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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LAWS is a self-certifying parametrized cache that generalizes mixture-of-experts and KV caching with uniform error bounds based on Lipschitz constants and embedding diameters.
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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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LAWS: Learning from Actual Workloads Symbolically -- A Self-Certifying Parametrized Cache Architecture for Neural Inference, Robotics, and Edge Deployment
LAWS is a self-certifying parametrized cache that generalizes mixture-of-experts and KV caching with uniform error bounds based on Lipschitz constants and embedding diameters.