Drifting with Gaussian kernels exactly matches score-matching on smoothed distributions via Tweedie's formula, while Laplace kernels approximate this closely in high dimensions.
Interpretation and Generalization of Score Matching
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abstract
Score matching is a recently developed parameter learning method that is particularly effective to complicated high dimensional density models with intractable partition functions. In this paper, we study two issues that have not been completely resolved for score matching. First, we provide a formal link between maximum likelihood and score matching. Our analysis shows that score matching finds model parameters that are more robust with noisy training data. Second, we develop a generalization of score matching. Based on this generalization, we further demonstrate an extension of score matching to models of discrete data.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Diffusion-based denoising score matching avoids the mode-separation degradation that affects vanilla score matching error bounds, via suitable hyperparameter choice.
citing papers explorer
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A Unified View of Score-Based and Drifting Models
Drifting with Gaussian kernels exactly matches score-matching on smoothed distributions via Tweedie's formula, while Laplace kernels approximate this closely in high dimensions.
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Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation
Diffusion-based denoising score matching avoids the mode-separation degradation that affects vanilla score matching error bounds, via suitable hyperparameter choice.