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Titlebook: Neural Information Processing; 30th International C Biao Luo,Long Cheng,Chaojie Li Conference proceedings 2024 The Editor(s) (if applicable

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發(fā)表于 2025-3-21 17:52:10 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Neural Information Processing
副標(biāo)題30th International C
編輯Biao Luo,Long Cheng,Chaojie Li
視頻videohttp://file.papertrans.cn/664/663643/663643.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Neural Information Processing; 30th International C Biao Luo,Long Cheng,Chaojie Li Conference proceedings 2024 The Editor(s) (if applicable
描述The nine-volume set constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023.??.The?1274?papers presented in the proceedings set were carefully reviewed and selected from?652?submissions.?.The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements..
出版日期Conference proceedings 2024
關(guān)鍵詞Affective and cognitive learning; Big data; Bioinformatics; Brain-machine interface; Computational finan
版次1
doihttps://doi.org/10.1007/978-981-99-8126-7
isbn_softcover978-981-99-8125-0
isbn_ebook978-981-99-8126-7Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

書目名稱Neural Information Processing影響因子(影響力)




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Neural Information Processing978-981-99-8126-7Series ISSN 1865-0929 Series E-ISSN 1865-0937
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Efficient Hierarchical Reinforcement Learning via?Mutual Information Constrained Subgoal Discoveryhe extensive subgoal space often results in low sample efficiency and challenging exploration. To address this issue, we extract informative subgoals by constraining their generation range in mutual information distance space. Specifically, we impose two constraints on the high-level policy during o
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