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Score-based diffusion models corrupt data by gradually adding Gaussian noise and then learn to reverse this process by estimating the score, the gradient of the log-density.
Langevin dynamics is a noisy gradient-ascent method that moves particles toward high probability regions while adding Gaussian noise to ensure proper exploration.