110 citations · 174 across the 11 of their papers we have counts for
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cs.IR2020
Coarse-to-Fine Memory Matching for Joint Retrieval and Classification
Allen Schmaltz, Andrew Beam
We present a novel end-to-end language model for joint retrieval and classification, unifying the strengths of bi- and cross- encoders into a single language model via a coarse-to-…
cs.LG2020★ 2 cited
Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures
Benjamin Kompa, Jasper Snoek, Andrew Beam
Uncertainty quantification for complex deep learning models is increasingly important as these techniques see growing use in high-stakes, real-world settings. Currently, the qualit…
cs.CL2020★ 7 cited
Exemplar Auditing for Multi-Label Biomedical Text Classification
Allen Schmaltz, Andrew Beam
Many practical applications of AI in medicine consist of semi-supervised discovery: The investigator aims to identify features of interest at a resolution more fine-grained than th…