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Deep learning on graphs has become a popular research topic with many applications.
On the evolution of random graphs
Erdos, Paul and Rényi, Alfréd · 1960
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A learning algorithm for continually running fully recurrent neural networks
Williams, Ronald J. and Zipser, David · 1989
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Estimation and prediction for stochastic blockmodels for graphs with latent block structure
Snijders, Tom A.B. and Nowicki, Krzysztof · 1997
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Emergence of scaling in random networks
Barabási, Albert-László and Albert, Réka · 1999
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Recognition of group activities using dynamic probabilistic networks
Gong, Shaogang and Xiang, Tao · 2003
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ZINC: A free tool to discover chemistry for biology
Irwin, John J., Sterling, Teague, Mysinger, Michael M., Bolstad, Erin S., and Coleman, Ryan G · 2012
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Auto-encoding variational bayes
Kingma, Diederik P. and Welling, Max · 2013
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Finding matches in a haystack: A max-pooling strategy for graph matching in the presence of outliers
Cho, Minsu, Sun, Jian, Duchenne, Olivier, and Ponce, Jean · 2014
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Practical graph isomorphism, II
McKay, Brendan D. and Piperno, Adolfo · 2014
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Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, Raghunathan, Dral, Pavlo O, Rupp, Matthias, and von Lilienfeld, O Anatole · 2014
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Seqgan: Sequence generative adversarial nets with policy gradient
Yu, Lantao, Zhang, Weinan, Wang, Jun, and Yu, Yong · 2014
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Scheduled sampling for sequence prediction with recurrent neural networks
Bengio, Samy, Vinyals, Oriol, Jaitly, Navdeep, and Shazeer, Noam · 2015
Cited alongside, same era.
Makhzani, Alireza, Shlens, Jonathon, Jaitly, Navdeep, and Goodfellow, Ian J · 2015
Cited alongside, same era.
Learning structured output representation using deep conditional generative models
Sohn, Kihyuk, Lee, Honglak, and Yan, Xinchen · 2015
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A note on the evaluation of generative models
Theis, Lucas, van den Oord, Aäron, and Bethge, Matthias · 2015
Cited alongside, same era.
Order matters: Sequence to sequence for sets
Vinyals, Oriol, Bengio, Samy, and Kudlur, Manjunath · 2015
Cited alongside, same era.
Geometric deep learning: going beyond euclidean data
Bronstein, Michael M, Bruna, Joan, LeCun, Yann, Szlam, Arthur, and Vandergheynst, Pierre · 2017
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Neural message passing for quantum chemistry
Gilmer, Justin, Schoenholz, Samuel S., Riley, Patrick F., Vinyals, Oriol, and Dahl, George E · 2017
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Chemnet: A transferable and generalizable deep neural network for small-molecule property prediction
Goh, Garrett B., Siegel, Charles, Vishnu, Abhinav, and Hodas, Nathan O · 2017
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Learning graphical state transitions
Johnson, Daniel D · 2017
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Distance metric learning using graph convolutional networks: Application to functional brain networks
Ktena, Sofia Ira, Parisot, Sarah, Ferrante, Enzo, Rajchl, Martin, Lee, Matthew C. H., Glocker, Ben, and Rueckert, Daniel · 2017
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Generating sentences from a continuous space
Bowman, Samuel R., Vilnis, Luke, Vinyals, Oriol, Dai, Andrew M., Józefowicz, Rafal, and Bengio, Samy · 2016
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Gpu-accelerated hungarian algorithms for the linear assignment problem
Date, Ketan and Nagi, Rakesh · 2016
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Automatic chemical design using a data-driven continuous representation of molecules
Gómez-Bombarelli, Rafael, Duvenaud, David K., Hernández-Lobato, José Miguel, Aguilera-Iparraguirre, Jorge, Hirzel, Timothy D., Adams, Ryan P., and Aspuru-Guzik, Alán · 2016
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Categorical reparameterization with gumbel-softmax
Jang, Eric, Gu, Shixiang, and Poole, Ben · 2016
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GANS for sequences of discrete elements with the gumbel-softmax distribution
Kusner, Matt J. and Hernández-Lobato, José Miguel · 2016
Cited alongside, same era.
End-to-end people detection in crowded scenes
Stewart, Russell, Andriluka, Mykhaylo, and Ng, Andrew Y · 2016
Cited alongside, same era.
RDKit: Open-source cheminformatics
Landrum, Greg
Cited in the paper.
Grammar variational autoencoder
Kusner, Matt J., Paige, Brooks, and Hernández-Lobato, José Miguel · 2017
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Molecular de novo design through deep reinforcement learning
Olivecrona, Marcus, Blaschke, Thomas, Engkvist, Ola, and Chen, Hongming · 2017
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Generating focussed molecule libraries for drug discovery with recurrent neural networks
Segler, Marwin H. S., Kogej, Thierry, Tyrchan, Christian, and Waller, Mark P · 2017
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Dynamic edge-conditioned filters in convolutional neural networks on graphs
Simonovsky, Martin and Komodakis, Nikos · 2017
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Scene graph generation by iterative message passing
Xu, Danfei, Zhu, Yuke, Choy, Christopher Bongsoo, and Fei-Fei, Li · 2017
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