#ml #snorkel
However, we can model this noise by learning a generative model of the labeling process, effectively synthesizing the labels created by the labeling functions. We can then use this new label set to train a noise-aware end discriminative model (such as a neural network in TensorFlow) with higher accuracy.
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Ratner_et_al-2017-dawn,cs,stanford,edu-Snorkel_and_The_Dawn, users focus on writing a set of labeling functions, which are just small functions that programmatically label data. The labels that they produce are noisy and could conflict with each other. <span>However, we can model this noise by learning a generative model of the labeling process, effectively synthesizing the labels created by the labeling functions. We can then use this new label set to train a noise-aware end discriminative model (such as a neural network in TensorFlow) with higher accuracy. This framework allow users to easily “program” machine learning models with high-level functions, and leverage whatever code, domain heuristics, or data resources they have at hand. And Summary
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