Zobrazeno 1 - 10
of 106
pro vyhledávání: '"GUODONG LONG"'
Autor:
Yiqiang Chen, Wuliang Huang, Xinlong Jiang, Teng Zhang, Yi Wang, Bingjie Yan, Zhirui Wang, Qian Chen, Yunbing Xing, Dong Li, Guodong Long
Publikováno v:
International Journal of Crowd Science, Vol 7, Iss 4, Pp 180-189 (2023)
The metaverse signifies the amalgamation of virtual and tangible realms through human-computer interaction. The seamless integration of human, cyber, and environments within ubiquitous computing plays a pivotal role in fully harnessing the metaverse
Externí odkaz:
https://doaj.org/article/38261be4d3654e978d23558d142b4750
Publikováno v:
IEEE Access, Vol 8, Pp 82481-82492 (2020)
Influence measurement in social networks is vital to various real-world applications, such as online marketing and political campaigns. In this paper, we investigate the problem of measuring time-sensitive and topic-specific influence based on stream
Externí odkaz:
https://doaj.org/article/06565e9ebc894f5486bdadd911564af0
Publikováno v:
Complexity, Vol 2018 (2018)
Early detection and treatment are regarded as the most effective ways to prevent suicidal ideation and potential suicide attempts—two critical risk factors resulting in successful suicides. Online communication channels are becoming a new way for p
Externí odkaz:
https://doaj.org/article/57c6d3c51f8040aca869bd73b81f6c30
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 34:2293-2305
We study many-class few-shot (MCFS) problem in both supervised learning and meta-learning settings. Compared to the well-studied many-class many-shot and few-class few-shot problems, the MCFS problem commonly occurs in practical applications but has
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::90992d73284272145e478ee47ea3ad91
https://doi.org/10.1109/tkde.2020.3004939
https://doi.org/10.1109/tkde.2020.3004939
Autor:
Fengwen Chen, Guodong Long
Publikováno v:
IEEE Transactions on Big Data. :1-11
Neighborhood aggregation algorithms, represented as graph convolutional networks, have attained non-negligible success in numerous topological structure-based scenarios with the assumption that the topological structure of the given graph is pre-defi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ed535115cf7f656efb803673d17c75f9
https://doi.org/10.1109/tbdata.2022.3216747
https://doi.org/10.1109/tbdata.2022.3216747
Publikováno v:
Communications in Computer and Information Science ISBN: 9789819916412
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ef73534daaff65027eaacd36661eada2
https://doi.org/10.1007/978-981-99-1642-9_6
https://doi.org/10.1007/978-981-99-1642-9_6
Publikováno v:
Web and Big Data ISBN: 9783031251573
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::148053f5769f5811d848c0427a0e5126
https://doi.org/10.1007/978-3-031-25158-0_19
https://doi.org/10.1007/978-3-031-25158-0_19
Publikováno v:
Humanity Driven AI ISBN: 9783030721879
Privacy protection is an ethical issue with broad concern in artificial intelligence (AI). Federated learning is a new machine learning paradigm to learn a shared model across users or organisations without direct access to the data. It has great pot
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::806e69ba0e02a15d4fbee94e26ba2d4d
https://hdl.handle.net/10453/167328
https://hdl.handle.net/10453/167328
Autor:
Qi Cheng, Guodong Long
Federated Learning has witnessed a rapid growth in research and industry applications as it offers the benefits of privacy preserving while contributing to the global model training. Cross-silo federated learning systems which are usually geographica
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a21e7e9475c8545ddbe9a636aff54212
https://hdl.handle.net/10453/170505
https://hdl.handle.net/10453/170505