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Semi-supervised learning has proven to be a powerful paradigm for leveraging unlabeled data to mitigate the reliance on large labeled datasets.
Verification of forecasts expressed in terms of probability
Glenn W. Brier · 1950
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Keeping neural networks simple by minimizing the description length of the weights
Geoffrey Hinton and Drew van Camp · 1993
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Learning by transduction
Alexander Gammerman, Volodya Vovk, and Vladimir Vapnik · 1998
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Transductive inference for text classification using support vector machines
Thorsten Joachims · 1999
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Laplacian eigenmaps and spectral techniques for embedding and clustering
Mikhail Belkin and Partha Niyogi · 2002
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Transductive learning via spectral graph partitioning
Thorsten Joachims · 2003
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Best practice for convolutional neural networks applied to visual document analysis
Patrice Y. Simard, David Steinkraus, and John C. Platt · 2003
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Semi-supervised learning using gaussian fields and harmonic functions
Xiaojin Zhu, Zoubin Ghahramani, and John D Lafferty · 2003
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Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2005
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Label Propagation and Quadratic Criterion
Yoshua Bengio, Olivier Delalleau, and Nicolas Le Roux · 2006
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Semi-Supervised Learning
Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien · 2006
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Principled hybrids of generative and discriminative models
Julia A. Lasserre, Christopher M. Bishop, and Thomas P. Minka · 2006
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Using deep belief nets to learn covariance kernels for Gaussian processes
Ruslan Salakhutdinov and Geoffrey E. Hinton · 2007
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Deep, big, simple neural nets for handwritten digit recognition
Dan Claudiu Cireşan, Ueli Meier, Luca Maria Gambardella, and Jürgen Schmidhuber · 2010
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An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
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The importance of encoding versus training with sparse coding and vector quantization
Adam Coates and Andrew Y. Ng · 2011
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Spike-and-slab sparse coding for unsupervised feature discovery
Ian J. Goodfellow, Aaron Courville, and Yoshua Bengio · 2011
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 2013
Cited alongside, same era.
Semi-supervised learning with deep generative models
Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
Cited alongside, same era.
On the generalization properties of differential privacy
Kobbi Nissim and Uri Stemmer · 2015
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Semi-supervised learning with ladder networks
Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W. Taylor · 2017
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Shake-shake regularization
Xavier Gastaldi · 2017
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Fixing weight decay regularization in Adam
Ilya Loshchilov and Frank Hutter · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
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Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, and Tapani Raiko · 2015
Cited alongside, same era.
Stacked what-where auto-encoders
Junbo Zhao, Michael Mathieu, Ross Goroshin, and Yann Lecun · 2015
Cited alongside, same era.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Cited alongside, same era.
Semi-supervised learning with context-conditional generative adversarial networks
Emily Denton, Sam Gross, and Rob Fergus · 2016
Cited alongside, same era.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2016
Cited alongside, same era.
Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Cited alongside, same era.
Semi-supervised learning with generative adversarial networks
Augustus Odena · 2016
Cited alongside, same era.
Antti Tarvainen and Harri Valpola · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz · 2017
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Improving consistency-based semi-supervised learning with weight averaging
Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, and Andrew Gordon Wilson · 2018
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Autoaugment: Learning augmentation policies from data
Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V. Le · 2018
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Invariant information distillation for unsupervised image segmentation and clustering
Xu Ji, Joao F Henriques, and Andrea Vedaldi · 2018
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Deep metric transfer for label propagation with limited annotated data
Bin Liu, Zhirong Wu, Han Hu, and Stephen Lin · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Shin Ishii, and Masanori Koyama · 2018
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Realistic evaluation of deep semi-supervised learning algorithms
Avital Oliver, Augustus Odena, Colin Raffel, Ekin Dogus Cubuk, and Ian Goodfellow · 2018
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Scalable private learning with pate
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson · 2018
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Three mechanisms of weight decay regularization
Guodong Zhang, Chaoqi Wang, Bowen Xu, and Roger Grosse · 2018
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Interpolation consistency training for semi-supervised learning
Vikas Verma, Alex Lamb, Juho Kannala, Yoshua Bengio, and David Lopez-Paz · 2019
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