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Common acquisition functions for active learning use either uncertainty or diversity sampling, aiming to select difficult and diverse data points from the pool of unlabeled data, respectively.
Discriminative active learning
Daniel Gissin and Shai Shalev-Shwartz. 2019 · 1907
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Heterogeneous uncertainty sampling for supervised learning
David D. Lewis and Jason Catlett. 1994 · 1994
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A sequential algorithm for training text classifiers
David D. Lewis and William A. Gale. 1994 · 1994
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Active learning with statistical models
David A. Cohn, Zoubin Ghahramani, and Michael I. Jordan. 1996 · 1996
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Toward optimal active learning through sampling estimation of error reduction
Nicholas Roy and Andrew McCallum. 2001 · 2001
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Incorporating diversity in active learning with support vector machines
Klaus Brinker. 2003 · 2003
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Online choice of active learning algorithms
Yoram Baram, Ran El-Yaniv, and Kobi Luz. 2004 · 2004
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Multi-criteria-based active learning for named entity recognition
Dan Shen, Jie Zhang, Jian Su, Guodong Zhou, and Chew-Lim Tan. 2004 · 2004
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Jingbo Zhu, Huizhen Wang, Tianshun Yao, and Benjamin K Tsou. 2008 · 2008
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Two faces of active learning
Sanjoy Dasgupta. 2011 · 2009
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Active learning literature survey
Burr Settles. 2009 · 2009
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Active learning with clustering
Zalán Bodó, Zsolt Minier, and Lehel Csató. 2011 · 2010
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Bayesian active learning for classification and preference learning
Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel. 2011 · 2011
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011 · 2011
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
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Active learning by learning
Wei-Ning Hsu and Hsuan-Tien Lin. 2015 · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani. 2016 · 2016
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Improved deep metric learning with multi-class n-pair loss objective
Kihyuk Sohn. 2016 · 2016
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PubMed 200k RCT: a dataset for sequential sentence classification in medical abstracts
Franck Dernoncourt and Ji Young Lee. 2017 · 2017
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Learning how to active learn: A deep reinforcement learning approach
Meng Fang, Yuan Li, and Trevor Cohn. 2017 · 2017
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Deep Bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani. 2017 · 2017
Cited alongside, same era.
Diverse mini-batch active learning
Fedor Zhdanov. 2019 · 2019
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Deep batch active learning by diverse, uncertain gradient lower bounds
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal. 2020 · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020 · 2020
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Calibration of pre-trained transformers
Shrey Desai and Greg Durrett. 2020 · 2020
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Fine-tuning pretrained language models: Weight initializations, data orders, and early stopping
Jesse Dodge, Gabriel Ilharco, Roy Schwartz, Ali Farhadi, Hannaneh Hajishirzi, and Noah A. Smith. 2020 · 2020
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Yonatan Geifman and Ran El-Yaniv. 2017 · 2017
Cited alongside, same era.
A continuously growing dataset of sentential paraphrases
Wuwei Lan, Siyu Qiu, Hua He, and Wei Xu. 2017 · 2017
Cited alongside, same era.
Deep active learning for named entity recognition
Yanyao Shen, Hyokun Yun, Zachary Lipton, Yakov Kronrod, and Animashree Anandkumar. 2017 · 2017
Cited alongside, same era.
Adversarial active learning for deep networks: a margin based approach
Melanie Ducoffe and Frederic Precioso. 2018 · 2018
Cited alongside, same era.
Learning how to actively learn: A deep imitation learning approach
Ming Liu, Wray Buntine, and Gholamreza Haffari. 2018 · 2018
Cited alongside, same era.
Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese. 2018 · 2018
Cited alongside, same era.
Deep bayesian active learning for natural language processing: Results of a Large-Scale empirical study
Aditya Siddhant and Zachary C Lipton. 2018 · 2018
Cited alongside, same era.
Active learning for BERT: An empirical study
Liat Ein-Dor, Alon Halfon, Ariel Gera, Eyal Shnarch, Lena Dankin, Leshem Choshen, Marina Danilevsky, Ranit Aharonov, Yoav Katz, and Noam Slonim. 2020 · 2020
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Evaluating models’ local decision boundaries via contrast sets
Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, and Ben Zhou. 2020 · 2020
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Pretrained transformers improve out-of-distribution robustness
Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, Adam Dziedzic, Rishabh Krishnan, and Dawn Song. 2020 · 2020
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Learning the difference that makes a difference with counterfactually-augmented data
Divyansh Kaushik, Eduard Hovy, and Zachary Lipton. 2020 · 2020
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Active sentence learning by adversarial uncertainty sampling in discrete space
Dongyu Ru, Jiangtao Feng, Lin Qiu, Hao Zhou, Mingxuan Wang, Weinan Zhang, Yong Yu, and Lei Li. 2020 · 2020
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Dataset cartography: Mapping and diagnosing datasets with training dynamics
Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A. Smith, and Yejin Choi. 2020 · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
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Cold-start active learning through self-supervised language modeling
Michelle Yuan, Hsuan-Tien Lin, and Jordan Boyd-Graber. 2020 · 2020
Later among the works it cites.
Supervised contrastive learning for pre-trained language model fine-tuning
Beliz Gunel, Jingfei Du, Alexis Conneau, and Veselin Stoyanov. 2021 · 2021
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Mind your outliers! investigating the negative impact of outliers on active learning for visual question answering
Siddharth Karamcheti, Ranjay Krishna, Li Fei-Fei, and Christopher Manning. 2021 · 2021
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Bayesian active learning with pretrained language models
Katerina Margatina, Loïc Barrault, and Nikolaos Aletras. 2021 · 2021
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Active learning for sequence tagging with deep pre-trained models and Bayesian uncertainty estimates
Artem Shelmanov, Dmitri Puzyrev, Lyubov Kupriyanova, Denis Belyakov, Daniil Larionov, Nikita Khromov, Olga Kozlova, Ekaterina Artemova, Dmitry V. Dylov, and Alexander Panchenko. 2021 · 2021
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