ProSeNet learns a sparse set of prototypes for case-based explanations in deep sequence models, matches state-of-the-art accuracy on several tasks, and supports manual prototype refinement by non-experts.
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2019 2verdicts
UNVERDICTED 2representative citing papers
Bayesian neural networks are used on EHR data to quantify prediction uncertainty from data noise, with experiments showing high-uncertainty cases degrade performance and can identify patients for data-quality intervention.
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Interpretable and Steerable Sequence Learning via Prototypes
ProSeNet learns a sparse set of prototypes for case-based explanations in deep sequence models, matches state-of-the-art accuracy on several tasks, and supports manual prototype refinement by non-experts.
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Modeling the Uncertainty in Electronic Health Records: a Bayesian Deep Learning Approach
Bayesian neural networks are used on EHR data to quantify prediction uncertainty from data noise, with experiments showing high-uncertainty cases degrade performance and can identify patients for data-quality intervention.