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Machine Learning has been the quintessential solution for many AI problems, but learning is still heavily dependent on the specific training data.
Holographic embeddings of knowledge graphs
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Freebase: a collaboratively created graph database for structuring human knowledge
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Natural language inference
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Deepdive: Web-scale knowledge-base construction using statistical learning and inference
Feng Niu, Ce Zhang, Christopher Ré, and Jude W Shavlik. 2012 · 2012
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Translating embeddings for modeling multi-relational data
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Efficient estimation of word representations in vector space
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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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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
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Transition-based knowledge graph embedding with relational mapping properties
Miao Fan, Qiang Zhou, Emily Chang, and Thomas Fang Zheng. 2014 · 2014
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Convolutional neural networks for sentence classification
Yoon Kim. 2014 · 2014
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Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D Manning. 2015 · 2015
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Never ending learning
Tom M Mitchell, William W Cohen, Estevam R Hruschka Jr, Partha Pratim Talukdar, Justin Betteridge, Andrew Carlson, Bhavana Dalvi Mishra, Matthew Gardner, Bryan Kisiel, Jayant Krishnamurthy, et al. 2015 · 2015
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Aligning knowledge and text embeddings by entity descriptions
Huaping Zhong, Jianwen Zhang, Zhen Wang, Hai Wan, and Zheng Chen. 2015 · 2015
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The more you know: Using knowledge graphs for image classification
Kenneth Marino, Ruslan Salakhutdinov, and Abhinav Gupta. 2016 · 2016
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A survey of inductive biases for factorial representation-learning
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A review of relational machine learning for knowledge graphs
Maximilian Nickel, Kevin Murphy, Volker Tresp, and Evgeniy Gabrilovich. 2016a
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Machine learning with world knowledge: The position and survey
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