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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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发表于 2025-3-21 18:16:32 | 显示全部楼层 |阅读模式
书目名称Neural Information Processing
副标题30th International C
编辑Biao Luo,Long Cheng,Chaojie Li
视频video
丛书名称Communications in Computer and Information Science
图书封面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
关键词Affective and cognitive learning; Big data; Bioinformatics; Brain-machine interface; Computational finan
版次1
doihttps://doi.org/10.1007/978-981-99-8181-6
isbn_softcover978-981-99-8180-9
isbn_ebook978-981-99-8181-6Series 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
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Neural Information Processing978-981-99-8181-6Series ISSN 1865-0929 Series E-ISSN 1865-0937
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Communications in Computer and Information Sciencehttp://image.papertrans.cn/n/image/663595.jpg
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Road Meteorological State Recognition in Extreme Weather Based on an Improved Mask-RCNNffic accidents can increase dramatically in winter or during seasonal changes when extreme weather often occurs. To achieve real-time and automatic RSC monitoring, this paper first proposes an improved Mask-RCNN model based on Swin Transformer and path aggregation feature pyramid network (PAFPN) as
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I-RAFT: Optical Flow Estimation Model Based on Multi-scale Initialization Strategynt performance improvements. However, existing models that employ recurrent neural networks to update optical flow from an initial value of 0 suffer from issues of instability and slow training. To address this, we propose a simple yet effective optical flow initialization module as part of the opti
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LSiF: Log-Gabor Empowered Siamese Federated Learning for Efficient Obscene Image Classification in thole. It is crucial to tackle this problem by implementing efficient content moderation, educating users, and creating technologies and policies that foster a more secure and wholesome online atmosphere. To address this issue, this research proposes the Log-Gabor Empowered Siamese Federated Learning
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Depth Normalized Stable View Synthesis supposed to be as close as possible to the scene content. We present Deep Normalized Stable View Synthesis (DNSVS), an NVS method for large-scale scenes based on the pipeline of Stable View Synthesis (SVS). SVS combines neural networks with the 3D scene representation obtained from structure-from-m
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Exploring the Integration of Large Language Models into Automatic Speech Recognition Systems: An Emp The increasing sophistication of LLMs, with their in-context learning capabilities and instruction-following behavior, has drawn significant attention in the field of Natural Language Processing (NLP). Our primary focus is to investigate the potential of using an LLM’s in-context learning capabilit
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