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Active learning (AL) aims to enable training high performance classifiers with low annotation cost by predicting which subset of unlabelled instances would be most beneficial to label.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
Earlier work this paper cites.
Query learning strategies using boosting and bagging
Naoki Abe and Hiroshi Mamitsuka · 1998
Earlier work this paper cites.
Support vector machine active learning with applications to text classification
Simon Tong and Daphne Koller · 2002
Earlier work this paper cites.
Online choice of active learning algorithms
Yoram Baram, Ran El-Yaniv, and Kobi Luz · 2003
Earlier work this paper cites.
Active learning with gaussian processes for object categorization
Ashish Kapoor, Kristen Grauman, Raquel Urtasun, and Trevor Darrell · 2007
Earlier work this paper cites.
Policy search for motor primitives in robotics
Jens Kober and Jan R. Peters · 2009
Earlier work this paper cites.
Active learning by querying informative and representative examples
Sheng-jun Huang, Rong Jin, and Zhi-hua Zhou · 2010
Cited alongside, same era.
Batch mode active sampling based on marginal probability distribution matching
Rita Chattopadhyay, Zheng Wang, Wei Fan, Ian Davidson, Sethuraman Panchanathan, and Jieping Ye · 2012
Cited alongside, same era.
Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
Cited alongside, same era.
Active learning by learning
Wei-Ning Hsu and Hsuan-Tien Lin · 2015
Cited alongside, same era.
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
Cited alongside, same era.
Learning algorithms for active learning
Philip Bachman, Alessandro Sordoni, and Adam Trischler · 2017
Later among the works it cites.
Domain Adaptation in Computer Vision Applications
Gabriela Csurka · 2017
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Learning active learning from real and synthetic data
Ksenia Konyushkova, Raphael Sznitman, and Pascal Fua · 2017
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Diet networks: Thin parameters for fat genomics
Adriana Romero, Pierre Luc Carrier, Akram Erraqabi, Tristan Sylvain, Alex Auvolat, Etienne Dejoie, Marc-André Legault, Marie-Pierre Dube, Julie G. Hussin, and Yoshua Bengio · 2017
Later among the works it cites.
Mark Woodward and Chelsea Finn · 2017
Later among the works it cites.
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