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The capability of making interpretable and self-explanatory decisions is essential for developing responsible machine learning systems.
Inductive logic programming
Nada Lavrac and Saso Dzeroski · 1994
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Ni Lao and William W Cohen · 2010
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Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko · 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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Fast rule mining in ontological knowledge bases with amie+
Luis Galárraga, Christina Teflioudi, Katja Hose, and Fabian M Suchanek · 2015
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Efficient and expressive knowledge base completion using subgraph feature extraction
Matt Gardner and Tom Mitchell · 2015
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Traversing knowledge graphs in vector space
Kelvin Guu, John Miller, and Percy Liang · 2015
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Yankai Lin, Zhiyuan Liu, Huanbo Luan, Maosong Sun, Siwei Rao, and Song Liu · 2015
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Rowan Zellers, Mark Yatskar, Sam Thomson, and Yejin Choi · 2015
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Rajarshi Das, Arvind Neelakantan, David Belanger, and Andrew McCallum · 2016
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Visual genome: Connecting language and vision using crowdsourced dense image annotations
Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, Michael Bernstein, and Li Fei-Fei · 2016
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The mythos of model interpretability
Zachary C Lipton · 2016
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Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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William W Cohen, Fan Yang, and Kathryn Rivard Mazaitis · 2017
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Tim Rocktäschel and Sebastian Riedel · 2017
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Scalable rule learning via learning representation
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Tucker: Tensor factorization for knowledge graph completion
Ivana Balažević, Carl Allen, and Timothy M Hospedales · 2019
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