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Despite their popularity, to date, the application of normalizing flows on categorical data stays limited.
GraphNVP: An Invertible Flow Model for Generating Molecular Graphs
Kaushalya Madhawa, Katushiko Ishiguro, Kosuke Nakago, and Motoki Abe. 2019 · 1905
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Normalizing Flows: Introduction and Ideas
Ivan Kobyzev, Simon Prince, and Marcus A. Brubaker. 2019 · 1908
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Dropout: A Simple Way to Prevent Neural Networks from Overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
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Graph theory with applications , volume 290
John Adrian Bondy, Uppaluri Siva Ramachandra Murty, and others. 1976 · 1976
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The Penn Treebank: Annotating Predicate Argument Structure
Mitch Marcus, Grace Kim, Mary Ann Marcinkiewicz, Robert MacIntyre, Ann Bies, Mark Ferguson, Karen Katz, and Britta Schasberger. 1994 · 1994
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Learning Discrete Distributions by Dequantization
Emiel Hoogeboom, Taco S. Cohen, and Jakub M. Tomczak. 2020 · 2001
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Density estimation by dual ascent of the log-likelihood
Esteban Tabak and Eric Vanden Eijnden. 2010 · 2010
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Large text compression benchmark
Matt Mahoney. 2011 · 2011
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ZINC: A Free Tool to Discover Chemistry for Biology
John J Irwin, Teague Sterling, Michael M Mysinger, Erin S Bolstad, and Ryan G Coleman. 2012 · 2012
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Subword language modeling with neural networks
Tomáš Mikolov, Ilya Sutskever, Anoop Deoras, Hai-Son Le, Stefan Kombrink, and Jan Cernocky. 2012 · 2012
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RNADE: The real-valued neural autoregressive density-estimator
Benigno Uria, Iain Murray, and Hugo Larochellehugo. 2013 · 2013
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Auto-Encoding Variational Bayes
Diederik P Kingma and Max Welling. 2014 · 2014
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Glove: Global Vectors for Word Representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Variational Inference with Normalizing Flows
Danilo Jimenez Rezende and Shakir Mohamed. 2015 · 2015
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Importance weighted autoencoders
Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov. 2016 · 2016
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Gaussian Error Linear Units (GELUs)
Dan Hendrycks and Kevin Gimpel. 2016 · 2016
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Improved variational inference with inverse autoregressive flow
Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling. 2016 · 2016
Cited alongside, same era.
A note on the evaluation of generative models
Lucas Theis, Aäron Van Den Oord, and Matthias Bethge. 2016 · 2016
Cited alongside, same era.
Density estimation using Real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. 2017 · 2017
Cited alongside, same era.
Pointer Sentinel Mixture Models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2017 · 2017
Cited alongside, same era.
Improving Variational Auto-Encoders using Householder Flow
Jakub M. Tomczak and Max Welling. 2017 · 2017
Cited alongside, same era.
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
Jiaxuan You, Rex Ying, Xiang Ren, William Hamilton, and Jure Leskovec. 2018 · 2018
Later among the works it cites.
Graph Neural Networks: A Review of Methods and Applications
Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2018 · 2018
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UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science
Dheeru Dua and Casey Graff. 2019 · 2019
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Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, and Pieter Abbeel. 2019 · 2019
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Attention is All you Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tbmmi Jaakkola. 2018 · 2018
Cited alongside, same era.
Glow: Generative Flow with Invertible 1x1 Convolutions
Diederik P. Kingma and Prafulla Dhariwal. 2018 · 2018
Cited alongside, same era.
Learning deep generative models of graphs
Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, and Peter Battaglia. 2018 · 2018
Cited alongside, same era.
Constrained Graph Variational Autoencoders for Molecule Design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, and Alexander Gaunt. 2018 · 2018
Cited alongside, same era.
Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders
Tengfei Ma, Jie Chen, and Cao Xiao. 2018 · 2018
Cited alongside, same era.
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Daniil Polykovskiy, Alexander Zhebrak, Benjamin Sanchez-Lengeling, Sergey Golovanov, Oktai Tatanov, Stanislav Belyaev, Rauf Kurbanov, Aleksey Artamonov, Vladimir Aladinskiy, Mark Veselov, Artur Kadurin, Simon Johansson, Hongming Chen, Sergey Nikolenko, Alan Aspuru-Guzik, and Alex Zhavoronkov. 2018 · 2018
Cited alongside, same era.
Emiel Hoogeboom, Jorn W. T. Peters, Rianne van den Berg, and Max Welling. 2019 · 2019
Later among the works it cites.
FloWaveNet : A Generative Flow for Raw Audio
Sungwon Kim, Sang-Gil Lee, Jongyoon Song, Jaehyeon Kim, and Sungroh Yoon. 2019 · 2019
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Graph colouring meets deep learning: Effective graph neural network models for combinatorial problems
Henrique Lemos, Marcelo Prates, Pedro Avelar, and Luis Lamb. 2019 · 2019
Later among the works it cites.
Efficient Graph Generation with Graph Recurrent Attention Networks
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Will Hamilton, David K Duvenaud, Raquel Urtasun, and Richard Zemel. 2019 · 2019
Later among the works it cites.
Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, and Kevin Swersky. 2019 · 2019
Later among the works it cites.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
Later among the works it cites.
Waveglow: A flow-based generative network for speech synthesis
Ryan Prenger, Rafael Valle, and Bryan Catanzaro. 2019 · 2019
Later among the works it cites.
Semi-supervised User Geolocation via Graph Convolutional Networks
Afshin Rahimi, Trevor Cohn, and Timothy Baldwin. 2018 · 2019
Later among the works it cites.
Discrete Flows: Invertible Generative Models of Discrete Data
Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, and Ben Poole. 2019 · 2019
Later among the works it cites.
Latent Normalizing Flows for Discrete Sequences
Zachary M. Ziegler and Alexander M. Rush. 2019 · 2019
Later among the works it cites.
On the Variance of the Adaptive Learning Rate and Beyond
Liyuan Liu, Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Jiawei Han. 2020 · 2020
Closest in time.
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian Tang. 2020 · 2020
Closest in time.