| 書目名稱 | Explainable AI: Interpreting, Explaining and Visualizing Deep Learning | | 編輯 | Wojciech Samek,Grégoire Montavon,Klaus-Robert Müll | | 視頻video | http://file.papertrans.cn/320/319285/319285.mp4 | | 概述 | Assesses the current state of research on Explainable AI (XAI).Provides a snapshot of interpretable AI techniques.Reflects the current discourse and provides directions of future development | | 叢書名稱 | Lecture Notes in Computer Science | | 圖書封面 |  | | 描述 | .The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For?sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to?perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner...The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of?interpretable and explainable AI and AI techniques that have been proposed recently reflecti | | 出版日期 | Book 2019 | | 關鍵詞 | artificial intelligence; computer vision; deep Learning; explainable AI; explanation Methods; fuzzy contr | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-28954-6 | | isbn_softcover | 978-3-030-28953-9 | | isbn_ebook | 978-3-030-28954-6Series ISSN 0302-9743 Series E-ISSN 1611-3349 | | issn_series | 0302-9743 | | copyright | Springer Nature Switzerland AG 2019 |
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書目名稱Explainable AI: Interpreting, Explaining and Visualizing Deep Learning影響因子(影響力)學科排名
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