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We introduce Graph Neural Processes (GNP), inspired by the recent work in conditional and latent neural processes.
Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity
Asim Kumar Debnath, Rosa L Lopez de Compadre, Gargi Debnath, Alan J Shusterman, and Corwin Hansch · 1991
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The predictive toxicology challenge 2000–2001
Christoph Helma, Ross D. King, Stefan Kramer, and Ashwin Srinivasan · 2001
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A new model for learning in graph domains
M. Gori, G. Monfardini, and F Scarselli · 2005
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Graph neural networks for ranking web pages
F. Scarselli, S. L. Yong, M. Gori, M. abd Hagenbuchner, A. C. Tsoi, and M. Maggini · 2005
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Derivation and validation of toxicophores for mutagenicity prediction
Jeroen Kazius, Ross McGuire, and Roberta Bursi · 2005
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The pharmacophore kernel for virtual screening with support vector machines
Pierre Mahé, Liva Ralaivola, Véronique Stoven, and Jean-Philippe Vert · 2006
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Bayesian neural networks for internet traffic classification
Tom Auld, Andrew W Moore, and Stephen F Gull · 2007
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Iam graph database repository for graph based pattern recognition and machine learning
Kaspar Riesen and Horst Bunke · 2008
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The discovery of structural form
Charles Kemp and Joshua B Tenenbaum · 2008
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Imputation of missing network data: Some simple procedures
Mark Huisman · 2009
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Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Tox21 data challenge, 2014
National Center for Advancing Translation Services · 2014
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Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
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Gated graph sequence neural networks
Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel · 2016
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Benchmark data sets for graph kernels, 2016
Kristian Kersting, Nils M. Kriege, Christopher Morris, Petra Mutzel, and Marion Neumann · 2016
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Interaction networks for learning about objects, relations and physics
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, et al · 2016
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A compositional object-based approach to learning physical dynamics
Michael B Chang, Tomer Ullman, Antonio Torralba, and Joshua B Tenenbaum · 2016
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Representation learning for visual-relational knowledge graphs
Daniel Oñoro-Rubio, Mathias Niepert, Alberto García-Durán, Roberto González, and Roberto J López-Sastre · 2017
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Knowledge transfer for out-of-knowledge-base entities: A graph neural network approach
Takuo Hamaguchi, Hidekazu Oiwa, Masashi Shimbo, and Yuji Matsumoto · 2017
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Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andrew Ballard, Justin Gilmer, George Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, and Razvan Pascanu · 2018
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Conditional neural processes
M Garnelo, D Rosenbaum, CJ Maddison, T Ramalho, D Saxton, M Shanahan, YW Teh, DJ Rezende, and SM. Eslami · 2018
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Sainbayar Sukhbaatar, Rob Fergus, et al · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Cited alongside, same era.
Geometric deep learning: going beyond euclidean data
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst · 2017
Cited alongside, same era.
Discovering objects and their relations from entangled scene representations
David Raposo, Adam Santoro, David Barrett, Razvan Pascanu, Timothy Lillicrap, and Peter Battaglia · 2017
Cited alongside, same era.
A simple neural network module for relational reasoning
Adam Santoro, David Raposo, David G Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, and Timothy Lillicrap · 2017
Cited alongside, same era.
Visual interaction networks: Learning a physics simulator from video
Nicholas Watters, Daniel Zoran, Theophane Weber, Peter Battaglia, Razvan Pascanu, and Andrea Tacchetti · 2017
Cited alongside, same era.
Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu · 2017
Cited alongside, same era.
Bayesian graph convolutional neural networks for semi-supervised classification
Yingxue Zhang, Soumyasundar Pal, Mark Coates, and Deniz Üstebay · 2018
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Relational neural expectation maximization: Unsupervised discovery of objects and their interactions
Sjoerd van Steenkiste, Michael Chang, Klaus Greff, and Jürgen Schmidhuber · 2018
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Graph networks as learnable physics engines for inference and control
Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, and Peter Battaglia · 2018
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Zhiyong Cui, Kristian Henrickson, Ruimin Ke, and Yinhai Wang · 2018
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Neural relational inference for interacting systems
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, and Richard Zemel · 2018
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Dynamic graph cnn for learning on point clouds
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon · 2018
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Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, and Maosong Sun · 2019
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Attentive neural processes
Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, and Yee Whye Teh · 2019
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