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approximation theory can be automated by a (neural network) guided search over the action space of standard tools (e.g., Hölder inequalities, Sobolev embeddings, ...). Certain proofs in these fields require
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developing adaptive numerical schemes powered by advanced nonlinear approximations—like Gaussian mixtures and neural networks. The key challenge? Designing robust and stable numerical schemes that remain
Searches related to neural networks
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