SED modifies diffusion models to generate only non-zero values in sparse data, preserving sparsity patterns, cutting computation, and matching or beating standard DM performance on benchmarks.
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Skipping the Zeros in Diffusion Models for Sparse Data Generation
SED modifies diffusion models to generate only non-zero values in sparse data, preserving sparsity patterns, cutting computation, and matching or beating standard DM performance on benchmarks.