June 2019

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Wednesday, June 12, 2019 — 12:15 PM EDT

Alireza Heidari, PhD candidate
David R. Cheriton School of Computer Science

We introduce a few-shot learning framework for error detection. We show that data augmentation (a form of weak supervision) is key to training high-quality, ML-based error detection models that require minimal human involvement.

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