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Titlebook: Neural Information Processing; 28th International C Teddy Mantoro,Minho Lee,Achmad Nizar Hidayanto Conference proceedings 2021 Springer Nat

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书目名称Neural Information Processing
副标题28th International C
编辑Teddy Mantoro,Minho Lee,Achmad Nizar Hidayanto
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Neural Information Processing; 28th International C Teddy Mantoro,Minho Lee,Achmad Nizar Hidayanto Conference proceedings 2021 Springer Nat
描述.The four-volume proceedings LNCS 13108, 13109, 13110, and 13111 constitutes the proceedings of the 28th International Conference on Neural Information Processing, ICONIP 2021, which was held during December 8-12, 2021. The conference was planned to take place in Bali, Indonesia but changed to an online format due to the COVID-19 pandemic. ..The total of 226 full papers presented in these proceedings was carefully reviewed and selected from 1093 submissions. The papers were organized in topical sections as follows:..Part I: Theory and algorithms; ..Part II: Theory and algorithms; human centred computing; AI and cybersecurity;..Part III: Cognitive neurosciences; reliable, robust, and secure machine learning algorithms; theory and applications of natural computing paradigms; advances in deep and shallow machine learning algorithms for biomedical data and imaging; applications;  ..Part IV: Applications..
出版日期Conference proceedings 2021
关键词artificial intelligence; computer vision; deep learning; Human-Computer Interaction (HCI); image analysi
版次1
doihttps://doi.org/10.1007/978-3-030-92238-2
isbn_softcover978-3-030-92237-5
isbn_ebook978-3-030-92238-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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A Just-In-Time Compilation Approach for Neural Dynamics Simulationchers in different disciplines. However, the current neural simulators based on low-level language programming or pseudo-programming using high-level descriptive language can not full fill users’ basic requirements, including easy-to-learn-and-use, high flexibility, good transparency, and high-speed
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STCN-GR: Spatial-Temporal Convolutional Networks for Surface-Electromyography-Based Gesture Recognit, which aims to classify gestures according to signals obtained from human hands. Since sEMG signals are characterized by spatial relevancy and temporal nonstationarity, sEMG-based gesture recognition is a challenging task. Previous works attempt to model this structured information and extract spat
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A Reinforcement Learning Approach for Abductive Natural Language GenerationCommonsense Reasoning (.) is a benchmark that investigates model’s ability on inferencing the most plausible explanation within the given context, which requires model using commonsense knowledge about the world. . consists of two datasets, . and ., that challenge models from . and . settings respec
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