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Active learning can be defined as iterations of data labeling, model training, and data acquisition, until sufficient labels are acquired.
Diverse mini-batch active learning
Zhdanov, F. 2019 · 1901
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A sequential algorithm for training text classifiers
Lewis, D. D.; and Gale, W. A. 1994 · 1994
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LSTM can solve hard long time lag problems
Hochreiter, S.; and Schmidhuber, J. 1997 · 1997
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A mathematical theory of communication
Shannon, C. E. 2001 · 2001
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Question-answering via enhanced understanding of questions
Roth, D.; Cumby, C.; Li, X.; Morie, P.; Nagarajan, R.; Rizzolo, N.; Small, K.; and Yih, W.-t. 2002 · 2002
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Representative sampling for text classification using support vector machines
Xu, Z.; Yu, K.; Tresp, V.; Xu, X.; and Wang, J. 2003 · 2003
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Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Pang, B.; and Lee, L. 2005 · 2005
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A Survey of Deep Active Learning
Ren, P.; Xiao, Y.; Chang, X.; Huang, P.-Y.; Li, Z.; Chen, X.; and Wang, X. 2020 · 2009
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Active learning literature survey
Settles, B. 2009 · 2009
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Off to a good start: Using clustering to select the initial training set in active learning
Hu, R.; Mac Namee, B.; and Delany, S. J. 2010 · 2010
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Active learning with clustering
Bodó, Z.; Minier, Z.; and Csató, L. 2011 · 2011
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Two faces of active learning
Dasgupta, S. 2011 · 2011
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Recursive deep models for semantic compositionality over a sentiment treebank
Socher, R.; Perelygin, A.; Wu, J.; Chuang, J.; Manning, C. D.; Ng, A. Y.; and Potts, C. 2013 · 2013
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A new active labeling method for deep learning
Wang, D.; and Shang, Y. 2014 · 2014
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Distilling the knowledge in a neural network
Hinton, G.; Vinyals, O.; and Dean, J. 2015 · 2015
Cited alongside, same era.
Cost-effective active learning for deep image classification
Wang, K.; Zhang, D.; Li, Y.; Zhang, R.; and Lin, L. 2016 · 2016
Cited alongside, same era.
Deep Active Learning for Dialogue Generation
Asghar, N.; Poupart, P.; Jiang, X.; and Li, H. 2017 · 2017
Cited alongside, same era.
Efficient Knowledge Distillation from an Ensemble of Teachers
Fukuda, T.; Suzuki, M.; Kurata, G.; Thomas, S.; Cui, J.; and Ramabhadran, B. 2017 · 2017
Cited alongside, same era.
Deep active learning for image classification
Ranganathan, H.; Venkateswara, H.; Chakraborty, S.; and Panchanathan, S. 2017 · 2017
Cited alongside, same era.
Agreeing to disagree: Active learning with noisy labels without crowdsourcing
Sampling Bias in Deep Active Classification: An Empirical Study
Prabhu, A.; Dognin, C.; and Singh, M. 2019 · 2019
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An empirical study of example forgetting during deep neural network learning
Toneva, M.; Sordoni, A.; Combes, R. T. d.; Trischler, A.; Bengio, Y.; and Gordon, G. J. 2019 · 2019
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On Warm-Starting Neural Network Training
Ash, J.; and Adams, R. P. 2020 · 2020
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On Statistical Bias In Active Learning: How and When to Fix It
Farquhar, S.; Gal, Y.; and Rainforth, T. 2020 · 2020
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Stopping criterion for active learning based on deterministic generalization bounds
Ishibashi, H.; and Hino, H. 2020 · 2020
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Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics
Swayamdipta, S.; Schwartz, R.; Lourie, N.; Wang, Y.; Hajishirzi, H.; Smith, N. A.; and Choi, Y. 2020 · 2020
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Bouguelia, M.-R.; Nowaczyk, S.; Santosh, K.; and Verikas, A. 2018 · 2018
Cited alongside, same era.
Born again neural networks
Furlanello, T.; Lipton, Z.; Tschannen, M.; Itti, L.; and Anandkumar, A. 2018 · 2018
Cited alongside, same era.
Practical obstacles to deploying active learning
Lowell, D.; Lipton, Z. C.; and Wallace, B. C. 2018 · 2018
Cited alongside, same era.
Active learning for convolutional neural networks: A core-set approach
Sener, O.; and Savarese, S. 2018 · 2018
Cited alongside, same era.
Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study
Siddhant, A.; and Lipton, Z. C. 2018 · 2018
Cited alongside, same era.
Deep batch active learning by diverse, uncertain gradient lower bounds
Ash, J. T.; Zhang, C.; Krishnamurthy, A.; Langford, J.; and Agarwal, A. 2019 · 2019
Cited alongside, same era.
Towards better uncertainty sampling: Active learning with multiple views for deep convolutional neural network
He, T.; Jin, X.; Ding, G.; Yi, L.; and Yan, C. 2019 · 2019
Cited alongside, same era.
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Wisdom of the Ensemble: Improving Consistency of Deep Learning Models
Wang, L.; Ghosh, D.; Gonzalez Diaz, M.; Farahat, A.; Alam, M.; Gupta, C.; Chen, J.; and Marathe, M. 2020 · 2020
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Revisiting knowledge distillation via label smoothing regularization
Yuan, L.; Tay, F. E.; Li, G.; Wang, T.; and Feng, J. 2020 · 2020
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Cold-start active learning through self-supervised language modeling
Yuan, M.; Lin, H.-T.; and Boyd-Graber, J. 2020 · 2020
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Weight Decay Scheduling and Knowledge Distillation for Active Learning
Yun, J.; Kim, B.; and Kim, J. 2020 · 2020
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Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering
Karamcheti, S.; Krishna, R.; Fei-Fei, L.; and Manning, C. 2021 · 2021
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Deep Active Learning for Text Classification with Diverse Interpretations
Liu, Q.; Zhu, Y.; Liu, Z.; Zhang, Y.; and Wu, S. 2021 · 2021
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Towards Understanding the Behaviors of Optimal Deep Active Learning Algorithms
Zhou, Y.; Renduchintala, A.; Li, X.; Wang, S.; Mehdad, Y.; and Ghoshal, A. 2021 · 2021
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