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Titlebook: Human-Centered Artificial Intelligence; Advanced Lectures Mohamed Chetouani,Virginia Dignum,Carles Sierra Textbook 2023 Springer Nature Swi

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書目名稱Human-Centered Artificial Intelligence
副標(biāo)題Advanced Lectures
編輯Mohamed Chetouani,Virginia Dignum,Carles Sierra
視頻videohttp://file.papertrans.cn/430/429616/429616.mp4
概述The notion of human-centered AI increasingly dominates the public AI debate.Includes extended and improved lecture notes from ACAI 2021 course.Basic knowledge needed to design, implement, operate and
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Human-Centered Artificial Intelligence; Advanced Lectures Mohamed Chetouani,Virginia Dignum,Carles Sierra Textbook 2023 Springer Nature Swi
描述.The 18th European Advanced Course on AI (ACAI) took place in Berlin on 11-15 October 2021, organized by the European project Humane-AI Net in collaboration with the European AI Association (EURAI). The school included tutorials on different topics, which were selected through an open call to top European AI researchers. In addition, the school also included 4 invited talks, a student poster presentation, and a mentorship program...This volume contains 21 tutorial chapters organized according to the following themes: human-centered AI; human-centered machine learning; explainable AI; ethics, law, and the societal aspects of AI; argumentation; and social simulation. The contributions include learning objectives, reading lists, and links to further resources..
出版日期Textbook 2023
關(guān)鍵詞human-centered computing; human-computer interaction; collaborative and social computing; artificial in
版次1
doihttps://doi.org/10.1007/978-3-031-24349-3
isbn_softcover978-3-031-24348-6
isbn_ebook978-3-031-24349-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2023
The information of publication is updating

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Generative Networks and the AutoEncoder, and shows that discriminative networks can be combined with generative networks to produce an autoencoder. Autoencoders can be trained with self-supervised learning to provide a compact code for signals. This code can be used to reconstruct clean copies of noisy signals. With a simple modification
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Transformers in?Natural Language Processingformer model, which plays a central role in a wide range of applications. This architecture condenses many advances in neural learning methods and can be exploited in many ways: to learn representations for linguistic entities; to generate coherent utterances and answer questions; to perform utteran
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