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Titlebook: Engineering Applications of Neural Networks; 25th International C Lazaros Iliadis,Ilias Maglogiannis,Chrisina Jayne Conference proceedings

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書目名稱Engineering Applications of Neural Networks
副標(biāo)題25th International C
編輯Lazaros Iliadis,Ilias Maglogiannis,Chrisina Jayne
視頻videohttp://file.papertrans.cn/321/320623/320623.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Engineering Applications of Neural Networks; 25th International C Lazaros Iliadis,Ilias Maglogiannis,Chrisina Jayne Conference proceedings
描述.This book constitutes the refereed proceedings of the 25th International Conference on Engineering Applications of Neural Networks, EANN 2024, held in Corfu, Greece, during June 27-30, 2024.?..The 41 full and 2 short papers included in this book were carefully reviewed and selected from 85 submissions. They deal with reinforcement; natural language; biomedical applications; classificaiton; deep learning; convolutional neural networks.?.
出版日期Conference proceedings 2024
關(guān)鍵詞deep learning; generative AI; cybersecurity; data mining; machine learning; anomaly detection; recommendat
版次1
doihttps://doi.org/10.1007/978-3-031-62495-7
isbn_softcover978-3-031-62494-0
isbn_ebook978-3-031-62495-7Series 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 Switzerl
The information of publication is updating

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https://doi.org/10.1007/978-1-4615-4997-0RL (reinforcement learning) for this task. In total, we compare six different RL algorithms. The utilized algorithms are Q-Learning, MAB (Multi-Armed Bandit), MCTS (Monte Carlo Tree Search), DQN (Deep Q Learning), A2C (Advantage Actor Critic), and PPO (Proximal Policy Optimization). Almost all algor
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https://doi.org/10.1007/978-3-7091-5551-6niversal threshold for table selection. To validate this innovative approach, a custom dataset was curated, leveraging the Spider dataset for NLQ-to-SQL tasks, and a comprehensive set of experiments was conducted using various language models, including GPT-4, GPT-3.5, and DeBERTa..Results demonstra
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