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Deep generative models have achieved remarkable success in various data domains, including images, time series, and natural languages.
On the evolution of random graphs
P. Erdős and A Rényi · 1960
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David Weininger · 1988
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Emergence of scaling in random networks
Albert-Laszlo Barabasi and Reka Albert · 1999
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The graph neural network model
F. Scarselli, M. Gori, A.C. Tsoi, M. Hagenbuchner, and G. Monfardini · 2009
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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
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2013
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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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Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole von Lilienfeld · 2014
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Importance weighted autoencoders
Yuri Burda, Roger B. Grosse, and Ruslan Salakhutdinov · 2015
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Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alan Aspuru-Guzik, and Ryan P Adams · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Generative moment matching networks
Yujia Li, Kevin Swersky, and Richard Zemel · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Terpret: A probabilistic programming language for program induction
Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, and Daniel Tarlow · 2016
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Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, David K. Duvenaud, José Miguel Hernández-Lobato, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, and Alán Aspuru-Guzik · 2016
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Molecular generation with recurrent neural networks (rnns)
Esben Jannik Bjerrum and Richard Threlfall · 2017
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Neural message passing for quantum chemistry
J. Gilmer, S.S. Schoenholz, P.F. Riley, O. Vinyals, and G.E. Dahl · 2017
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Learning graphical state transitions
Daniel D. Johnson · 2017
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Grammar variational autoencoder
Matt J. Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Generating focussed molecule libraries for drug discovery with recurrent neural networks
Marwin H. S. Segler, Thierry Kogej, Christian Tyrchan, and Mark P. Waller · 2017
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Natasha Jaques, Shixiang Gu, Richard E. Turner, and Douglas Eck · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2016
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f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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Improved techniques for training gans
Tim Salimans, Ian J. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Generating videos with scene dynamics
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2016
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Towards principled methods for training generative adversarial networks
Martín Arjovsky and Léon Bottou · 2017
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Wasserstein generative adversarial networks
Martín Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Generalization and equilibrium in generative adversarial nets (GANs)
Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang · 2017
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Sahar Tavakoli, Alireza Hajibagheri, and Gita Sukthankar · 2017
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GraphGAN: Generating graphs via random walks
Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, and Stephan Günnemann · 2018
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Syntax-directed variational autoencoder for structured data
Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, and Le Song · 2018
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Xiaojie Guo, Lingfei Wu, and Liang Zhao · 2018
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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Learning deep generative models of graphs, 2018
Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, and Peter Battaglia · 2018
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GraphVAE: Towards generation of small graphs using variational autoencoders, 2018
Martin Simonovsky and Nikos Komodakis · 2018
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