Moon Taesup (13 results)
- Softcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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- Softcover
Seller: Chiron Media, Wallingford, United KingdomChiron Media
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PF. Condition: New.
- Softcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
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Condition: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
- Softcover
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrelandKennys Bookshop and Art Galleries Ltd.
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- Softcover
Seller: Kennys Bookstore, Olney, MD, U.S.A.Kennys Bookstore
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- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This is an open access book.Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have beco…me increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed.After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science.
More images- Softcover
Seller: preigu, Osnabrück, Germanypreigu
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Taschenbuch. Condition: Neu. xxAI - Beyond Explainable AI | International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers | Andreas Holzinger (u. a.) | Taschenbuch | Lecture Notes in Computer Science | x | Englisch | 2022 | Springer | EAN 9783031040825 | Verantwortliche P…erson für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
- Softcover
- Print on Demand
Seller: Brook Bookstore On Demand, Napoli, NA, ItalyBrook Bookstore On Demand
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Condition: new. Questo è un articolo print on demand.
Language: English
Published by Springer International Publishing Apr 2022 2022
- Softcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This is an open access book.Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity…, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed.After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science. 408 pp. Englisch.
- Softcover
- Print on Demand
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
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- Softcover
- Print on Demand
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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Language: English
Published by Springer, Berlin|Springer International Publishing|Fraunhofer Heinrich Hertz Institute|Springer 2022
- Softcover
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Seller: moluna, Greven, Germanymoluna
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This is an open access book.Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed bett…er predictivity, they have become inc.
- Softcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Editorial.- xxAI - Beyond explainable Artificial Intelligence.- Current Methods and Challenges.- Explainable AI Methods - A Brief Overview.- Challenges in Deploying Explainable Machine Learning.- Methods for Machine Learning Models.- CL…EVR-X: A Visual Reasoning Dataset for Natural Language Explanations.- New Developments in Explainable AI.- A Rate-Distortion Framework for Explaining Black-box Model Decisions.- Explaining the Predictions of Unsupervised Learning Models.- Towards Causal Algorithmic Recourse.- Interpreting Generative Adversarial Networks for Interactive Image Generation.- XAI and Strategy Extraction via Reward Redistribution.- Interpretable, Verifiable, and Robust Reinforcement Learning via Program Synthesis.- Interpreting and improving deep-learning models with reality checks.- Beyond the Visual Analysis of Deep Model Saliency.- ECQ^2: Quantization for Low-Bit and Sparse DNNs.- A whale's tail - Finding the right whale in an uncertain world.- Explainable Artificial Intelligence in Meteorology and Climate Science: Model fine-tuning, calibrating trust and learning new science.- An Interdisciplinary Approach to Explainable AI.-Varieties of AI Explanations under the Law - From the GDPR to the AIA, and beyond.- Towards Explainability for AI Fairness.- Logic and Pragmatics in AI Explanation.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 408 pp. Englisch.
