Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Shengyi Jiang"'
Publikováno v:
Applied Artificial Intelligence, Vol 36, Iss 1 (2022)
With the rapid development of social networks, more and more people express their emotions and opinions via online videos. However, most of the current research on multimodal sentiment analysis cannot do well with effective emotional fusion in multim
Externí odkaz:
https://doaj.org/article/0b3c32fa622a4e83a27e23875934b871
Publikováno v:
IEEE/ACM Transactions on Audio, Speech, and Language Processing. 31:371-382
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8550235e06948e869fcfdbc7d67d9cbf
https://doi.org/10.1109/taslp.2022.3212698
https://doi.org/10.1109/taslp.2022.3212698
Publikováno v:
IEEE/ACM Transactions on Audio, Speech, and Language Processing. 31:1265-1275
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0c560cff9a86625a35da4d8dac719110
https://doi.org/10.1109/taslp.2023.3250828
https://doi.org/10.1109/taslp.2023.3250828
Publikováno v:
Tsinghua Science and Technology. 27:709-718
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3d20e2c4407b627da62daf7fdb0ef5e2
https://doi.org/10.26599/tst.2021.9010060
https://doi.org/10.26599/tst.2021.9010060
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 36:7637-7646
Dealing with real-world reinforcement learning (RL) tasks, we shall be aware that the environment may have sudden changes. We expect that a robust policy is able to handle such changes and adapt to the new environment rapidly. Context-based meta rein
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8630ea499d3d79e1c131517beb5e428e
https://doi.org/10.1609/aaai.v36i7.20730
https://doi.org/10.1609/aaai.v36i7.20730
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 36:9260-9268
Good representations can help RL agents perform concise modeling of their surroundings, and thus support effective decision-making in complex environments. Previous methods learn good representations by imposing extra constraints on dynamics. However
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::06e328ef6d431e6e2f17412cbdc26ae6
https://doi.org/10.1609/aaai.v36i8.20913
https://doi.org/10.1609/aaai.v36i8.20913
Publikováno v:
Digital Scholarship in the Humanities. 37:1073-1083
At the stage of current studies, the synergetic-linguistic model is mainly used for the validation of individual languages, lacking application to the measurement of linguistic differences. In addition, the structure of the model is largely fixed and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9cb02c4791aecfc9fd05195490404c18
https://doi.org/10.1093/llc/fqac009
https://doi.org/10.1093/llc/fqac009
Deep learning-based text classification models need abundant labeled data to obtain competitive performance. Unfortunately, annotating large-size corpus is time-consuming and laborious. To tackle this, multiple researches try to use data augmentation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bcee0213a9caa1a7d227254ed5c164d1
Autor:
Wenbo Zhang, Jing Li, Paul C. Struik, Ke Jin, Baoming Ji, Shengyi Jiang, Yong Zhang, Yuanheng Li, Xiaojiang Yang, Zhen Wang
Publikováno v:
Science of the Total Environment, 892
Science of the Total Environment 892 (2023)
Science of the Total Environment 892 (2023)
Grazing exclusion changes soil physical-chemical characteristics, rapidly affects microbial community composition and function, and alters biogeochemical processes, e.g., carbon (C) cycle, over time. However, the temporal dynamics of CO2 emission and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6a5c4dd00831304a285528ef92f88d7a
https://research.wur.nl/en/publications/recovery-through-proper-grazing-exclusion-promotes-the-carbon-cyc
https://research.wur.nl/en/publications/recovery-through-proper-grazing-exclusion-promotes-the-carbon-cyc
Publikováno v:
Neural Information Processing ISBN: 9783031301100
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d02c299cb13027d95206d4be5ba7a10d
https://doi.org/10.1007/978-3-031-30111-7_3
https://doi.org/10.1007/978-3-031-30111-7_3