Geometric Structures of Statistical Physics, Information Geometry, and Learning

Name: Geometric Structures of Statistical Physics, Information Geometry, and Learning
Brand: Springer Nature
SKU: 9783030779566
Availability: InStock

Best Price (Coupon Required):
Buy Geometric Structures of Statistical Physics, Information Geometry, and Learning for $197.10 at @ Link.springer.com when you apply the 10% OFF coupon at checkout.
Click “Get Coupon & Buy” to copy the code and unlock the deal.

Set a price drop alert to never miss an offer.

1 Offer Price Range: $219.00 - $219.00

BEST PRICE

Single Product Purchase

$197.10

@ Link.springer.com with extra coupon

Price Comparison

Seller	Contact Seller	List Price	On Sale	Shipping	Best Promo	Final Price	Volume Discount	Financing	Availability	Seller's Page
BEST PRICE 1 Product Purchase @ Link.springer.com		$219.00	$219.00		10% OFF This deals requires coupon	$197.10		See Site	In stock	Visit Store

Product Details

Brand

Springer Nature

Manufacturer

N/A

Part Number

GTIN

9783030779566

Condition

New

Product Description

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.

Available Colors

N/A

Available Sizes

N/A