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.
Full error analysis of the random deep splitting method for nonlinear parabolic pdes and pides with infinite activity
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INEUS solves high-dimensional PIDEs via iterative neural regression with single-jump sampling instead of full integral evaluation.
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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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INEUS: Iterative Neural Solver for High-Dimensional PIDEs
INEUS solves high-dimensional PIDEs via iterative neural regression with single-jump sampling instead of full integral evaluation.