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Titlebook: Neural Information Processing; 29th International C Mohammad Tanveer,Sonali Agarwal,Adam Jatowt Conference proceedings 2023 The Editor(s) (

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書目名稱Neural Information Processing
副標題29th International C
編輯Mohammad Tanveer,Sonali Agarwal,Adam Jatowt
視頻videohttp://file.papertrans.cn/664/663617/663617.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Neural Information Processing; 29th International C Mohammad Tanveer,Sonali Agarwal,Adam Jatowt Conference proceedings 2023 The Editor(s) (
描述The four-volume set CCIS 1791, 1792, 1793 and 1794 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022.?.The 213 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications..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 2023
關(guān)鍵詞pattern recognition; signal processing; computer vision; image reconstruction; neural networks; image pro
版次1
doihttps://doi.org/10.1007/978-981-99-1642-9
isbn_softcover978-981-99-1641-2
isbn_ebook978-981-99-1642-9Series 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

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Adaptive Rounding Compensation for?Post-training Quantization dynamic compensation method to select critical layers to be compensated in terms of parameters and quantization errors. Extensive experiments verify that our method can achieve superior results on ImageNet for classification and MSCOCO for object detection. Codes are available at ..
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Multi-Grained Fusion Graph Neural Networks for?Sequential Recommendationsts. In addition, we capture coarse-grained and fine-grained interests by attention mechanism and then fuse them as a multi-grained interest representation. Empirical studies on three real-world datasets demonstrate the effectiveness of our proposed method.
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