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    Taschenbuch. Condition: Neu. Geometric Structures of Statistical Physics, Information Geometry, and Learning | SPIGL'20, Les Houches, France, July 27-31 | Frédéric Barbaresco (u. a.) | Taschenbuch | xiii | Englisch | 2022 | Springer | EAN 9783030779597 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

  • Language: English

    Published by Springer Nature Switzerland AG, Cham, 2021

    3030779564 / 9783030779566

    Series: Book 348 of 464 - Springer Proceedings in Mathematics & Statistics

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    Hardcover. Condition: new. Hardcover. Machine learning and artificial intelligence increasingly use methodological tools rooted in statistical physics. Conversely, limitations and pitfalls encountered in AI question the very foundations of statistical physics. This interplay between AI and statistical physics has been attested since the birth of AI, and principles underpinning statistical physics can shed new light on the conceptual basis of AI. During the last fifty years, statistical physics has been investigated through new geometric structures allowing covariant formalization of the thermodynamics. Inference methods in machine learning have begun to adapt these new geometric structures to process data in more abstract representation spaces.This volume collects selected contributions on the interplay of statistical physics and artificial intelligence. The aim is to provide a constructive dialogue around a common foundation to allow the establishment of new principles and laws governing these two disciplines in a unified manner. The contributions were presented at the workshop on the Joint Structures and Common Foundation of Statistical Physics, Information Geometry and Inference for Learning which was held in Les Houches in July 2020. The various theoretical approaches are discussed in the context of potential applications in cognitive systems, machine learning, signal processing. Machine learning and artificial intelligence increasingly use methodological tools rooted in statistical physics. This interplay between AI and statistical physics has been attested since the birth of AI, and principles underpinning statistical physics can shed new light on the conceptual basis of AI. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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    Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Machine learning and artificial intelligence increasingly use methodological tools rooted in statistical physics. Conversely, limitations and pitfalls encountered in AI question the very foundations of statistical physics. This interplay between AI and statistical physics has been attested since the birth of AI, and principles underpinning statistical physics can shed new light on the conceptual basis of AI. During the last fifty years, statistical physics has been investigated through new geometric structures allowing covariant formalization of the thermodynamics. Inference methods in machine learning have begun to adapt these new geometric structures to process data in more abstract representation spaces.This volume collects selected contributions on the interplay of statistical physics and artificial intelligence. The aim is to provide a constructive dialogue around a common foundation to allow the establishment of new principles and laws governing these two disciplines in a unified manner. The contributions were presented at the workshop on the Joint Structures and Common Foundation of Statistical Physics, Information Geometry and Inference for Learning which was held in Les Houches in July 2020. The various theoretical approaches are discussed in the context of potential applications in cognitive systems, machine learning, signal processing.…

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    Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Machine learning and artificial intelligence increasingly use methodological tools rooted in statistical physics. Conversely, limitations and pitfalls encountered in AI question the very foundations of statistical physics. This interplay between AI and statistical physics has been attested since the birth of AI, and principles underpinning statistical physics can shed new light on the conceptual basis of AI. During the last fifty years, statistical physics has been investigated through new geometric structures allowing covariant formalization of the thermodynamics. Inference methods in machine learning have begun to adapt these new geometric structures to process data in more abstract representation spaces.This volume collects selected contributions on the interplay of statistical physics and artificial intelligence. The aim is to provide a constructive dialogue around a common foundation to allow the establishment of new principles and laws governing these two disciplines in a unified manner. The contributions were presented at the workshop on the Joint Structures and Common Foundation of Statistical Physics, Information Geometry and Inference for Learning which was held in Les Houches in July 2020. The various theoretical approaches are discussed in the context of potential applications in cognitive systems, machine learning, signal processing. …

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    Condition: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.

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  • Language: English

    Published by Springer, 2022

    3030779599 / 9783030779597

    Series: Book 348 of 464 - Springer Proceedings in Mathematics & Statistics

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  • Language: English

    Published by Springer, 2021

    3030779564 / 9783030779566

    Series: Book 348 of 464 - Springer Proceedings in Mathematics & Statistics

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  • Language: English

    Published by Springer, Berlin|Springer International Publishing|Springer, 2022

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    Series: Book 348 of 464 - Springer Proceedings in Mathematics & Statistics

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    Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Machine learning and artificial intelligence increasingly use methodological tools rooted in statistical physics. Conversely, limitations and pitfalls encountered in AI question the very foundations of statistical physics. This interplay between AI and s.…

  • Language: English

    Published by Springer, Berlin|Springer International Publishing|Springer, 2021

    3030779564 / 9783030779566

    Series: Book 348 of 464 - Springer Proceedings in Mathematics & Statistics

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    Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Machine learning and artificial intelligence increasingly use methodological tools rooted in statistical physics. This interplay between AI and statistical physics has been attested since the birth of AI, and principles underpinning statistical physics can .…

