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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 19:15:34 | 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/664/663599/663599.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/10.1007/978-981-99-8178-6
isbn_softcover978-981-99-8177-9
isbn_ebook978-981-99-8178-6Series 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)




書目名稱Neural Information Processing影響因子(影響力)學(xué)科排名




書目名稱Neural Information Processing網(wǎng)絡(luò)公開度




書目名稱Neural Information Processing網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Neural Information Processing被引頻次




書目名稱Neural Information Processing被引頻次學(xué)科排名




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書目名稱Neural Information Processing年度引用學(xué)科排名




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書目名稱Neural Information Processing讀者反饋學(xué)科排名




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發(fā)表于 2025-3-21 22:17:31 | 只看該作者
Neuron Attribution-Based Attacks Fooling Object Detectors examples. Since our attack disturbs the upstream feature outputs, it effectively disorders the outputs of downstream tasks, such as box regression and classification, and finally fool the detector. Extensive experiments on PASCAL VOC and COCO dataset demonstrate that our method achieves better transferability compared to the state-of-the-arts.
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Conference proceedings 2024CONIP 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..
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Improve Conversational Search with?Multi-document Informationeed to be retrieved based on the conversation history to respond to the user. However, existing approaches still make it difficult to distinguish irrelevant information from the user’s question at the semantic level. When a conversation involves multiple documents, it sometimes affects the retrieval
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發(fā)表于 2025-3-23 04:34:15 | 只看該作者
Recurrent Update Representation Based on?Multi-head Attention Mechanism for?Joint Entity and?Relatioever, most of the existing work only considers the information of the context in the sentence and the information of the entities, with little attention to the information of the possible relations between the entities, which may lead to the failure to extract valid triplets. In this paper, we propo
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