Title:
Machine learning meets super-resolution
Abstract:
We demonstrate a duality between certain problems of function approximation and probability estimation in
machine learning and problems of super-resolution in signal separation. In particular, we will explain how the same
tools from harmonic analysis can be used for both purposes, leading to a unified theory. We will demonstrate our
ideas with some numerical examples.
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