Search Results for author:
Found 4 papers, 3 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.
Extracting Meaningful Attention on Source Code: An Empirical Study of Developer and Neural Model Code Exploration
The high effectiveness of neural models of code, such as OpenAI Codex and AlphaCode, suggests coding capabilities of models that are at least comparable to those of humans.
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.
