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Geometric Structures of Statistical Physics, Information Geometry, and Learning - SPIGL'20, Les Houches, France, July 27-31 (Paperback, 1st ed. 2021)
Loot Price: R6,920
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Geometric Structures of Statistical Physics, Information Geometry, and Learning - SPIGL'20, Les Houches, France, July 27-31 (Paperback, 1st ed. 2021)
Series: Springer Proceedings in Mathematics & Statistics, 361
Expected to ship within 10 - 15 working days
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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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