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            End-to-End Weak Supervision
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            End-to-End Weak Supervision

            Dec 6, 2021

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            Salva Rühling Cachay

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            Benedikt Boecking

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            Artur Dubrawski

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            About

            Aggregating multiple sources of weak supervision (WS) can ease the data-labeling bottleneck prevalent in many machine learning applications, by replacing the tedious manual collection of ground truth labels. Current state of the art approaches that do not use any labeled training data, however, require two separate modeling steps: Learning a probabilistic latent variable model based on the WS sources – making assumptions that rarely hold in practice – followed by downstream model training. Impor…

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            NeurIPS 2021

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