How to Know When Machine Learning Does Not Now
It is becoming increasingly important to understand how a prediction made by a Machine Learning model is informed by its training data.
adversarial-learning interpretability uncertainty adversarial-examples active-learning outlier-detection anomaly-detection semi-supervised-learning tutorial article research paper arxiv:1803.04765

This post will outline an approach we call Deep k-Nearest Neighbors [Papernot and McDaniel] that attempts to address this issue. We will also explore a loss function we recently introduced to shine some light on how models structure their internal representations [Frosst et al.]. We’ll illustrate how the Deep k-Nearest Neighbors (DkNN) helps recognize data that is not from the training distribution by observing anomalies in the internal representation of such data.

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Research Engineer @ Google -- Singer @ Good Kid
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