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.
By:
Seymour L Purvis Imprint: Scholastic Singapore Country of Publication: Singapore Dimensions:
Height: 229mm,
Width: 152mm,
Spine: 8mm
Weight: 200g ISBN:9789810898106 ISBN 10: 981089810X Pages: 142 Publication Date:13 March 2024 Audience:
General/trade
,
ELT Advanced
Format:Paperback Publisher's Status: Active