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Most recent neural semi-supervised learning algorithms rely on adding small perturbation to either the input vectors or their representations.
“Learning question classifiers,”
Xin Li and Dan Roth, · 2002
Earlier work this paper cites.
“Semi-supervised learning using gaussian fields and harmonic functions,”
Xiaojin Zhu, Zoubin Ghahramani, and John D Lafferty, · 2003
Earlier work this paper cites.
“Semi-supervised learning literature survey,”
Xiaojin Jerry Zhu, · 2005
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“Semi-supervised learning (chapelle, o. et al., eds.; 2006)[book reviews],”
Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien, · 2009
Earlier work this paper cites.
“Learning word vectors for sentiment analysis,”
Andrew L Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts, · 2011
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“Distributed representations of words and phrases and their compositionality,”
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean, · 2013
Earlier work this paper cites.
“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
Earlier work this paper cites.
“Learning with pseudo-ensembles,”
Philip Bachman, Ouais Alsharif, and Doina Precup, · 2014
Earlier work this paper cites.
“Decaf: A deep convolutional activation feature for generic visual recognition,”
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell, · 2014
Earlier work this paper cites.
“Dropout: a simple way to prevent neural networks from overfitting,”
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov, · 2014
Earlier work this paper cites.
“Adam: A method for stochastic optimization,”
Diederik P Kingma and Jimmy Ba, · 2014
Earlier work this paper cites.
“Semi-supervised sequence learning,”
Andrew M Dai and Quoc V Le, · 2015
Cited alongside, same era.
“Fully convolutional networks for semantic segmentation,”
Jonathan Long, Evan Shelhamer, and Trevor Darrell, · 2015
Cited alongside, same era.
“Temporal ensembling for semi-supervised learning,”
Samuli Laine and Timo Aila, · 2016
Cited alongside, same era.
“Regularization with stochastic transformations and perturbations for deep semi-supervised learning,”
Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen, · 2016
Cited alongside, same era.
“Adversarial training methods for semi-supervised text classification,”
Takeru Miyato, Andrew M Dai, and Ian Goodfellow, · 2016
Cited alongside, same era.
“Semi-supervised sequence modeling with cross-view training,”
Kevin Clark, Minh-Thang Luong, Christopher D Manning, and Quoc V Le, · 2018
Later among the works it cites.
“Deep contextualized word representations,”
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer, · 2018
Later among the works it cites.
“Universal language model fine-tuning for text classification,”
Jeremy Howard and Sebastian Ruder, · 2018
Later among the works it cites.
“Bert: Pre-training of deep bidirectional transformers for language understanding,”
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova, · 2018
Later among the works it cites.
“Improving language understanding by generative pre-training,”
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Lili Mou, Zhao Meng, Rui Yan, Ge Li, Yan Xu, Lu Zhang, and Zhi Jin, · 2016
Cited alongside, same era.
“Pointer sentinel mixture models,”
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher, · 2016
Cited alongside, same era.
“Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,”
Antti Tarvainen and Harri Valpola, · 2017
Cited alongside, same era.
“Learned in translation: Contextualized word vectors,”
Bryan McCann, James Bradbury, Caiming Xiong, and Richard Socher, · 2017
Cited alongside, same era.
“Semi-supervised sequence tagging with bidirectional language models,”
Matthew E Peters, Waleed Ammar, Chandra Bhagavatula, and Russell Power, · 2017
Cited alongside, same era.
“Virtual adversarial training: a regularization method for supervised and semi-supervised learning,”
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii, · 2018
Cited alongside, same era.
Repository to track the progress in NLP, including the datasets and the current state-of-the-art for the most common NLP tasks
“NLP-progress,” https://nlpprogress.com/ ,
Cited in the paper.
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever, · 2018
Later among the works it cites.
“Realistic evaluation of deep semi-supervised learning algorithms,”
Avital Oliver, Augustus Odena, Colin A Raffel, Ekin Dogus Cubuk, and Ian Goodfellow, · 2018
Later among the works it cites.
“Averaging weights leads to wider optima and better generalization,”
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson, · 2018
Later among the works it cites.
“There are many consistent explanations of unlabeled data: Why you should average,”
Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, and Andrew Gordon Wilson, · 2018
Later among the works it cites.
“Loss surfaces, mode connectivity, and fast ensembling of dnns,”
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P Vetrov, and Andrew G Wilson, · 2018
Later among the works it cites.
“Direct optimization of f-measure for retrieval-based personal question answering,”
Rasool Fakoor, Amanjit Kainth, Siamak Shakeri, Christopher Winestock, Abdel-rahman Mohamed, and Ruhi Sarikaya, · 2018
Later among the works it cites.
“Transformers: State-of-the-art natural language processing,” 2019
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and Jamie Brew, · 2019
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