£156.61

About as cheap as it gets — within 5% of the all-time low it hit 2 months ago.

£157 today · all-time low £157 (Aug 2026) · usually £157

World Scientific Publishing Company Equivariant & Coordinate Independent Convolutional Networks - Gauge Field Theory

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Description

Explore the mathematical foundations of neural networks designed to process spatial signals on Euclidean spaces and manifolds through gauge field theory. This advanced text examines how feature vector fields represent data including images, audio, videos, and point clouds, whilst equivariant networks preserve symmetry group actions on feature spaces. Written by leading researchers, this book bridges differential geometry and deep learning, offering rigorous treatment of coordinate-independent methods and their applications to tensor fields such as fluid flows and electromagnetic fields. Essential reading for mathematicians and machine learning researchers seeking to understand the geometric principles underlying modern convolutional architectures.

Key Features

Presents gauge field theory approach to convolutional networks processing signals on Euclidean spaces and general manifolds.

Covers feature vector fields as mathematical framework for representing images, audio, videos, point clouds, and tensor field data.

Explains equivariant networks that commute with symmetry group actions on feature spaces.

Combines differential geometry with deep learning theory for coordinate-independent network design.

Published by World Scientific Publishing Company as part of the Progress In Data Science series.

Suitable for advanced graduate students and researchers in mathematics and machine learning.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
24 December 2025
Listed Since
07 December 2024

Barcode

No barcode data available

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