Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Minglong Lei"'
Publikováno v:
IEEE Access, Vol 7, Pp 17195-17206 (2019)
Unsupervised feature learning via auto-encoders results in low-dimensional representations in latent space that capture the patterns of input data. The auto-encoders with robust regularization learn qualified features that are less sensitive to small
Externí odkaz:
https://doaj.org/article/4fca71641f5e4a2e8d7469da5725cf5e
Publikováno v:
IEEE Transactions on Network Science and Engineering. 10:1684-1695
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a87a7967299816a5cfb78d9478cbd7b1
https://doi.org/10.1109/tnse.2022.3233479
https://doi.org/10.1109/tnse.2022.3233479
Publikováno v:
Neural Networks. 154:413-424
Graph, as a powerful data structure, has shown superior capability on modeling complex systems. Since real-world objects and their interactions are often multi-modal and multi-typed, compared with traditional homogeneous graphs, heterogeneous graphs
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::add5bf80f458759cf5dc07c33ea2d04b
https://doi.org/10.1016/j.neunet.2022.07.028
https://doi.org/10.1016/j.neunet.2022.07.028
Publikováno v:
Neurocomputing. 501:778-789
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b1e7f6c749b78b0e817d1746954f059c
https://doi.org/10.1016/j.neucom.2022.06.062
https://doi.org/10.1016/j.neucom.2022.06.062
Publikováno v:
Information Sciences. 608:1301-1316
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1ead2a9362a149b18a67a9265a557f9b
https://doi.org/10.1016/j.ins.2022.07.041
https://doi.org/10.1016/j.ins.2022.07.041
Publikováno v:
IEEE Transactions on Cybernetics. 52:912-924
Recent interests in graph neural networks (GNNs) have received increasing concerns due to their superior ability in the network embedding field. The GNNs typically follow a message passing scheme and represent nodes by aggregating features from neigh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1ddc41349332d61cbb65373175fd23e5
https://doi.org/10.1109/tcyb.2020.2988791
https://doi.org/10.1109/tcyb.2020.2988791
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. :1-14
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::028e7f860629765954fd94da44d2c67e
https://doi.org/10.1109/tits.2022.3219626
https://doi.org/10.1109/tits.2022.3219626
Publikováno v:
Procedia Computer Science. 214:1160-1167
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cc4befd35d177c6ccb95b4f36c8c0a7e
https://doi.org/10.1016/j.procs.2022.11.291
https://doi.org/10.1016/j.procs.2022.11.291
Publikováno v:
Pattern Recognition Letters. 149:150-156
Relation Extraction (RE) aims at extracting meaningful relation facts between entities in texts. It is an important semantic processing task in the field of natural language processing (NLP) and has many applications. Traditional RE focuses on extrac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::787f946a8dca208154a8287b075a468a
https://doi.org/10.1016/j.patrec.2021.06.012
https://doi.org/10.1016/j.patrec.2021.06.012
Publikováno v:
IEEE transactions on neural networks and learning systems.
Learning brain effective connectivity networks (ECN) from functional magnetic resonance imaging (fMRI) data has gained much attention in recent years. With the successful applications of deep learning in numerous fields, several brain ECN learning me
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f4fdf331aedd642f6a05bf7ee0334f94
https://pubmed.ncbi.nlm.nih.gov/36399590
https://pubmed.ncbi.nlm.nih.gov/36399590