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The availability of labelled data is one of the main limitations in machine learning.
GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014
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Tubespam: Comment spam filtering on youtube
T. C. Alberto, J. V. Lochter, and T. A. Almeida · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Visual relationship detection with language priors
Cewu Lu, Ranjay Krishna, Michael Bernstein, and Li Fei-Fei · 2016
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Data programming: Creating large training sets, quickly
Alexander Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, and Christopher Ré · 2016
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Snorkel: Rapid training data creation with weak supervision
Alexander Ratner, Stephen H Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré · 2017
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Hybridization of Active Learning and Data Programming for Labeling Large Industrial Datasets
Mona Nashaat, Aindrila Ghosh, James Miller, Shaikh Quader, Chad Marston, and Jean Francois Puget · 2018
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Snuba: Automating weak supervision to label training data
Paroma Varma and Christopher Ré · 2018
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A brief introduction to weakly supervised learning
Zhi Hua Zhou · 2018
Cited alongside, same era.
Snorkel Drybell: A case study in deploying weak supervision at industrial scale
Stephen H. Bach, Daniel Rodriguez, Yintao Liu, Chong Luo, Haidong Shao, Cassandra Xia, Souvik Sen, Alex Ratner, Braden Hancock, Houman Alborzi, Rahul Kuchhal, Chris Ré, and Rob Malkin · 2019
Cited alongside, same era.
Weak Supervision for Learning Discourse Structure
Sonia Badene, Kate Thompson, Jean-Pierre Lorré, and Nicholas Asher · 2019
Cited alongside, same era.
Scene graph prediction with limited labels
Vincent Chen, Paroma Varma, Ranjay Krishna, Michael Bernstein, Christophe Re, and Li Fei Fei · 2019
Cited alongside, same era.
Interactive Programmatic Labeling for Weak Supervision
Benjamin Cohen-Wang, Stephen Mussmann, Alex Ratner, and Chris Ré · 2019
Learning dependency structures for weak supervision models
Paroma Varma, Frederic Sala, Ann He, Alexander Ratner, and Christopher Ré · 2019
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Learning from rules generalizing labeled exemplars
Abhijeet Awasthi, Sabyasachi Ghosh, Rasna Goyal, and Sunita Sarawagi · 2020
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Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling
Benedikt Boecking, Willie Neiswanger, Eric Xing, and Artur Dubrawski · 2020
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Active and Incremental Learning with Weak Supervision
Clemens-Alexander Brust, Christoph Käding, and Joachim Denzler · 2020
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Cross-Modal Data Programming Enables Rapid Medical Machine Learning
Jared A Dunnmon, Alexander J Ratner, Khaled Saab, Nishith Khandwala, Matthew Markert, Hersh Sagreiya, Roger Goldman, Christopher Lee-Messer, Matthew P Lungren, Daniel L Rubin, and Christopher Ré · 2020
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Cited alongside, same era.
Training Complex Models with Multi-Task Weak Supervision
Alexander Ratner, Braden Hancock, Jared Dunnmon, Frederic Sala, Shreyash Pandey, and Christopher Ré · 2019
Cited alongside, same era.
Asterisk: Generating Large Training Datasets with Automatic Active Supervision
Mona Nashaat, Aindrila Ghosh, James Miller, and Shaikh Quader
Cited in the paper.
Wesal: Applying active supervision to find high-quality labels at industrial scale
Mona Nashaat, Aindrila Ghosh, James Miller, and Shaikh Quader
Cited in the paper.
WeakAL: Combining Active Learning and Weak Supervision
Julius Gonsior, Maik Thiele, and Wolfgang Lehner · 2020
Later among the works it cites.