Snorkel

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Snorkel: Beyond Hand-labeled Data

This talk describes Snorkel, a software system whose goal is to make routine machine learning tasks dramatically easier. Snorkel focuses on a key bottleneck in the development of machine learning systems: the lack of large training datasets for a user’s task. In Snorkel, a user implicitly defines large training sets by writing simple programs that create labeled data, instead of tediously hand-...

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Snorkel: A System for Lightweight Extraction

We describe a vision and an initial prototype system for extracting structured data from unstructured or dark input sources–such as text, embedded tables, images, and diagrams–called Snorkel, in which users write traditional extraction scripts which are automatically enhanced by machine learning techniques. The key technical idea is to view the user’s actions with standard tools as implicitly d...

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Are oxygen uptake kinetics modified when using a respiratory snorkel?

PURPOSE The aim of this study was to compare VO2 kinetics during constant power cycle exercise measured using a conventional facemask (CM) or a respiratory snorkel (RS) designed for breath-by-breath analysis in swimming. METHODS VO2 kinetics parameters-obtained using CM or RS, in randomized counterbalanced order-were compared in 10 trained triathletes performing two submaximal heavy-intensity...

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Wave Characteristics in Breaststroke Technique with and Without Snorkel Use

The purpose of this paper was to examine the characteristics of waves generated when swimming with and without the use of Aquatrainer® snorkels. Eight male swimmers performed two maximal bouts of 25 m breaststroke, first without the use of a snorkel (normal condition) and then using a snorkel (snorkel condition). The body landmarks, centre of the mass velocity, stroke rate, stroke length, strok...

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Snorkel: Rapid Training Data Creation with Weak Supervision

Labeling training data is increasingly the largest bottleneck in deploying machine learning systems. We present Snorkel, a first-of-its-kind system that enables users to train stateof-the-art models without hand labeling any training data. Instead, users write labeling functions that express arbitrary heuristics, which can have unknown accuracies and correlations. Snorkel denoises their outputs...

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ژورنال

عنوان ژورنال: Proceedings of the VLDB Endowment

سال: 2017

ISSN: 2150-8097

DOI: 10.14778/3157794.3157797