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eccd2a86bae4728b38627162ba297828-Paper.pdf

Neural Information Processing Systems

In contrast, we show that the computation of one PI-explanation for an NBC can be achieved in log-linear time, and that the same result also applies to the more general class of linear classifiers. Furthermore, we show that the enumeration ofPI-explanations can beobtained with polynomial delay. Experimental results demonstrate the performance gains ofthe newalgorithms when compared with earlierwork.


TowardsTrustworthyAutomaticDiagnosisSystemsby EmulatingDoctors'ReasoningwithDeep ReinforcementLearning

Neural Information Processing Systems

Moreover,doctors explicitly explore severepathologies before potentially ruling them out from the differential, especially in acute care settings. Finally, for doctors to trust a system's recommendations, they need to understand how the gathered evidences led to the predicted diseases.




SurDis: ASurfaceDiscontinuityDatasetforWearable TechnologytoAssistBlindNavigationinUrban Environments

Neural Information Processing Systems

With feedbackfromthesevolunteers,wedevelopedalightweight,smallandunobtrusive prototype equipped with a tiny stereo camera and an embedded system on a single board computer to capture the samples from 10 different locations. We describe instrument development, datacollection, preprocessing, annotation, and experiments conducted.