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Obtaining large annotated datasets is critical for training successful machine learning models and it is often a bottleneck in practice.
Maximum likelihood estimation of observer error-rates using the em algorithm
Alexander Philip Dawid and Allan M Skene · 1979
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Active learning for identifying function threshold boundaries
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Active learning by labeling features
Gregory Druck, Burr Settles, and Andrew McCallum · 2009
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Sebastian Riedel, Limin Yao, and Andrew McCallum · 2010
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Knowledge-based weak supervision for information extraction of overlapping relations
Raphael Hoffmann, Congle Zhang, Xiao Ling, Luke Zettlemoyer, and Daniel S Weld · 2011
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Iterative learning for reliable crowdsourcing systems
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
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Aggregating crowdsourced binary ratings
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Active learning for level set estimation
Alkis Gotovos · 2013
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Error rate analysis of labeling by crowdsourcing
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Using anchors to estimate clinical state without labeled data
Yoni Halpern, Youngduck Choi, Steven Horng, and David Sontag · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Spectral methods meet EM: A provably optimal algorithm for crowdsourcing
Yuchen Zhang, Xi Chen, Dengyong Zhou, and Michael I Jordan · 2014
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Introducing the ‘active search’method for iterative virtual screening
Roman Garnett, Thomas Gärtner, Martin Vogt, and Jürgen Bajorath · 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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Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
Ruining He and Julian McAuley · 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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Socratic learning: Augmenting generative models to incorporate latent subsets in training data
Paroma Varma, Bryan He, Dan Iter, Peng Xu, Rose Yu, Christopher De Sa, and Christopher Ré · 2016
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Learning the structure of generative models without labeled data
Stephen H Bach, Bryan He, Alexander Ratner, and Christopher Ré · 2017
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Neural ranking models with weak supervision
Mostafa Dehghani, Hamed Zamani, Aliaksei Severyn, Jaap Kamps, and W. Bruce Croft · 2017
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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
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Pairwise feedback for data programming
Benedikt Boecking and Artur Dubrawski · 2019
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Slice-based learning: A programming model for residual learning in critical data slices
Vincent Chen, Sen Wu, Alexander J Ratner, Jen Weng, and Christopher Ré · 2019
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Interactive programmatic labeling for weak supervision
Benjamin Cohen-Wang, Stephen Mussmann, Alex Ratner, and Chris Ré · 2019
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Bias in bios: A case study of semantic representation bias in a high-stakes setting
Maria De-Arteaga, Alexey Romanov, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, and Adam Tauman Kalai · 2019
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Classification of time sequences using graphs of temporal constraints
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Efficient nonmyopic active search
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Inferring generative model structure with static analysis
Paroma Varma, Bryan D He, Payal Bajaj, Nishith Khandwala, Imon Banerjee, Daniel Rubin, and Christopher Ré · 2017
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The power of ensembles for active learning in image classification
William H Beluch, Tim Genewein, Andreas Nürnberger, and Jan M Köhler · 2018
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Large-scale visual active learning with deep probabilistic ensembles
Kashyap Chitta, Jose M Alvarez, and Adam Lesnikowski · 2018
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Fidelity-weighted learning
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Training classifiers with natural language explanations
Braden Hancock, Martin Bringmann, Paroma Varma, Percy Liang, Stephanie Wang, and Christopher Ré · 2018
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Weakly supervised classification of aortic valve malformations using unlabeled cardiac mri sequences
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Training complex models with multi-task weak supervision
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Multi-resolution weak supervision for sequential data
Frederic Sala, Paroma Varma, Jason Fries, Daniel Y Fu, Shiori Sagawa, Saelig Khattar, Ashwini Ramamoorthy, Ke Xiao, Kayvon Fatahalian, James Priest, and Chris Re · 2019
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Learning dependency structures for weak supervision models
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Robust data programming with precision-guided labeling functions
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Cross-modal data programming enables rapid medical machine learning
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Weak supervision as an efficient approach for automated seizure detection in electroencephalography
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