Abstract: Generative diffusion models are gaining attention as a promising solution for synthetic data generation, offering a distinct advantage over traditional statistical methods and basic ...
Abstract: Differential neural networks (DiNNs) encounter a trade-off between the approximation quality and structural complexity. One promising approach to address this trade-off is incorporating ...
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