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About this paper
Siwei Lyu
Establishes a formal link between score matching and maximum likelihood estimation, interpreting score matching as finding parameters robust to noise. The work also generalizes score matching to a broader framework, enabling its application to discrete data models.
Key insights
- Score matching can be interpreted as minimizing a derivative of the Kullback-Leibler divergence, suggesting its estimators are robust to small data perturbations.
- Generalizing the core operator from a gradient to an arbitrary linear operator (L) extends score matching's theoretical reach and practical applicability.
- For discrete data, utilizing a marginalization operator within the generalized framework provides a tractable objective for parameter estimation.
Summary by alphaXiv.
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