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An interpretable imbalanced semi-supervised deep learning framework for improving differential diagnosis of skin diseases
This paper presents the first study of the interpretability and imbalanced semi-supervised learning of the multiclass intelligent skin diagnosis framework (ISDL) using 58, 457 skin images with 10, 857 unlabeled samples.
Permutation-based tests for discontinuities in event studies
Under a high-level condition that the observed data can be coupled by a collection of conditionally independent variables, we establish the asymptotic validity of the permutation test, allowing the sizes of the local subsamples to be either be fixed or grow to infinity.
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