東華大學圖書館 |

登入

回首頁

Geometric structures of statistical ...

Joint Structures and Common Foundation of Statistical Physics, Information Geometry and Inference for Learning ((2020 :)

FindBook

Google Book

Amazon

博客來

Geometric structures of statistical physics, information geometry, and learning = SPIGL'20, Les Houches, France, July 27-31 /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Geometric structures of statistical physics, information geometry, and learning/ edited by Frederic Barbaresco, Frank Nielsen.
其他題名:	SPIGL'20, Les Houches, France, July 27-31 /
其他題名:	SPIGL'20
其他作者:	Barbaresco, Frederic.
團體作者:	Joint Structures and Common Foundation of Statistical Physics, Information Geometry and Inference for Learning
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	xiii, 459 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Statistical physics - Congresses. -
電子資源:	https://doi.org/10.1007/978-3-030-77957-3
ISBN:	9783030779573

Geometric structures of statistical physics, information geometry, and learning = SPIGL'20, Les Houches, France, July 27-31 /
Geometric structures of statistical physics, information geometry, and learningSPIGL'20, Les Houches, France, July 27-31 /[electronic resource] :SPIGL'20edited by Frederic Barbaresco, Frank Nielsen. - Cham :Springer International Publishing :2021. - xiii, 459 p. :ill. (some col.), digital ;24 cm. - Springer proceedings in mathematics & statistics,v.3612194-1009 ;. - Springer proceedings in mathematics & statistics ;v.361..

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.

ISBN: 9783030779573

Standard No.: 10.1007/978-3-030-77957-3doiSubjects--Topical Terms:

659297
Statistical physics
--Congresses.

LC Class. No.: QC174.7

Dewey Class. No.: 530.13

Geometric structures of statistical physics, information geometry, and learning = SPIGL'20, Les Houches, France, July 27-31 /
LDR:02546nmm a2200337 a 4500 001 2244860
003 DE-He213
005 20210702131828.0
006 m d
007 cr nn 008maaau
008 211207s2021 sz s 0 eng d
020 $a 9783030779573 $q (electronic bk.)
020 $a 9783030779566 $q (paper)
024 7 $a 10.1007/978-3-030-77957-3 $2 doi
035 $a 978-3-030-77957-3
040 $a GP $c GP
041 0 $a eng
050 4 $a QC174.7
072 7 $a PBWH $2 bicssc
072 7 $a MAT003000 $2 bisacsh
072 7 $a PBWH $2 thema
082 0 4 $a 530.13 $2 23
090 $a QC174.7 $b .J74 2020
111 2 $a Joint Structures and Common Foundation of Statistical Physics, Information Geometry and Inference for Learning $d (2020 : $c Les Houches, Haute-Savoie, France) $3 3506156
245 1 0 $a Geometric structures of statistical physics, information geometry, and learning $h [electronic resource] : $b SPIGL'20, Les Houches, France, July 27-31 / $c edited by Frederic Barbaresco, Frank Nielsen.
246 3 $a SPIGL'20
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xiii, 459 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Springer proceedings in mathematics & statistics, $x 2194-1009 ; $v v.361
520 $a 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.
650 0 $a Statistical physics $x Congresses. $3 659297
650 0 $a Artificial intelligence $v Congresses. $3 606815
650 1 4 $a Mathematical Applications in Computer Science. $3 1567978
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Theoretical, Mathematical and Computational Physics. $3 1066859
650 2 4 $a Statistical Theory and Methods. $3 891074
700 1 $a Barbaresco, Frederic. $3 2162570
700 1 $a Nielsen, Frank. $3 778030
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Springer proceedings in mathematics & statistics ; $v v.361. $3 3506157
856 4 0 $u https://doi.org/10.1007/978-3-030-77957-3
950 $a Mathematics and Statistics (SpringerNature-11649)