检索结果-内蒙古大学图书馆

arXiv 2018年

作者： Chen, Wang Gao, Yifan Zhang, Jiani King, Irwin Lyu, Michael R. Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong Shenzhen Key Laboratory of Rich Media Big Data Analytics and Application Shenzhen Research Institute Chinese University of Hong Kong Shenzhen China

keyphrase generation (KG) aims to generate a set of keyphrases given a document, which is a fundamental task in natural language processing (NLP). Most previous methods solve this problem in an extractive manner, while recently, several attempts are made under the generative setting using deep neural networks. However, the state-of-the-art generative methods simply treat the document title and the document main body equally, ignoring the leading role of the title to the overall document. To solve this problem, we introduce a new model called Title-Guided Network (TG-Net) for automatic keyphrase generation task based on the encoderdecoder architecture with two new features: (i) the title is additionally employed as a query-like input, and (ii) a titleguided encoder gathers the relevant information from the title to each word in the document. Experiments on a range of KG datasets demonstrate that our model outperforms the state-of-the-art models with a large margin, especially for documents with either very low or very high title length ratios. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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FEDLAB: A FLEXIBLE FEDERATED LEARNING FRAMEWORK

arXiv

引用

arXiv 2021年

作者： Zeng, Dun Liang, Siqi Hu, Xiangjing Wang, Hui Xu, Zenglin School of Computer Science and Engineering University of Electronic Science and Technology of China China Rich Media Big Data Analytics and Application Key Laboratory Shenzhen Research Institute The Chinese University of Hong Kong Hong Kong School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory Shenzhen China

Federated learning (FL) is a machine learning field in which researchers try to facilitate model learning process among multiparty without violating privacy protection regulations. Considerable effort has been invested in FL optimization and communication related researches. In this work, we introduce FedLab, a lightweight open-source framework for FL simulation. The design of FedLab focuses on FL algorithm effectiveness and communication efficiency. Also, FedLab is scalable in different deployment scenario. We hope FedLab could provide flexible API as well as reliable baseline implementations, and relieve the burden of implementing novel approaches for researchers in FL community. The source code is available at https://***/SMILELab-FL/FedLab. © 2021, CC BY.

关键词： Learning systems

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