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Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data.
Holographic embeddings of knowledge graphs
Maximilian Nickel, Lorenzo Rosasco, Tomaso A Poggio, et al. 2016 · 1961
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Yago: a core of semantic knowledge
Fabian M Suchanek, Gjergji Kasneci, and Gerhard Weikum. 2007 · 2007
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Learning structured embeddings of knowledge bases
Antoine Bordes, Jason Weston, Ronan Collobert, Yoshua Bengio, et al. 2011 · 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 · 2011
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Predicting human preferences using the block structure of complex social networks
Roger Guimerà, Alejandro Llorente, Esteban Moro, and Marta Sales-Pardo. 2012 · 2012
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Factorizing yago: scalable machine learning for linked data
Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. 2012 · 2012
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Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013 · 2013
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Hidden factors and hidden topics: understanding rating dimensions with review text
Julian McAuley and Jure Leskovec. 2013 · 2013
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Relation extraction with matrix factorization and universal schemas
Sebastian Riedel, Limin Yao, Andrew McCallum, and Benjamin M Marlin. 2013 · 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 · 2013
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Deep convolutional neural networks for sentiment analysis of short texts
Cícero Nogueira Dos Santos and Maira Gatti. 2014 · 2014
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Knowledge graph embedding by translating on hyperplanes
Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014 · 2014
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Deep visual-semantic alignments for generating image descriptions
Andrej Karpathy and Li Fei-Fei. 2015 · 2015
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Dbpedia–a large-scale, multilingual knowledge base extracted from wikipedia
Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick Van Kleef, Sören Auer, et al. 2015 · 2015
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Age and gender classification using convolutional neural networks
Gil Levi and Tal Hassner. 2015 · 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 · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman. 2015 · 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 · 2015
Cited alongside, same era.
Embedding entities and relations for learning and inference in knowledge bases
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2015 · 2015
Cited alongside, same era.
Aligning knowledge and text embeddings by entity descriptions
Huaping Zhong, Jianwen Zhang, Zhen Wang, Hai Wan, and Zheng Chen. 2015 · 2015
Cited alongside, same era.
Capturing semantic similarity for entity linking with convolutional neural networks
Kblrn: End-to-end learning of knowledge base representations with latent, relational, and numerical features
Alberto Garcia-Duran and Mathias Niepert. 2017 · 2017
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Long text generation via adversarial training with leaked information
Jiaxian Guo, Sidi Lu, Han Cai, Weinan Zhang, Yong Yu, and Jun Wang. 2017 · 2017
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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. 2017 · 2017
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Self-normalizing neural networks
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter. 2017 · 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 · 2017
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Matthew Francis-Landau, Greg Durrett, and Dan Klein. 2016 · 2016
Cited alongside, same era.
Compact bilinear pooling
Yang Gao, Oscar Beijbom, Ning Zhang, and Trevor Darrell. 2016 · 2016
Cited alongside, same era.
The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan. 2016 · 2016
Cited alongside, same era.
Variational graph auto-encoders
Thomas N Kipf and Max Welling. 2016 · 2016
Cited alongside, same era.
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 · 2016
Cited alongside, same era.
Multilingual relation extraction using compositional universal schema
Patrick Verga, David Belanger, Emma Strubell, Benjamin Roth, and Andrew McCallum. 2016 · 2016
Cited alongside, same era.
Planet-photo geolocation with convolutional neural networks
Tobias Weyand, Ilya Kostrikov, and James Philbin. 2016 · 2016
Cited alongside, same era.
Adversarial generation of natural language
Sai Rajeswar, Sandeep Subramanian, Francis Dutil, Christopher Pal, and Aaron Courville. 2017 · 2017
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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. 2017 · 2017
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Cane: Context-aware network embedding for relation modeling
Cunchao Tu, Han Liu, Zhiyuan Liu, and Maosong Sun. 2017 · 2017
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Image-embodied knowledge representation learning
Ruobing Xie, Zhiyuan Liu, Tat-seng Chua, Huanbo Luan, and Maosong Sun. 2017 · 2017
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Seqgan: Sequence generative adversarial nets with policy gradient
Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu. 2017 · 2017
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Adversarially regularized autoencoders
Junbo Jake Zhao, Yoon Kim, Kelly Zhang, Alexander M Rush, Yann LeCun, et al. 2017 · 2017
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Convolutional 2d knowledge graph embeddings
Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. 2018 · 2018
Closest in time.
A multimodal translation-based approach for knowledge graph representation learning
Hatem Mousselly Sergieh, Teresa Botschen, Iryna Gurevych, and Stefan Roth. 2018 · 2018
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Complex embeddings for simple link prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. 2016 · 2080
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