  • Language: English

    Published by Springer International Publishing, Springer Nature Switzerland Jun 2021, 2021

    3030779564 / 9783030779566

    Series: Book 348 of 464 - Springer Proceedings in Mathematics & Statistics

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    Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Machine learning and artificial intelligence increasingly use methodological tools rooted in statistical physics. Conversely, limitations and pitfalls encountered in AI question the very foundations of statistical physics. This interplay between AI and statistical physics has been attested since the birth of AI, and principles underpinning statistical physics can shed new light on the conceptual basis of AI. During the last fifty years, statistical physics has been investigated through new geometric structures allowing covariant formalization of the thermodynamics. Inference methods in machine learning have begun to adapt these new geometric structures to process data in more abstract representation spaces.This volume collects selected contributions on the interplay of statistical physics and artificial intelligence. The aim is to provide a constructive dialogue around a common foundation to allow the establishment of new principles and laws governing these two disciplines in a unified manner. The contributions were presented at the workshop on the Joint Structures and Common Foundation of Statistical Physics, Information Geometry and Inference for Learning which was held in Les Houches in July 2020. The various theoretical approaches are discussed in the context of potential applications in cognitive systems, machine learning, signal processing. 476 pp. Englisch.…

  • Language: English

    Published by Springer International Publishing, Springer Nature Switzerland Jun 2022, 2022

    3030779599 / 9783030779597

    Series: Book 348 of 464 - Springer Proceedings in Mathematics & Statistics

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    Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Machine learning and artificial intelligence increasingly use methodological tools rooted in statistical physics. Conversely, limitations and pitfalls encountered in AI question the very foundations of statistical physics. This interplay between AI and statistical physics has been attested since the birth of AI, and principles underpinning statistical physics can shed new light on the conceptual basis of AI. During the last fifty years, statistical physics has been investigated through new geometric structures allowing covariant formalization of the thermodynamics. Inference methods in machine learning have begun to adapt these new geometric structures to process data in more abstract representation spaces.This volume collects selected contributions on the interplay of statistical physics and artificial intelligence. The aim is to provide a constructive dialogue around a common foundation to allow the establishment of new principles and laws governing these two disciplines in a unified manner. The contributions were presented at the workshop on the Joint Structures and Common Foundation of Statistical Physics, Information Geometry and Inference for Learning which was held in Les Houches in July 2020. The various theoretical approaches are discussed in the context of potential applications in cognitive systems, machine learning, signal processing. 476 pp. Englisch.…

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    Published by Springer, Springer Jun 2022, 2022

    3030779599 / 9783030779597

    Series: Book 348 of 464 - Springer Proceedings in Mathematics & Statistics

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    Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Machine learning and artificial intelligence increasingly use methodological tools rooted in statistical physics. Conversely, limitations and pitfalls encountered in AI question the very foundations of statistical physics. This interplay between AI and statistical physics has been attested since the birth of AI, and principles underpinning statistical physics can shed new light on the conceptual basis of AI. During the last fifty years, statistical physics has been investigated through new geometric structures allowing covariant formalization of the thermodynamics. Inference methods in machine learning have begun to adapt these new geometric structures to process data in more abstract representation spaces.This volume collects selected contributions on the interplay of statistical physics and artificial intelligence. The aim is to provide a constructive dialogue around a common foundation to allow the establishment of new principles and laws governing these two disciplines in a unified manner. The contributions were presented at the workshop on the Joint Structures and Common Foundation of Statistical Physics, Information Geometry and Inference for Learning which was held in Les Houches in July 2020. The various theoretical approaches are discussed in the context of potential applications in cognitive systems, machine learning, signal processing.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 476 pp. Englisch.…

  • Language: English

    Published by Springer, Springer International Publishing Jun 2021, 2021

    3030779564 / 9783030779566

    Series: Book 348 of 464 - Springer Proceedings in Mathematics & Statistics

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    Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Machine learning and artificial intelligence increasingly use methodological tools rooted in statistical physics. Conversely, limitations and pitfalls encountered in AI question the very foundations of statistical physics. This interplay between AI and statistical physics has been attested since the birth of AI, and principles underpinning statistical physics can shed new light on the conceptual basis of AI. During the last fifty years, statistical physics has been investigated through new geometric structures allowing covariant formalization of the thermodynamics. Inference methods in machine learning have begun to adapt these new geometric structures to process data in more abstract representation spaces.This volume collects selected contributions on the interplay of statistical physics and artificial intelligence. The aim is to provide a constructive dialogue around a common foundation to allow the establishment of new principles and laws governing these two disciplines in a unified manner. The contributions were presented at the workshop on the Joint Structures and Common Foundation of Statistical Physics, Information Geometry and Inference for Learning which was held in Les Houches in July 2020. The various theoretical approaches are discussed in the context of potential applications in cognitive systems, machine learning, signal processing.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 476 pp. Englisch.…