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To reduce the human annotation efforts, the programmatic weak supervision (PWS) paradigm abstracts weak supervision sources as labeling functions (LFs) and involves a label model to aggregate the output of multiple LFs to produce training labels.
Maximum likelihood estimation of observer error-rates using the em algorithm
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A Modern Introduction to Probability and Statistics: Understanding why and how
Frederik Michel Dekking, Cornelis Kraaikamp, Hendrik Paul Lopuhaä, and Ludolf Erwin Meester · 2005
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Distant supervision for relation extraction without labeled data
Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky · 2009
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Active learning
Burr Settles · 2012
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Incremental knowledge base construction using deepdive
Jaeho Shin, Sen Wu, Feiran Wang, Christopher De Sa, Ce Zhang, and Christopher Ré · 2015
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On large-batch training for deep learning: Generalization gap and sharp minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 2016
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Data programming: Creating large training sets, quickly
Alexander J Ratner, Christopher M De Sa, Sen Wu, Daniel Selsam, and Christopher Ré · 2016
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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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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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
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Truth inference in crowdsourcing: Is the problem solved?
Yudian Zheng, Guoliang Li, Yuanbing Li, Caihua Shan, and Reynold Cheng · 2017
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Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson · 2018
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Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2018
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Fonduer: Knowledge base construction from richly formatted data
Sen Wu, Luke Hsiao, Xiao Cheng, Braden Hancock, Theodoros Rekatsinas, Philip Levis, and Christopher Ré · 2018
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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, et al · 2019
Cited alongside, same era.
Weakly supervised classification of aortic valve malformations using unlabeled cardiac mri sequences
Jason A Fries, Paroma Varma, Vincent S Chen, Ke Xiao, Heliodoro Tejeda, Priyanka Saha, Jared Dunnmon, Henry Chubb, Shiraz Maskatia, Madalina Fiterau, et al · 2019
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Exploiting worker correlation for label aggregation in crowdsourcing
Yuan Li, Benjamin Rubinstein, and Trevor Cohn · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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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.
Deep double descent: Where bigger models and more data hurt
Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, and Ilya Sutskever · 2021
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A gentle introduction to graph neural networks
Benjamin Sanchez-Lengeling, Emily Reif, Adam Pearce, and Alexander B Wiltschko · 2021
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Demonstration of panda: A weakly supervised entity matching system
Renzhi Wu, Prem Sakala, Peng Li, Xu Chu, and Yeye He · 2021
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Wrench: A comprehensive benchmark for weak supervision
Jieyu Zhang, Yue Yu, , Yujing Wang, Yaming Yang, Mao Yang, and Alexander Ratner · 2021
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URL https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
sklearn.ensemble.RandomForestClassifier, May 2022 · 2022
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yu-aistats22-code, May 2022a
BatsResearch · 2022
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On the convergence of adam and beyond
Sashank J Reddi, Satyen Kale, and Sanjiv Kumar · 2019
Cited alongside, same era.
Goggles: Automatic image labeling with affinity coding
Nilaksh Das, Sanya Chaba, Renzhi Wu, Sakshi Gandhi, Duen Horng Chau, and Xu Chu · 2020
Cited alongside, same era.
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, et al · 2020
Cited alongside, same era.
Fast and three-rious: Speeding up weak supervision with triplet methods
Daniel Fu, Mayee Chen, Frederic Sala, Sarah Hooper, Kayvon Fatahalian, and Christopher Ré · 2020
Cited alongside, same era.
A survey on contrastive self-supervised learning
Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Debapriya Banerjee, and Fillia Makedon · 2020
Cited alongside, same era.
Named entity recognition without labelled data: A weak supervision approach
Pierre Lison, Jeremy Barnes, Aliaksandr Hubin, and Samia Touileb · 2020
Cited alongside, same era.
Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 2020
Cited alongside, same era.
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mazzetto-icml21-code, September 2022b
BatsResearch · 2022
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flyingsquid, April 2022
HazyResearch · 2022
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DeepSets, November 2022
manzilzaheer · 2022
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snorkel-extraction, April 2022a
snorkel team · 2022
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snorkel, April 2022b
snorkel team · 2022
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Fast-Dawid-Skene, April 2022
sukrutrao · 2022
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Learning from multiple noisy partial labelers
Peilin Yu, Tiffany Ding, and Stephen H. Bach · 2022
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URL https://github.com/yuan-li/truth-inference-at-scale
yuan li, May 2022 · 2022
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Wrench Project Homepage, 2022a
Jieyu Zhang · 2022
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Wrench Github Issue. ”Question on train/val/test split when evaluating label model.”, 2022b
Jieyu Zhang · 2022
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A survey on programmatic weak supervision
Jieyu Zhang, Cheng-Yu Hsieh, Yue Yu, Chao Zhang, and Alexander Ratner · 2022
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