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Graph Neural Networks (GNNs) are versatile, powerful machine learning methods that enable graph structure and feature representation learning, and have applications across many domains.
Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity
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Complexity of inference in graphical models
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David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert Mueller · 2009
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Understanding the difficulty of training deep feedforward neural networks
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Reverse engineering the neural networks for rule extraction in classification problems
M. Gethsiyal Augasta and T. Kathirvalavakumar · 2011
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Perturbation biology: inferring signaling networks in cellular systems
Evan J Molinelli, Anil Korkut, Weiqing Wang, Martin L Miller, Nicholas P Gauthier, Xiaohong Jing, Poorvi Kaushik, Qin He, Gordon Mills, David B Solit, et al · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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ℓ 1 \ell_{1} -regularized neural networks are improperly learnable in polynomial time, 2015
Yuchen Zhang, Jason D. Lee, and Michael I. Jordan · 2015
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Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding, 2015
Song Han, Huizi Mao, and William J. Dally · 2015
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Distilling the knowledge in a neural network, 2015
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Gated graph sequence neural networks
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Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Deepred - rule extraction from deep neural networks
Jan Ruben Zilke, Eneldo Loza Mencía, and Frederik Janssen · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
Relational inductive biases, deep learning, and graph networks, 2018
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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Graph embedding techniques, applications, and performance: A survey
Palash Goyal and Emilio Ferrara · 2018
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Sanity checks for saliency maps, 2018
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
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Amc: Automl for model compression and acceleration on mobile devices
Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, and Song Han · 2018
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This looks like that: Deep learning for interpretable image recognition, 2018
Chaofan Chen, Oscar Li, Chaofan Tao, Alina Jade Barnett, Jonathan Su, and Cynthia Rudin · 2018
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Representation learning on graphs: Methods and applications, 2017
William L. Hamilton, Rex Ying, and Jure Leskovec · 2017
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Interpretable & explorable approximations of black box models, 2017
Himabindu Lakkaraju, Ece Kamar, Rich Caruana, and Jure Leskovec · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Beyond sparsity: Tree regularization of deep models for interpretability, 2017
Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, and Finale Doshi-Velez · 2017
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Graph convolutional neural networks for web-scale recommender systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec · 2018
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Modeling polypharmacy side effects with graph convolutional networks
Marinka Zitnik, Monica Agrawal, and Jure Leskovec · 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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Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling · 2018
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Network-based prediction of protein interactions
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
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Gnnexplainer: Generating explanations for graph neural networks
Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec · 2019
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Graph neural networks with convolutional arma filters
Filippo Maria Bianchi, Daniele Grattarola, Cesare Alippi, and Lorenzo Livi · 2019
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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, et al · 2019
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Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
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Network medicine framework for identifying drug repurposing opportunities for covid-19, 2020
Deisy Morselli Gysi, Ítalo Do Valle, Marinka Zitnik, Asher Ameli, Xiao Gan, Onur Varol, Helia Sanchez, Rebecca Marlene Baron, Dina Ghiassian, Joseph Loscalzo, and Albert-László Barabási · 2020
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