Results 21 to 30 of about 574,210 (309)
, 2021 Recent studies have been demonstrated that the excessive inflammatory response is an important factor of death in COVID-19 patients. In this study, we proposed a network representation learning-based methodology, termed AIdrug2cov, to discover drug ...
Xiaoqi W +7 more
europepmc +2 more sources
, 2020 Abstract Network representation learning aims to embed the vertexes in a network into low-dimensional dense representations, in which similar vertices in the network should have “close” representations (usually measured by cosine similarity or Euclidean distance of their representations). The representations can be used as the feature
Zhiyuan Liu, Yankai Lin, Maosong Sun
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Extending a network-of-elaborations representation to polyphonic music: Schenker and species counterpoint. [PDF]
, 2004 A system of representing melodies as a network of elaborations has been developed, and used as the basis for software which generates melodies in response to the movements of a dancer.
Marsden, Alan, Alan Marsden
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Attributed Bipartite Network Representation Learning
Jisuanji kexue yu tansuo, 2021 Existing network embedding models are mostly designed for homogeneous networks or heterogeneous networks, but ignore the special features of bipartite network which arise in recommender systems, search engines, question answering systems and so on ...
ZHAO Xueli, LU Guangyue, LV Shaoqing, ZHANG Pan
doaj +1 more source
Latent Representation Prediction Networks [PDF]
International Journal of Pattern Recognition and Artificial Intelligence, 2021 Modern model-based reinforcement learning methods for high-dimensional inputs often incorporate an unsupervised learning step for dimensionality reduction. The training objective of these unsupervised learning methods often leverages only static inputs such as reconstructing observations. These representations are combined with predictor functions for
Hlynur Davíð Hlynsson +4 more
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Active discriminative network representation learning [PDF]
, 2018 © 2018 International Joint Conferences on Artificial Intelligence. All right reserved. Most of current network representation models are learned in unsupervised fashions, which usually lack the capability of discrimination when applied to network ...
Zhou, C +17 more
core +1 more source
IEEE Access, 2019 With an increasing number of scholarly publications, accessing and retrieving appropriate papers is becoming an essential task for researchers. Citation recommendation, which can automatically provide a reference list based on a text segment, can ...
Libin Yang +3 more
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Bibliographic Network Representation Based Personalized Citation Recommendation
IEEE Access, 2019 With the increasing number of scientific papers, researchers find it more and more difficult to obtain relevant and appropriate papers to cite. Citation recommendation aims to overcome this problem by providing a reference paper list for a given ...
Xiaoyan Cai +4 more
doaj +1 more source
A Hybrid Deep Network Representation Model for Detecting Researchers’ Communities [PDF]
Journal of Artificial Intelligence and Data Mining, 2022 Recently, network representation has attracted many research works mostly concentrating on representing of nodes in a dense low-dimensional vector. There exist some network embedding methods focusing only on the node structure and some others considering
A. Torkaman +4 more
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AttrHIN: Network Representation Learning Method for Heterogeneous Information Network
IEEE Access, 2021 Network representation learning can map complex network to the low dimensional vector space, capture the topological properties of networks, and reduce the time complexity and space complexity of the algorithm.
Qingbiao Zhou, Chen Wang, Qi Li
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