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Active learning for sentence understanding aims at discovering informative unlabeled data for annotation and therefore reducing the demand for labeled data.
An introduction to kernel and nearest-neighbor nonparametric regression
Naomi S Altman. 1992 · 1992
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A sequential algorithm for training text classifiers
David D Lewis and William A Gale. 1994 · 1994
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A maximum entropy approach to natural language processing
Adam L Berger, Vincent J Della Pietra, and Stephen A Della Pietra. 1996 · 1996
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Introduction to the conll-2003 shared task: Language-independent named entity recognition
Erik F Tjong Kim Sang and Fien De Meulder. 2003 · 2003
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Unsupervised construction of large paraphrase corpora: Exploiting massively parallel news sources
Bill Dolan, Chris Quirk, and Chris Brockett. 2004 · 2004
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Large-scale text categorization by batch mode active learning
Steven CH Hoi, Rong Jin, and Michael R Lyu. 2006 · 2006
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An analysis of active learning strategies for sequence labeling tasks
Burr Settles and Mark Craven. 2008 · 2008
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Multi-class active learning for image classification
Ajay J Joshi, Fatih Porikli, and Nikolaos Papanikolopoulos. 2009 · 2009
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Active learning literature survey
Burr Settles. 2009 · 2009
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Active learning by querying informative and representative examples
Sheng-Jun Huang, Rong Jin, and Zhi-Hua Zhou. 2010 · 2010
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Hashing hyperplane queries to near points with applications to large-scale active learning
Prateek Jain, Sudheendra Vijayanarasimhan, and Kristen Grauman. 2010 · 2010
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Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli. 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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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2014 · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015 · 2015
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Generative adversarial active learning
Jia-Jie Zhu and José Bento. 2017 · 2017
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Adversarial active learning for sequences labeling and generation
Yue Deng, KaWai Chen, Yilin Shen, and Hongxia Jin. 2018 · 2018
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Adversarial active learning for deep networks: a margin based approach
Melanie Ducoffe and Frederic Precioso. 2018 · 2018
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Query-efficient black-box attack by active learning
Pengcheng Li, Jinfeng Yi, and Lijun Zhang. 2018 · 2018
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Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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Deepfool: A simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard. 2016 · 2016
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Adversarial diversity and hard positive generation
Andras Rozsa, Ethan M Rudd, and Terrance E Boult. 2016 · 2016
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner. 2017 · 2017
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Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio. 2017 · 2017
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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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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Billion-scale similarity search with GPUs
Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2019 · 2019
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Activethief: Model extraction using active learning and unannotated public data
Soham Pal, Yash Gupta, Aditya Shukla, Aditya Kanade, Shirish Shevade, and Vinod Ganapathy. 2020 · 2020
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