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Autoregressive Score Matching
[article]
2020
arXiv
pre-print
Autoregressive models use chain rule to define a joint probability distribution as a product of conditionals. These conditionals need to be normalized, imposing constraints on the functional families that can be used. To increase flexibility, we propose autoregressive conditional score models (AR-CSM) where we parameterize the joint distribution in terms of the derivatives of univariate log-conditionals (scores), which need not be normalized. To train AR-CSM, we introduce a new divergence
arXiv:2010.12810v1
fatcat:axo4u3wxyrcanggv35ews7apwq