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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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樓主
發(fā)表于 2025-3-21 19:41:50 | 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/664/663577/663577.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/10.1007/978-981-99-8076-5
isbn_softcover978-981-99-8075-8
isbn_ebook978-981-99-8076-5Series 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
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發(fā)表于 2025-3-21 21:55:31 | 只看該作者
978-981-99-8075-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
板凳
發(fā)表于 2025-3-22 01:47:41 | 只看該作者
Neural Information Processing978-981-99-8076-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
地板
發(fā)表于 2025-3-22 04:53:38 | 只看該作者
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/n/image/663577.jpg
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Cross-Domain Bearing Fault Diagnosis Method Using Hierarchical Pseudo Labelstual industrial scenarios, the scarcity of labeled data presents a challenge. To alleviate this problem, many transfer learning methods have been proposed. Some domain adaptation methods use models trained on source domain to generate pseudo labels for target domain data, which are further employed
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發(fā)表于 2025-3-23 01:51:45 | 只看該作者
Differentiable Topics Guided New Paper Recommendationt is challenging to recommend valuable new papers to the interested researchers. In this paper, we investigate the new paper recommendation task from the point of involved topics and use the concept of subspace to distinguish the academic contributions. We model the papers as topic distributions ove
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