Score-augmented loss functions for neural likelihood surrogates in SBI deliver downstream inference performance equivalent to 10x more training data at under 1.1x training time cost on network and spatial process models.
Curran Associates Inc., Red Hook, NY , USA
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Neural networks learn to construct argumentation structures that explain classifications through support and attack relations, trained jointly with differentiable semantics and structure constraints.
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Keeping Score: Efficiency Improvements in Neural Likelihood Surrogate Training via Score-Augmented Loss Functions
Score-augmented loss functions for neural likelihood surrogates in SBI deliver downstream inference performance equivalent to 10x more training data at under 1.1x training time cost on network and spatial process models.