Symmetric Neural Networks Theory - Paperback

FormatBook

Language English

ISBN-13 / SKU 9789810898106

About this item

by Seymour L. Purvis (Author)

Symmetric functions, which take as input an unordered, fixed-size s et, find practical application in myriad physical settings based on indistinguishable points or particles, and are also used as intermediate building blocks to construct networks with other invariances. Symmetric functions

are known to be universally representable by neural networks that enforce permutation invariance. However the theoretical tools that characterize the approximation, optimization and generalization of typical networks fail to adequately characterize architectures that enforce invariance.

Number of Pages: 142

Dimensions: 0.3 x 9 x 6 IN

Publication Date: March 13, 2024

$33.83

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