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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 19:22:45 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Neural Information Processing
副標題30th International C
編輯Biao Luo,Long Cheng,Chaojie Li
視頻videohttp://file.papertrans.cn/664/663593/663593.mp4
叢書名稱Lecture Notes in Computer 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 six-volume set LNCS 14447 until 14452 constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023.?.The 652 papers presented in the proceedings set were carefully reviewed and selected from 1274 submissions. They focus on theory and algorithms, cognitive neurosciences; human centred computing; applications in neuroscience, neural networks, deep learning, and related fields.?.
出版日期Conference proceedings 2024
關鍵詞pattern recognition; affective and cognitive learning; big data; bioinformatics; brain-machine interface
版次1
doihttps://doi.org/10.1007/978-981-99-8082-6
isbn_softcover978-981-99-8081-9
isbn_ebook978-981-99-8082-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
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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沙發(fā)
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地板
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Two-Stage Attention Model to?Solve Large-Scale Traveling Salesman Problemsthe high complexity of large-scale TSPs. This paper proposes a two-stage attention model (TSAM) that incorporates the divide-and-conquer strategy and attention model to solve large-scale TSPs efficiently. Experimental results demonstrate that TSAM can rapidly produce promising solutions for TSP instances ranging from 500 to 10,000 nodes.
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發(fā)表于 2025-3-22 14:01:57 | 只看該作者
0302-9743 d from 1274 submissions. They focus on theory and algorithms, cognitive neurosciences; human centred computing; applications in neuroscience, neural networks, deep learning, and related fields.?.978-981-99-8081-9978-981-99-8082-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Impulsive Accelerated Reinforcement Learning for?, Controlrated gradient methods. Moreover, by utilizing the quasi-periodic Lyapunov function method, sufficient condition for input-to-state stability with respect to approximation errors of the closed-loop system is established. A numerical example with comparisons is provided to illustrate the theoretical results.
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