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Found 3 papers, 2 papers with code
Feature Importance Explanations for Temporal Black-Box Models
Models in the supervised learning framework may capture rich and complex representations over the features that are hard for humans to interpret.
Understanding Learned Models by Identifying Important Features at the Right Resolution
In many application domains, it is important to characterize how complex learned models make their decisions across the distribution of instances.
Multiple-Instance Active Learning
In particular, we consider the case in which an MI learner is allowed to selectively query unlabeled instances in positive bags.
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