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Titlebook: Advances in Neural Computation, Machine Learning, and Cognitive Research VII; Selected Papers from Boris Kryzhanovsky,Witali Dunin-Barkowsk

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發(fā)表于 2025-3-21 19:37:26 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Advances in Neural Computation, Machine Learning, and Cognitive Research VII
期刊簡(jiǎn)稱Selected Papers from
影響因子2023Boris Kryzhanovsky,Witali Dunin-Barkowski,Valentin
視頻videohttp://file.papertrans.cn/150/149137/149137.mp4
發(fā)行地址Reports on advanced theories and applications of artificial neural networks.Focuses on problems in neuroscience, systems biophysics, cognitive research, and adaptive control.Merges topics in neurobiol
學(xué)科分類Studies in Computational Intelligence
圖書封面Titlebook: Advances in Neural Computation, Machine Learning, and Cognitive Research VII; Selected Papers from Boris Kryzhanovsky,Witali Dunin-Barkowsk
影響因子.This book describes new theories and applications of artificial neural networks, with a special focus on answering questions in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large-scale neural models, brain–computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more. The book includes both selected and invited papers presented at the XXV International Conference on Neuroinformatics, held on October 23-27, 2023, in Moscow, Russia..
Pindex Conference proceedings 2023
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978-3-031-44867-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Boris Kryzhanovsky,Witali Dunin-Barkowski,ValentinReports on advanced theories and applications of artificial neural networks.Focuses on problems in neuroscience, systems biophysics, cognitive research, and adaptive control.Merges topics in neurobiol
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Studies in Computational Intelligencehttp://image.papertrans.cn/a/image/149137.jpg
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Stefanos Gritzalis,Diomidis Spinellisch require the presence of a substantially sized annotated dataset, recent works aiming to utilize pretrained extractive question-answering (QA) models have shown significant few and zero-shot capabilities. This work aims to further investigate their applicability in zero-shot setting i.e. without e
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MMIP — Mixed Mobile Internet Protocolin detailed 3D point clouds of scanned areas, significantly increasing the efficiency of identification. The breakthrough in 3D object classification has opened new opportunities for the practical application of deep learning methods to identify forest tree species, which is a key task for forest ma
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