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Found 3 papers, 1 papers with code
BLEURT Has Universal Translations: An Analysis of Automatic Metrics by Minimum Risk Training
In this study, we systematically analyze and compare various mainstream and cutting-edge automatic metrics from the perspective of their guidance for training machine translation systems.
Amortized Inference of Variational Bounds for Learning Noisy-OR
Classical approaches for approximate inference depend on cleverly designed variational distributions and bounds.
Active-set prediction for interior point methods using controlled perturbations
We also find that a primal-dual path-following algorithm applied to the perturbed problem is able to accurately predict the optimal active set of the original problem when the duality gap for the perturbed problem is not too small; furthermore, depending on problem conditioning, this prediction can happen sooner than predicting the active set for the perturbed problem or when the original one is solved.
Optimization and Control
