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In this work, we propose the use of a fully managed machine learning service, which utilizes active learning to directly build models from unstructured data.
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Information-based objective functions for active data selection
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Multiclass least squares support vector machines
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Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov · 2017
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Adaptive low-rank multi-label active learning for image classification
Jian Wu, Anqian Guo, Victor S. Sheng, Pengpeng Zhao, Zhiming Cui, and Hua Li · 2017
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Active learning: an empirical study of common baselines
Maria Eugenia Ramirez-Loaiza, Manali Sharma, Geet Kumar, and Mustafa Bilgic · 2017
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Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
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Universal sentence encoder, 2018
Daniel Cer, Yinfei Yang, Sheng yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Yun-Hsuan Sung, Brian Strope, and Ray Kurzweil · 2018
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Glove: Global vectors for word representation
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Microservices
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Michael Bloodgood · 2018
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Deep bayesian active learning for natural language processing: Results of a large-scale empirical study
Aditya Siddhant and Zachary C. Lipton · 2018
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Learning loss for active learning
Donggeun Yoo and In So Kweon · 2019
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