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Ethan Caballero–Broken Neural Scaling Laws
The Inside View
The Future of Diffusion Models
diffusion models are like KO, divergences within the loss. They decompose in such a way that they downweight the bits of entropy that are imperceptible to human perception. Most second-on-revaluable tasks don't depend on any imperceptible bits and so that's the main reason in my opinion why like diffusion models are successful compared to the other generative models of perceptual data.
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