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A challenge in training discriminative models like neural networks is obtaining enough labeled training data.
Maximum likelihood estimation of observer error-rates using the EM algorithm
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Long short-term memory
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The unified medical language system (umls): integrating biomedical terminology
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Learning to extract relations from the web using minimal supervision
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Distant supervision for relation extraction without labeled data
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Sharp thresholds for high-dimensional and noisy sparsity recovery using
M. J. Wainwright · 2009
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High-dimensional Ising model selection using
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Modeling relations and their mentions without labeled text
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Harnessing the crowdsourcing power of social media for disaster relief
H. Gao, G. Barbier, and R. Goolsby · 2011
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Knowledge-based weak supervision for information extraction of overlapping relations
R. Hoffmann, C. Zhang, X. Ling, L. Zettlemoyer, and D. S. Weld · 2011
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Pattern learning for relation extraction with a hierarchical topic model
E. Alfonseca, K. Filippova, J.-Y. Delort, and G. Garrido · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Reducing wrong labels in distant supervision for relation extraction
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How transferable are features in deep neural networks?
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
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Comprehensive and reliable crowd assessment algorithms
M. Joglekar, H. Garcia-Molina, and A. Parameswaran · 2015
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Crowdflower dataset: Airline twitter sentiment, 2015
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Incremental knowledge base construction using DeepDive
J. Shin, S. Wu, F. Wang, C. De Sa, C. Zhang, and C. Ré · 2015
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Overview of the BioCreative V chemical disease relation (CDR) task
C.-H. Wei, Y. Peng, R. Leaman, A. P. Davis, C. J. Mattingly, J. Li, T. C. Wiegers, and Z. Lu · 2015
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Learning from massive noisy labeled data for image classification
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Combining generative and discriminative model scores for distant supervision
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Exploiting commonality and interaction effects in crowdsourcing tasks using latent factor models
P. Ruvolo, J. Whitehill, and J. R. Movellan · 2013
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Microsoft COCO: Common objects in context
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Training deep neural networks on noisy labels with bootstrapping
S. Reed, H. Lee, D. Anguelov, C. Szegedy, D. Erhan, and A. Rabinovich · 2014
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T. Xiao, T. Xia, Y. Yang, C. Huang, and X. Wang · 2015
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Data programming: Creating large training sets, quickly
A. J. Ratner, C. M. De Sa, S. Wu, D. Selsam, and C. Ré · 2016
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Spectral methods meet em: A provably optimal algorithm for crowdsourcing
Y. Zhang, X. Chen, D. Zhou, and M. I. Jordan · 2016
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Learning the structure of generative models without labeled data
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The comparative toxicogenomics database: update 2017
A. P. Davis, C. J. Grondin, R. J. Johnson, D. Sciaky, B. L. King, R. McMorran, J. Wiegers, T. C. Wiegers, and C. J. Mattingly · 2017
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Label-free supervision of neural networks with physics and domain knowledge
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