AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Recently, Drifting Models and Wasserstein Gradient Flows have attracted substantial attention because they move iterative distributional refinement to training and amortize it into a generator, enabling fast inference. However, existing formulations have been developed largely for continuous Euclidean domains, such as...
The Flow Divergence Sampler is proposed, a training-free framework that refines intermediate states before each solver step that consistently improves fidelity across various generation tasks including text-to-image synthesis, and inverse problems.
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