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From Distributions to Stochastic Processes: Neural Approximation of Measure-Valued Maps
Learning mappings between probability distributions arises naturally when inputs and outputs are represented by populations of samples rather than individual observations. We develop an approximation-theoretic framework for distribution-to-distribution learning and extend it to mappings between stoc
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