normalizing flows
Emiel Hoogeboom, Rianne van den Berg, and Max Welling · 1901
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Integer discrete flows and lossless compression
Emiel Hoogeboom, Jorn WT Peters, Rianne van den Berg, and Max Welling · 1905
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Inverting modified matrices
Max A Woodbury · 1950
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Generating sequences with recurrent neural networks
Alex Graves · 2013
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Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2013
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NICE: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Made: Masked autoencoder for distribution estimation
Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle · 2015
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Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Learning structured output representation using deep conditional generative models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao · 2015
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Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
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Density estimation using real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 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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Chin-Wei Huang, David Krueger, Alexandre Lacoste, and Aaron Courville · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
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Residual flows for invertible generative modeling
Tian Qi Chen, Jens Behrmann, David K Duvenaud, and Jörn-Henrik Jacobsen · 2019
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Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 2019
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Invertible convolutional networks
Marc Finz, Pavel Izmailov, Wesley Maddox, Polina Kirichenko, and Andrew Gordon Wilson · 2019
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Masked autoregressive flow for density estimation
George Papamakarios, Theo Pavlakou, and Iain Murray · 2017
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Seqgan: Sequence generative adversarial nets with policy gradient
Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Jens Behrmann, Will Grathwohl, Ricky TQ Chen, David Duvenaud, and Jörn-Henrik Jacobsen · 2018
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Sylvester normalizing flows for variational inference
Rianne van den Berg, Leonard Hasenclever, Jakub M Tomczak, and Max Welling · 2018
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Neural ordinary differential equations
Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, and Pieter Abbeel · 2019
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Invertible convolutional flow
Mahdi Karami, Dale Schuurmans, Jascha Sohl-Dickstein, Laurent Dinh, and Daniel Duckworth · 2019
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Macow: Masked convolutional generative flow
Xuezhe Ma, Xiang Kong, Shanghang Zhang, and Eduard Hovy · 2019
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Neural importance sampling
Thomas Müller, Brian McWilliams, Fabrice Rousselle, Markus Gross, and Jan Novák · 2019
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Mintnet: Building invertible neural networks with masked convolutions
Yang Song, Chenlin Meng, and Stefano Ermon · 2019
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Discrete flows: Invertible generative models of discrete data
Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, and Ben Poole · 2019
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Latent normalizing flows for discrete sequences
Zachary M Ziegler and Alexander M Rush · 2019
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