Geometric Structures of Statistical Physics, Information Geometry, and Learning: SPIGL'20, Les Houches, France, July 27–31 - Couverture souple

9783030779597: Geometric Structures of Statistical Physics, Information Geometry, and Learning: SPIGL'20, Les Houches, France, July 27–31

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ISBN 10 : 3030779599 ISBN 13 : 9783030779597

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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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�diteurSpringer
Date d'�dition2022
ISBN 10 3030779599
ISBN 13 9783030779597
ReliureBroch�
Langueanglais
Nombre de pages476
�diteurBarbaresco Fr�d�ric, Nielsen Frank
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9783030779566: Geometric Structures of Statistical Physics, Information Geometry, and Learning: Spigl 20, Les Houches, France, July 27-31

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ISBN 10 : 3030779564 ISBN 13 : 9783030779566
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Geometric Structures of Statistical Physics, Information Geometry, and Learning: SPIGL'20, Les Houches, France, July 27-31 (Springer Proceedings in Mathematics & Statistics)

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Geometric Structures of Statistical Physics, Information Geometry, and Learning : SPIGL'20, Les Houches, France, July 27-31

Frank Nielsen

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9783030779597

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Geometric Structures of Statistical Physics, Information Geometry, and Learning

Frank Nielsen

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9783030779597

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Geometric Structures of Statistical Physics, Information Geometry, and Learning

Frank Nielsen

Edit� par Springer International Publishing, Springer Nature Switzerland Jun 2022, 2022

ISBN 10 : 3030779599 ISBN 13 : 9783030779597

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Taschenbuch. Etat : Neu. 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 GmbH, Tiergartenstr. 17, 69121 Heidelberg 476 pp. Englisch. N� de r�f. du vendeur 9783030779597

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