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Active learning is an effective technique for reducing the labeling cost by improving data efficiency.
Information-based objective functions for active data selection
D. J. MacKay · 1992
Earlier work this paper cites.
A sequential algorithm for training text classifiers
D. D. Lewis and W. A. Gale · 1994
Earlier work this paper cites.
Semi-supervised learning using gaussian fields and harmonic functions
X. Zhu, Z. Ghahramani, and J. D. Lafferty · 2003
Earlier work this paper cites.
Geometric approximation via coresets
P. K. Agarwal, S. Har-Peled, K. R. Varadarajan, et al · 2005
Earlier work this paper cites.
A semi-supervised active learning framework for image retrieval
S. C. H. Hoi and M. R. Lyu · 2005
Earlier work this paper cites.
Batch mode active learning and its application to medical image classification
S. C. Hoi, R. Jin, J. Zhu, and M. R. Lyu · 2006
Earlier work this paper cites.
Margin based active learning
M.-F. Balcan, A. Broder, and T. Zhang · 2007
Earlier work this paper cites.
Discriminative batch mode active learning
Y. Guo and D. Schuurmans · 2008
Earlier work this paper cites.
Visualizing data using t-sne
L. v. d. Maaten and G. Hinton · 2008
Earlier work this paper cites.
Combining active learning and semi-supervised learning to construct svm classifier
Y. Leng, X. Xu, and G. Qi · 2013
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Adam: A method for stochastic gradient descent
D. P. Kingma and J. L. Ba · 2015
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Semi-supervised learning with ladder networks
A. Rasmus, M. Berglund, M. Honkala, H. Valpola, and T. Raiko · 2015
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Free spoken digit dataset, 2016
Z. Jackson · 2016
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Hyperparameter optimization with approximate gradient
F. Pedregosa · 2016
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S. Zagoruyko and N. Komodakis · 2016
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Active learning for convolutional neural networks: A core-set approach
O. Sener and S. Savarese · 2018
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Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
P. Warden · 2018
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Mixmatch: A holistic approach to semi-supervised learning
D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. A. Raffel · 2019
Later among the works it cites.
Consistency-based semi-supervised active learning: Towards minimizing labeling cost
M. Gao, Z. Zhang, G. Yu, S. O. Arik, L. S. Davis, and T. Pfister · 2019
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Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning
A. Kirsch, J. van Amersfoort, and Y. Gal · 2019
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O. Bachem, M. Lucic, and A. Krause · 2017
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Neural tangent kernel: Convergence and generalization in neural networks
A. Jacot, F. Gabriel, and C. Hongler · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
T. Miyato, S.-i. Maeda, M. Koyama, and S. Ishii · 2018
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S. Song, D. Berthelot, and A. Rostamizadeh · 2019
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Coresets via bilevel optimization for continual learning and streaming
Z. Borsos, M. Mutný, and A. Krause · 2020
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Neural tangents: Fast and easy infinite neural networks in python
R. Novak, L. Xiao, J. Hron, J. Lee, A. A. Alemi, J. Sohl-Dickstein, and S. S. Schoenholz · 2020
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