Bounds on Sparsity of One-Hidden-Layer Perceptron Networks

0478625 - ÚI 2018 RIV DE eng C - Conference Paper (international conference)
Kůrková, Věra
Bounds on Sparsity of One-Hidden-Layer Perceptron Networks.
Proceedings ITAT 2017: Information Technologies - Applications and Theory. Aachen & Charleston: Technical University & CreateSpace Independent Publishing Platform, 2017 - (Hlaváčová, J.), s. 100-105. CEUR Workshop Proceedings, V-1885. ISBN 978-1974274741. ISSN 1613-0073.
[ITAT 2017. Conference on Theory and Practice of Information Technologies - Applications and Theory /17./. Martinské hole (SK), 22.09.2017-26.09.2017]
R&D Projects: GA ČR GA15-18108S
Institutional support: RVO:67985807
Keywords : shallow perceptron networks * sparse networks * pseudo-noise sequences * variational norm
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://ceur-ws.org/Vol-1885/100.pdf

Limitations of one-hidden-layer (shallow) perceptron networks to sparsely represent multivariable functions is investigated. A concrete class of functions is described whose computation by shallow perceptron networks requires either large number of units or is unstable due to large output weights. The class is constructed using pseudo-noise sequences which have many features of random sequences but can be generated using special polynomials. Connections with the central paradox of coding theory are discussed.
Permanent Link: http://hdl.handle.net/11104/0274766