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Score matching is one of the core techniques for training diffusion models, with the score function defined as the gradient of the log probability density ∇_x log p_t(x).
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11/2/2026
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VerifiedIn diffusion models, the score function is used to describe the log gradient of the data distribution and is trained via denoising score matching.80% similarUnverifiedTraditional score-matching-based generative models typically fix the diffusion coefficient of the forward process and learn only the score function (i.e., the drift term).74% similarVerifiedThe idea of flow matching is to learn a time-dependent vector field v_t(x) such that the ordinary differential equation driven by this vector field can transport a simple reference distribution to the target data distribution.73% similarUnverifiedThe target score identity expresses the score function as a self-consistent relation between the current model prediction and the gradient of the unnormalized density, making it possible to construct a training objective without external training data.72% similarUnverifiedSplit conformal prediction is a distribution-free uncertainty quantification method that fits a model on a training set, computes the quantile of nonconformity scores on a calibration set as a threshold, and provides a prediction interval for a test point, guaranteeing that the probability of the true value falling within the interval is no lower than a preset confidence level (e.g., 98.4%), with this guarantee depending only on data exchangeability.70% similar
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