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In adversarial training, a set of models learn together by pursuing competing goals, usually defined on single data instances.
Theory of t-norms and fuzzy inference methods
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Adversarial Classification
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Freebase: A Shared Database of Structured General Human Knowledge
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BPR: Bayesian Personalized Ranking from Implicit Feedback
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Understanding the difficulty of training deep feedforward neural networks
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Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
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Factorizing YAGO: Scalable Machine Learning for Linked Data
M. Nickel, V. Tresp, and H.-P. Kriegel · 2012
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Translating Embeddings for Modeling Multi-relational Data
A. Bordes, N. Usunier, A. García-Durán, J. Weston, and O. Yakhnenko · 2013
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Reasoning With Neural Tensor Networks for Knowledge Base Completion
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Typed Tensor Decomposition of Knowledge Bases for Relation Extraction
K.-W. Chang, W.-t. Yih, B. Yang, and C. Meek · 2014
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Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion
X. Dong, E. Gabrilovich, G. Heitz, W. Horn, N. Lao, K. Murphy, T. Strohmann, S. Sun, and W. Zhang · 2014
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Generative Adversarial Nets
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, and Y. Bengio · 2014
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Reducing the Rank in Relational Factorization Models by Including Observable Patterns
M. Nickel, X. Jiang, and V. Tresp · 2014
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Low-Dimensional Embeddings of Logic
T. Rocktäschel, M. Bosnjak, S. Singh, and S. Riedel · 2014
Representing Text for Joint Embedding of Text and Knowledge Bases
K. Toutanova, D. Chen, P. Pantel, P. Choudhury, and M. Gamon · 2015
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Knowledge Base Completion Using Embeddings and Rules
Q. Wang, B. Wang, and L. Guo · 2015
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Embedding Entities and Relations for Learning and Inference in Knowledge Bases
B. Yang, W. Yih, X. He, J. Gao, and L. Deng · 2015
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Lifted rule injection for relation embeddings
T. Demeester, T. Rocktäschel, and S. Riedel · 2016
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Jointly Embedding Knowledge Graphs and Logical Rules
S. Guo, Q. Wang, L. Wang, B. Wang, and L. Guo · 2016
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A review of relational machine learning for knowledge graphs
M. Nickel, K. Murphy, V. Tresp, and E. Gabrilovich · 2016
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus · 2014
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Learning to generate chairs with convolutional neural networks
A. Dosovitskiy, J. Tobias Springenberg, and T. Brox · 2015
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Compositional Vector Space Models for Knowledge Base Completion
A. Neelakantan, B. Roth, and A. McCallum · 2015
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Injecting Logical Background Knowledge into Embeddings for Relation Extraction
T. Rocktäschel, S. Singh, and S. Riedel · 2015
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Relation Extraction with Matrix Factorization and Universal Schemas
S. Riedel, L. Yao, A. McCallum, and B. M. Marlin
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Relation Extraction with Matrix Factorization and Universal Schemas
S. Riedel, L. Yao, A. McCallum, and B. M. Marlin
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Discriminative gaifman models
M. Niepert · 2016
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Complex Embeddings for Simple Link Prediction
T. Trouillon, J. Welbl, S. Riedel, É. Gaussier, and G. Bouchard · 2016
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Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learning
E. Vylomova, L. Rimell, T. Cohn, and T. Baldwin · 2016
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Learning First-Order Logic Embeddings via Matrix Factorization
W. Y. Wang and W. W. Cohen · 2016
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