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Harvard University
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Columbia University
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Harvard University
Massachusetts Institute of Technology
Yale University
Columbia University
Columbia University
University of Hartford
Princeton University
Stanford University
Cornell University
New York University
Harvard University
Massachusetts Institute of Technology
Yale University
Columbia University
Columbia University
University of Hartford
Princeton University
Stanford University
Cornell University
New York University
Harvard University
Massachusetts Institute of Technology
Yale University
Columbia University
Columbia University
University of Hartford
Princeton University
Stanford University
Cornell University
New York University

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instead we simply use independent logistic classifiers. Dur- ing training we use binary cross-entropy loss for the class predictions. (Baevski at al, 2020)

This formulation helps when we move to more complex domains like the

We use a simple approach to handle the multi-label case: (Smith et al, 2021)

Inline citations

instead we simply use independent logistic classifiers. Dur- ing training we use binary cross-entropy loss for the class predictions.

Footnotes

we use binary cross-entropy loss for the class predictions.

References

References

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This formulation helps

Instead we simply use independent logistic classifiers. During training we use binary

This formulation helps

Instead we simply use independent logistic classifiers. During training we use binary

This formulation helps

Instead we simply use independent logistic classifiers. During training we use binary

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