Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks
The paper presents a rigorous theoretical framework for understanding Diffusion Models by using ratio-based function approximation with SignReLU networks. The authors developed this approach to handle complex ratio-type functionals in generative modeling, which is difficult for standard AI methods. It provides practical blueprints and tight bounds on KL risk, leading to more reliable and interpretable generative AI.