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Found 4 papers, 4 papers with code
Bayesian Quantification with Black-Box Estimators
Understanding how different classes are distributed in an unlabeled data set is an important challenge for the calibration of probabilistic classifiers and uncertainty quantification.
Follow-up Attention: An Empirical Study of Developer and Neural Model Code Exploration
However, it often remains unclear how the models actually process code, and to what extent their reasoning and the way their attention mechanism scans the code matches the patterns of developers.
Productivity Assessment of Neural Code Completion
Neural code synthesis has reached a point where snippet generation is accurate enough to be considered for integration into human software development workflows.
Unsupervised Recalibration
Unsupervised recalibration (URC) is a general way to improve the accuracy of an already trained probabilistic classification or regression model upon encountering new data while deployed in the field.
