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Creating labeled training sets has become one of the major roadblocks in machine learning.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 1908
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Studying the live cross-platform circulation of images with computer vision api: An experiment based on a sports media event
Carlos d’Andrea and André Mintz · 1932
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The mathematics of statistical machine translation: Parameter estimation
Peter F Brown, Stephen A Della Pietra, Vincent J Della Pietra, and Robert L Mercer · 1993
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Wordnet: a lexical database for english
George A Miller · 1995
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Conditional random fields for object recognition
Ariadna Quattoni, Michael Collins, and Trevor Darrell · 2004
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Learning to detect unseen object classes by between-class attribute transfer
Christoph H Lampert, Hannes Nickisch, and Stefan Harmeling · 2009
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Structured output learning with indirect supervision
Ming-Wei Chang, Vivek Srikumar, Dan Goldwasser, and Dan Roth · 2010
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Devise: A deep visual-semantic embedding model
Andrea Frome, Greg S Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Marc' Aurelio Ranzato, and Tomas Mikolov · 2013
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Attribute-based classification for zero-shot visual object categorization
Christoph H Lampert, Hannes Nickisch, and Stefan Harmeling · 2013
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Learning dependency-based compositional semantics
Percy Liang, Michael I Jordan, and Dan Klein · 2013
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Large-scale object classification using label relation graphs
Jia Deng, Nan Ding, Yangqing Jia, Andrea Frome, Kevin Murphy, Samy Bengio, Yuan Li, Hartmut Neven, and Hartwig Adam · 2014
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Parameter identifiability of discrete bayesian networks with hidden variables
Elizabeth S Allman, John A Rhodes, Elena Stanghellini, and Marco Valtorta · 2015
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LSHTC: A benchmark for large-scale text classification
Ioannis Partalas, Aris Kosmopoulos, Nicolas Baskiotis, Thierry Artières, George Paliouras, Éric Gaussier, Ion Androutsopoulos, Massih-Reza Amini, and Patrick Gallinari · 2015
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An embarrassingly simple approach to zero-shot learning
Bernardino Romera-Paredes and Philip Torr · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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An overview of microsoft academic service (mas) and applications
Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June Paul Hsu, and Kuansan Wang · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Estimation from indirect supervision with linear moments
Aditi Raghunathan, Roy Frostig, John Duchi, and Percy Liang · 2016
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Data programming: Creating large training sets, quickly
A. J. Ratner, Christopher M. De Sa, Sen Wu, Daniel Selsam, and C. Ré · 2016
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Learning the structure of generative models without labeled data
Stephen H. Bach, Bryan He, Alexander J. Ratner, and Christopher Ré · 2017
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Finer grained entity typing with typenet
Shikhar Murty, Pat Verga, L. Vilnis, and A. McCallum · 2017
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Complexity vs. performance: Empirical analysis of machine learning as a service
Yuanshun Yao, Zhujun Xiao, Bolun Wang, Bimal Viswanath, Haitao Zheng, and Ben Y. Zhao · 2017
Generating multi-agent trajectories using programmatic weak supervision
Eric Zhan, Stephan Zheng, Yisong Yue, Long Sha, and Patrick Lucey · 2019
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Learning from indirect observations, 2019
Yivan Zhang, Nontawat Charoenphakdee, and Masashi Sugiyama · 2019
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Frugalml: How to use ml prediction apis more accurately and cheaply
Lingjiao Chen, Matei Zaharia, and James Zou · 2020
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Autoknow: Self-driving knowledge collection for products of thousands of types
Xin Luna Dong, Xiang He, Andrey Kan, Xian Li, Yan Liang, Jun Ma, Yifan Ethan Xu, Chenwei Zhang, Tong Zhao, Gabriel Blanco Saldana, et al · 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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Who said what: Modeling individual labelers improves classification
Melody Y. Guan, Varun Gulshan, Andrew M. Dai, and Geoffrey E. Hinton · 2018
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Learning from noisy singly-labeled data
Ashish Khetan, Zachary C. Lipton, and Anima Anandkumar · 2018
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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 · 2018
Cited alongside, same era.
Zero-shot learning of classifiers from natural language quantification
Shashank Srivastava, Igor Labutov, and Tom Mitchell · 2018
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The Gene Ontology Resource: 20 years and still GOing strong
The Gene Ontology Consortium · 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.
Fast and three-rious: Speeding up weak supervision with triplet methods
Daniel Y. Fu, Mayee F. Chen, Frederic Sala, Sarah M. Hooper, Kayvon Fatahalian, and Christopher Ré · 2020
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Named entity recognition without labelled data: A weak supervision approach
Pierre Lison, Jeremy Barnes, Aliaksandr Hubin, and Samia Touileb · 2020
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Few-shot relation extraction via bayesian meta-learning on relation graphs
Meng Qu, Tianyu Gao, Louis-Pascal Xhonneux, and Jian Tang · 2020
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Weakly supervised sequence tagging from noisy rules
Esteban Safranchik, Shiying Luo, and Stephen Bach · 2020
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Learnability with indirect supervision signals
Kaifu Wang, Qiang Ning, and Dan Roth · 2020
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Co-tuning for transfer learning
Kaichao You, Zhi Kou, Mingsheng Long, and Jianmin Wang · 2020
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Nero: A neural rule grounding framework for label-efficient relation extraction
Wenxuan Zhou, Hongtao Lin, Bill Yuchen Lin, Ziqi Wang, Junyi Du, Leonardo Neves, and Xiang Ren · 2020
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Ontology-driven weak supervision for clinical entity classification in electronic health records
Jason A Fries, Ethan Steinberg, Saelig Khattar, Scott L Fleming, Jose Posada, Alison Callahan, and Nigam H Shah · 2021
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Cut out the annotator, keep the cutout: better segmentation with weak supervision
Sarah Hooper, Michael Wornow, Ying Hang Seah, Peter Kellman, Hui Xue, Frederic Sala, Curtis Langlotz, and Christopher Re · 2021
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Wrench: A comprehensive benchmark for weak supervision
Jieyu Zhang, Yue Yu, Yinghao Li, Yujing Wang, Yaming Yang, Mao Yang, and Alexander Ratner · 2021
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