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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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發(fā)表于 2025-3-21 18:48:44 | 只看該作者 |倒序瀏覽 |閱讀模式
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/664/663631/663631.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
關鍵詞pattern recognition; signal processing; computer vision; image reconstruction; neural networks; image seg
版次1
doihttps://doi.org/10.1007/978-981-99-1648-1
isbn_softcover978-981-99-1647-4
isbn_ebook978-981-99-1648-1Series 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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發(fā)表于 2025-3-21 20:41:02 | 只看該作者
https://doi.org/10.1007/978-981-99-1648-1pattern recognition; signal processing; computer vision; image reconstruction; neural networks; image seg
板凳
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Neural Information Processing978-981-99-1648-1Series ISSN 1865-0929 Series E-ISSN 1865-0937
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Automating Patient-Level Lung Cancer Diagnosis in?Different Data Regimeseening with low-dose computed tomography detects cancer at an early stage and reduces mortality. However, it requires the tedious work of radiologists to obtain malignancy scores, which additionally are very subjective. That is why many researchers worked on methods automating lung cancer classifica
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Automatically Generating Storylines from?Microblogging Platforms automatically generate a storyline from the rich information hidden in microblogging platforms, in this paper, we present an effective solution for generating the storyline from microblog posts. Firstly, primary events are extracted through a social-influence-based event model with the temporal dis
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Improving Document Image Understanding with?Reinforcement Finetuningate the problem of improving the performance of Artificial Intelligence systems in understanding document images, especially in cases where training data is limited. We address the problem by proposing a novel finetuning method using reinforcement learning. Our approach treats the Information Extrac
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