EMNLP 2018, Association for Computational Linguistics, 4 novembre 2018
We propose a novel strategy to encode the syntax parse tree of sentence into a learnable distributed representation. The proposed syntax encoding scheme is...
Proceedings of The Web Conference 2020, ACM, 20 avril 2020
Entity linking, which maps named entity mentions in a document into the proper entities in a given knowledge graph, has been shown to be able to significantly...
AAAI 2020: 34th AAAI Conference on Artificial Intelligence, AAAI press, 7 février 2020
We consider the problem of learning a neural network classifier. Under the information bottleneck (IB) principle, we associate with this classification problem...
32nd International Conference on Machine Learning(ICML 2015), International Machine Learning Society, mars 2016, Volume : 2
The chain-structured long short-term memory (LSTM) has showed to be effective in a wide range of problems such as speech recognition and machine translation....
Short-term energy load forecasting, such as hourly predictions for the next n (n ≥ 2) hours, will benefit from exploiting the relationships among the n...
Proceedings of The World Wide Web Conference WWW 2019, Association for Computing Machinery, mai 2019
Knowledge graphs such as DBPedia and Freebase contain sparse linkage connectivity, which poses severe challenge to link prediction between entities. In...
2014 IEEE International Conference on Big Data (Big Data), IEEE, 27 octobre 2014
Multi-instance (MI) learning is different than standard propositional classification, as it uses a set of bags containing many instances as input. While the...
Energy consumption estimation for building energy management systems (BEMS) is one of the key factors in the success of energy saving measures in modern...
Proceedings of New Frontiers in Mining Complex Patterns (NFMCP 2012), septembre 2012
Consider a scenario where one aims to learn models from dynamic and evolving data being characterized by very large fluctuations that are neither attributable...
Consider a scenario where one aims to learn models from data being characterized by very large fluctuations that are neither attributable to noise nor...
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