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Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data.
An efficient explanation of individual classifications using game theory
Igor Kononenko et al · 2010
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Learning structured embeddings of knowledge bases
Antoine Bordes, Jason Weston, Ronan Collobert, Yoshua Bengio, et al · 2011
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A three-way model for collective learning on multi-relational data
Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel · 2011
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Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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Adversarial attacks against intrusion detection systems: Taxonomy, solutions and open issues
Igino Corona, Giorgio Giacinto, and Fabio Roli · 2013
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Reasoning with neural tensor networks for knowledge base completion
Richard Socher, Danqi Chen, Christopher D Manning, and Andrew Ng · 2013
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Security evaluation of pattern classifiers under attack
Battista Biggio, Giorgio Fumera, and Fabio Roli · 2014
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Asymmetric lsh (alsh) for sublinear time maximum inner product search (mips)
Anshumali Shrivastava and Ping Li · 2014
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Knowledge graph embedding by translating on hyperplanes
Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen · 2014
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Knowledge graph embedding via dynamic mapping matrix
Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, and Jun Zhao · 2015
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Learning entity and relation embeddings for knowledge graph completion
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu · 2015
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Representing text for joint embedding of text and knowledge bases
Kristina Toutanova, Danqi Chen, Patrick Pantel, Hoifung Poon, Pallavi Choudhury, and Michael Gamon · 2015
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Embedding entities and relations for learning and inference in knowledge bases
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng · 2015
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Stranse: a novel embedding model of entities and relationships in knowledge bases
Dat Quoc Nguyen, Kairit Sirts, Lizhen Qu, and Mark Johnson · 2016
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The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami · 2016
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Compositional learning of embeddings for relation paths in knowledge base and text
Kristina Toutanova, Victoria Lin, Wen-tau Yih, Hoifung Poon, and Chris Quirk · 2016
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Complex embeddings for simple link prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard · 2016
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Towards interpretable deep neural networks by leveraging adversarial examples
Yinpeng Dong, Hang Su, Jun Zhu, and Fan Bao · 2017
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Understanding the origins of bias in word embeddings
Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, and Richard Zemel · 2018
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Kbgan: Adversarial learning for knowledge graph embeddings
Liwei Cai and William Yang Wang · 2018
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Adversarial attack on graph structured data
Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song · 2018
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Hyte: Hyperplane-based temporally aware knowledge graph embedding
Shib Sankar Dasgupta, Swayambhu Nath Ray, and Partha Talukdar · 2018
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Convolutional 2d knowledge graph embeddings
Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel · 2018
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Deep learning for chemical reaction prediction
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Alberto Garcia-Duran and Mathias Niepert · 2017
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Knowledge base completion: Baselines strike back
Rudolf Kadlec, Ondrej Bajgar, and Jan Kleindienst · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Adversarial sets for regularising neural link predictors
P Minervini, T Demeester, T Rocktäschel, and S Riedel · 2017
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Representation learning for visual-relational knowledge graphs
Daniel Oñoro-Rubio, Mathias Niepert, Alberto García-Durán, Roberto González-Sánchez, and Roberto J López-Sastre · 2017
Cited alongside, same era.
Cane: Context-aware network embedding for relation modeling
Cunchao Tu, Han Liu, Zhiyuan Liu, and Maosong Sun · 2017
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Knowledge graph embedding: A survey of approaches and applications
Quan Wang, Zhendong Mao, Bin Wang, and Li Guo · 2017
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David Fooshee, Aaron Mood, Eugene Gutman, Mohammadamin Tavakoli, Gregor Urban, Frances Liu, Nancy Huynh, David Van Vranken, and Pierre Baldi · 2018
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Graph embedding techniques, applications, and performance: A survey
Palash Goyal and Emilio Ferrara · 2018
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A novel embedding model for knowledge base completion based on convolutional neural network
Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, and Dinh Phung · 2018
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Embedding multimodal relational data for knowledge base completion
Pouya Pezeshkpour, Liyan Chen, and Sameer Singh · 2018
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Towards understanding the geometry of knowledge graph embeddings
Aditya Sharma, Partha Talukdar, et al · 2018
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Knowledge graph embedding with hierarchical relation structure
Zhao Zhang, Fuzhen Zhuang, Meng Qu, Fen Lin, and Qing He · 2018
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Adversarial attacks on neural networks for graph data
Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann · 2018
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