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Deep learning on graph structures has shown exciting results in various applications.
The graph neural network model
Scarselli, Franco, Gori, Marco, Tsoi, Ah Chung, Hagenbuchner, Markus, and Monfardini, Gabriele · 2009
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, Christian, Zaremba, Wojciech, Sutskever, Ilya, Bruna, Joan, Erhan, Dumitru, Goodfellow, Ian, and Fergus, Rob · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, Ian J, Shlens, Jonathon, and Szegedy, Christian · 2014
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
Earlier work this paper cites.
Graph based anomaly detection and description: a survey
Akoglu, Leman, Tong, Hanghang, and Koutra, Danai · 2015
Earlier work this paper cites.
Convolutional networks on graphs for learning molecular fingerprints
Duvenaud, David K, Maclaurin, Dougal, Iparraguirre, Jorge, Bombarell, Rafael, Hirzel, Timothy, Aspuru-Guzik, Alán, and Adams, Ryan P · 2015
Earlier work this paper cites.
Gated graph sequence neural networks
Li, Yujia, Tarlow, Daniel, Brockschmidt, Marc, and Zemel, Richard · 2015
Earlier work this paper cites.
Neural combinatorial optimization with reinforcement learning
Bello, Irwan, Pham, Hieu, Le, Quoc V, Norouzi, Mohammad, and Bengio, Samy · 2016
Earlier work this paper cites.
Discriminative embeddings of latent variable models for structured data
Dai, Hanjun, Dai, Bo, and Song, Le · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Kipf, Thomas N and Welling, Max · 2016
Cited alongside, same era.
Deepfool: a simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, Seyed-Mohsen, Fawzi, Alhussein, and Frossard, Pascal · 2016
Cited alongside, same era.
Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Chen, Pin-Yu, Zhang, Huan, Sharma, Yash, Yi, Jinfeng, and Hsieh, Cho-Jui · 2017
Cited alongside, same era.
Learning combinatorial optimization algorithms over graphs
Dai, Hanjun, Khalil, Elias B, Zhang, Yuyu, Dilkina, Bistra, and Song, Le · 2017
Cited alongside, same era.
Deriving neural architectures from sequence and graph kernels
Lei, Tao, Jin, Wengong, Barzilay, Regina, and Jaakkola, Tommi · 2017
Later among the works it cites.
Miikkulainen, Risto, Liang, Jason, Meyerson, Elliot, Rawal, Aditya, Fink, Dan, Francon, Olivier, Raju, Bala, Navruzyan, Arshak, Duffy, Nigel, and Hodjat, Babak · 2017
Later among the works it cites.
Practical black-box attacks against machine learning
Papernot, Nicolas, McDaniel, Patrick, Goodfellow, Ian, Jha, Somesh, Celik, Z Berkay, and Swami, Ananthram · 2017
Later among the works it cites.
Large-scale evolution of image classifiers
Real, Esteban, Moore, Sherry, Selle, Andrew, Saxena, Saurabh, Suematsu, Yutaka Leon, Le, Quoc, and Kurakin, Alex · 2017
Later among the works it cites.
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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
Cited alongside, same era.
Inductive representation learning on large graphs
Hamilton, William L, Ying, Rex, and Leskovec, Jure · 2017
Cited alongside, same era.
Adversarial examples for evaluating reading comprehension systems
Jia, Robin and Liang, Percy · 2017
Cited alongside, same era.
Su, Jiawei, Vargas, Danilo Vasconcellos, and Kouichi, Sakurai · 2017
Later among the works it cites.
Know-evolve: Deep temporal reasoning for dynamic knowledge graphs
Trivedi, Rakshit, Dai, Hanjun, Wang, Yichen, and Song, Le · 2017
Later among the works it cites.
Thermometer encoding: One hot way to resist adversarial examples
Buckman, Jacob, Roy, Aurko, Raffel, Colin, and Goodfellow, Ian · 2018
Closest in time.
Adversarial attacks on neural networks for graph data
Zügner, Daniel, Akbarnejad, Amir, and Günnemann, Stephan · 2018
Closest in time.