Estimates probabilistic labels by learning a generative label model from the provided noisy labels. This node is a key component for the realization of weak supervision approaches as popularized by Snorkel . The idea in weak supervision is that it is often possible to create a number of simple inaccurate models (e.g. simple rules or existing models for slightly different tasks) that can label unlabeled data and that the agreements and disagreements of these simple models can be analyzed to infer information of the true label. Our implementation is a TensorFlow based adaptation of the matrix completion approach proposed in this paper by the Snorkel team. We refer to the publication for details on the strategy.
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