Banach-valued random feature models, including random single-hidden-layer networks, universally approximate elements of Bochner spaces over non-compact domains with explicit approximation rates.
Approximation by superpositions of a sigmoidal function.Mathematics of Control, Signals and Systems, 2(4):303–314
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Distributed systems in biology, economics, and computing optimize productivity by converging on maximum feasible heterogeneity, with environmental demands and communication topology setting the limits.
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Universal approximation property of Banach space-valued random feature models including random neural networks
Banach-valued random feature models, including random single-hidden-layer networks, universally approximate elements of Bochner spaces over non-compact domains with explicit approximation rates.
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The Principle of Maximum Heterogeneity Optimises Productivity in Distributed Production Systems Across Biology, Economics, and Computing
Distributed systems in biology, economics, and computing optimize productivity by converging on maximum feasible heterogeneity, with environmental demands and communication topology setting the limits.