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Active learning, which effectively collects informative unlabeled data for annotation, reduces the demand for labeled data.
Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel. 2019b · 1905
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Xlda: Cross-lingual data augmentation for natural language inference and question answering
Jasdeep Singh, Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher. 2019 · 1905
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Efficient and accurate estimation of lipschitz constants for deep neural networks
Mahyar Fazlyab, Alexander Robey, Hamed Hassani, Manfred Morari, and George J Pappas. 2019 · 1906
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Discriminative active learning
Daniel Gissin and Shai Shalev-Shwartz. 2019 · 1907
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Facebook fair’s wmt19 news translation task submission
Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, and Sergey Edunov. 2019 · 1907
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Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019 · 1909
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Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring
David Berthelot, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, and Colin Raffel. 2019a · 1911
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On information and sufficiency
Solomon Kullback and Richard A Leibler. 1951 · 1951
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii. 2018 · 1993
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A sequential algorithm for training text classifiers
David D Lewis and William A Gale. 1994 · 1994
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Learning with labeled and unlabeled data
Matthias Seeger. 2000 · 2000
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Building a question answering test collection
Ellen M Voorhees and Dawn M Tice. 2000 · 2000
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Learning from labeled and unlabeled data using graph mincuts
Avrim Blum and Shuchi Chawla. 2001 · 2001
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Cluster kernels for semi-supervised learning
Olivier Chapelle, Jason Weston, and Bernhard Schölkopf. 2002 · 2002
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Maxup: A simple way to improve generalization of neural network training
Chengyue Gong, Tongzheng Ren, Mao Ye, and Qiang Liu. 2020 · 2002
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Types of cost in inductive concept learning
Peter D. Turney. 2002 · 2002
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Calibration of pre-trained transformers
Shrey Desai and Greg Durrett. 2020 · 2003
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A new metric for probability distributions
Dominik Maria Endres and Johannes E Schindelin. 2003 · 2003
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Representative sampling for text classification using support vector machines
Zhao Xu, Kai Yu, Volker Tresp, Xiaowei Xu, and Jizhi Wang. 2003 · 2003
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Semi-supervised learning using gaussian fields and harmonic functions
Xiaojin Zhu, Zoubin Ghahramani, and John D Lafferty. 2003 · 2003
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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 · 2004
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Learning with local and global consistency
Dengyong Zhou, Olivier Bousquet, Thomas N Lal, Jason Weston, and Bernhard Schölkopf. 2004 · 2004
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Reducing labeling effort for structured prediction tasks
Aron Culotta and Andrew McCallum. 2005 · 2005
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The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005 · 2005
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Divergence measures and message passing
Tom Minka et al. 2005 · 2005
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Semi-supervised learning (chapelle, o. et al., eds.; 2006)[book reviews]
Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien. 2009 · 2006
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Resolution limits of sparse coding in high dimensions
Alyson K Fletcher, Sundeep Rangan, and Vivek K Goyal. 2008 · 2008
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Get another label? improving data quality and data mining using multiple, noisy labelers
V. Sheng, F. Provost, and Panagiotis G. Ipeirotis. 2008 · 2008
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. 2009 · 2009
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Active learning literature survey
Burr Settles. 2009 · 2009
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On strategies for imbalanced text classification using svm: A comparative study
Aixin Sun, Ee-Peng Lim, and Ying Liu. 2009 · 2009
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Active learning with clustering
Zalán Bodó, Zsolt Minier, and Lehel Csató. 2011 · 2010
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Understanding back-translation at scale
Sergey Edunov, Myle Ott, Michael Auli, and David Grangier. 2018 · 2018
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Discrete adversarial attacks and submodular optimization with applications to text classification
Qi Lei, Lingfei Wu, Pin-Yu Chen, Alexandros G Dimakis, Inderjit S Dhillon, and Michael Witbrock. 2018 · 2018
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Practical obstacles to deploying active learning
David Lowell, Zachary C Lipton, and Byron C Wallace. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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Lipschitz regularity of deep neural networks: analysis and efficient estimation
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Xinjie Fan, Shujian Zhang, Bo Chen, and Mingyuan Zhou. 2020 · 2010
Cited alongside, same era.
On the importance of adaptive data collection for extremely imbalanced pairwise tasks
Stephen Mussmann, Robin Jia, and Percy Liang. 2020b · 2010
Cited alongside, same era.
Cold-start active learning through self-supervised language modeling
Michelle Yuan, Hsuan-Tien Lin, and Jordan Boyd-Graber. 2020 · 2010
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Learning word vectors for sentiment analysis
Andrew Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts. 2011 · 2011
Cited alongside, same era.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. 2013 · 2013
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Cited alongside, same era.
Kevin Scaman and Aladin Virmaux. 2018 · 2018
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Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning
Andreas Kirsch, Joost Van Amersfoort, and Yarin Gal. 2019 · 2019
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Inherent disagreements in human textual inferences
Ellie Pavlick and Tom Kwiatkowski. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 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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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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Benefits of intermediate annotations in reading comprehension
Dheeru Dua, Sameer Singh, and Matt Gardner. 2020 · 2020
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Consistency-based semi-supervised active learning: Towards minimizing labeling cost
Mingfei Gao, Zizhao Zhang, Guo Yu, Sercan Ö Arık, Larry S Davis, and Tomas Pfister. 2020 · 2020
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What can we learn from collective human opinions on natural language inference data?
Yixin Nie, Xiang Zhou, and Mohit Bansal. 2020 · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang 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, Julien Chaumond, Lysandre Debut, Victor Sanh, Clement Delangue, Anthony Moi, Pierric Cistac, Morgan Funtowicz, Joe Davison, Sam Shleifer, et al. 2020 · 2020
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Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, E. Hovy, Minh-Thang Luong, and Quoc V. Le. 2020 · 2020
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Learning with noisy labels by targeted relabeling
Derek Chen, Zhou Yu, and Samuel R Bowman. 2021 · 2021
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Contextual dropout: An efficient sample-dependent dropout module
Xinjie Fan, Shujian Zhang, Korawat Tanwisuth, Xiaoning Qian, and Mingyuan Zhou. 2021 · 2021
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Did they answer? subjective acts and intents in conversational discourse
Elisa Ferracane, Greg Durrett, Junyi Jessy Li, and Katrin Erk. 2021 · 2021
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Active learning by acquiring contrastive examples
Katerina Margatina, Giorgos Vernikos, Loïc Barrault, and Nikolaos Aletras. 2021 · 2021
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Mauve: Measuring the gap between neural text and human text using divergence frontiers
Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, and Zaid Harchaoui. 2021 · 2021
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Augmax: Adversarial composition of random augmentations for robust training
Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Anima Anandkumar, and Zhangyang Wang. 2021 · 2021
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Locality sensitive teaching
Zhaozhuo Xu, Beidi Chen, Chaojian Li, Weiyang Liu, Le Song, Yingyan Lin, and Anshumali Shrivastava. 2021 · 2021
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Mike Zhang and Barbara Plank. 2021 · 2021
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Calibrate before use: Improving few-shot performance of language models
Tony Z Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
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