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Semi-supervised learning (SSL) algorithms have had great success in recent years in limited labeled data regimes.
Accelerated greedy algorithms for maximizing submodular set functions
M. Minoux · 1978
Earlier work this paper cites.
Backpropagation applied to handwritten zip code recognition
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel · 1989
Earlier work this paper cites.
Laplacian eigenmaps and spectral techniques for embedding and clustering
M. Belkin and P. Niyogi · 2001
Earlier work this paper cites.
Semi-supervised learning using gaussian fields and harmonic functions
X. Zhu, Z. Ghahramani, and J. Lafferty · 2003
Earlier work this paper cites.
Using deep belief nets to learn covariance kernels for gaussian processes
G. E. Hinton and R. R. Salakhutdinov · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
Earlier work this paper cites.
Semi-Supervised Learning
O. Chapelle, B. Schlkopf, and A. Zien · 2010
Earlier work this paper cites.
Super-human multi-talker speech recognition: A graphical modeling approach
J. R. Hershey, S. J. Rennie, P. A. Olsen, and T. T. Kristjansson · 2010
Earlier work this paper cites.
MNIST handwritten digit database
Y. LeCun and C. Cortes · 2010
Earlier work this paper cites.
The importance of encoding versus training with sparse coding and vector quantization
A. Coates and A. Y. Ng · 2011
Earlier work this paper cites.
Submodular meets spectral: Greedy algorithms for subset selection, sparse approximation and dictionary selection
A. Das and D. Kempe · 2011
Earlier work this paper cites.
Learning word vectors for sentiment analysis
A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
Earlier work this paper cites.
Multi-column deep neural networks for image classification, 2012
D. Cireşan, U. Meier, and J. Schmidhuber · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Semi-supervised learning with deep generative models
D. P. Kingma, S. Mohamed, D. Jimenez Rezende, and M. Welling · 2014
Earlier work this paper cites.
Submodular subset selection for large-scale speech training data
K. Wei, Y. Liu, K. Kirchhoff, C. Bartels, and J. Bilmes · 2014
Earlier work this paper cites.
Semi-supervised convolutional neural networks for text categorization via region embedding
R. Johnson and T. Zhang · 2015
Earlier work this paper cites.
SVitchboard II and FiSVer I: High-quality limited-complexity corpora of conversational English speech
Y. Liu, R. Iyer, K. Kirchhoff, and J. Bilmes · 2015
Earlier work this paper cites.
Lazier than lazy greedy
B. Mirzasoleiman, A. Badanidiyuru, A. Karbasi, J. Vondrák, and A. Krause · 2015
Earlier work this paper cites.
Submodularity in data subset selection and active learning
K. Wei, R. Iyer, and J. Bilmes · 2015
Cited alongside, same era.
Tensorflow: A system for large-scale machine learning
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng · 2016
Cited alongside, same era.
SGDR: stochastic gradient descent with restarts
I. Loshchilov and F. Hutter · 2016
Cited alongside, same era.
Variational autoencoder for deep learning of images, labels and captions
Y. Pu, Z. Gan, R. Henao, X. Yuan, C. Li, A. Stevens, and L. Carin · 2016
Cited alongside, same era.
Improved techniques for training gans
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, X. Chen, and X. Chen · 2016
Cited alongside, same era.
Mixmatch: A holistic approach to semi-supervised learning, 2019
D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. Raffel · 2019
Later among the works it cites.
Proceedings of the Fourth Conference on Machine Translation, WMT 2019, Florence, Italy, August 1-2, 2019 - Volume 1: Research Papers
O. Bojar, R. Chatterjee, C. Federmann, M. Fishel, Y. Graham, B. Haddow, M. Huck, A. Jimeno-Yepes, P. Koehn, A. Martins, C. Monz, M. Negri, A. Névéol, M. L. Neves, M. Post, M. Turchi, and K. Verspoor, editors · 2019
Later among the works it cites.
Distributionally robust semi-supervised learning for people-centric sensing
K. Chen, L. Yao, D. Zhang, X. Chang, G. Long, and S. Wang · 2019
Later among the works it cites.
Learning from less data: A unified data subset selection and active learning framework for computer vision
V. Kaushal, R. Iyer, S. Kothawade, R. Mahadev, K. Doctor, and G. Ramakrishnan · 2019
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Y. Yan, Z. Xu, I. W. Tsang, G. Long, and Y. Yang · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
C. Finn, P. Abbeel, and S. Levine · 2017
Cited alongside, same era.
Google Vizier: A Service for Black-Box Optimization
D. Golovin, B. Solnik, S. Moitra, G. Kochanski, J. E. Karro, and D. Sculley, editors · 2017
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Scalable greedy feature selection via weak submodularity
R. Khanna, E. Elenberg, A. Dimakis, S. Negahban, and J. Ghosh · 2017
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
B. Lakshminarayanan, A. Pritzel, and C. Blundell · 2017
Cited alongside, same era.
Adversarial training methods for semi-supervised text classification
T. Miyato, A. M. Dai, and I. J. Goodfellow · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
Cited alongside, same era.
A. Lacoste, A. Luccioni, V. Schmidt, and T. Dandres · 2019
Later among the works it cites.
Deep metric transfer for label propagation with limited annotated data, 2019
B. Liu, Z. Wu, H. Hu, and S. Lin · 2019
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Realistic evaluation of deep semi-supervised learning algorithms, 2019
A. Oliver, A. Odena, C. Raffel, E. D. Cubuk, and I. J. Goodfellow · 2019
Later among the works it cites.
Dota 2 with large scale deep reinforcement learning, 2019
OpenAI, :, C. Berner, G. Brockman, B. Chan, V. Cheung, P. Dębiak, C. Dennison, D. Farhi, Q. Fischer, S. Hashme, C. Hesse, R. Józefowicz, S. Gray, C. Olsson, J. Pachocki, M. Petrov, H. P. d. O. Pinto, J. Raiman, T. Salimans, J. Schlatter, J. Schneider, S. Sidor, I. Sutskever, J. Tang, F. Wolski, and S. Zhang · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
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Meta-weight-net: Learning an explicit mapping for sample weighting
J. Shu, Q. Xie, L. Yi, Q. Zhao, S. Zhou, Z. Xu, and D. Meng · 2019
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Energy and policy considerations for deep learning in nlp, 2019
E. Strubell, A. Ganesh, and A. McCallum · 2019
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Deep batch active learning by diverse, uncertain gradient lower bounds
J. T. Ash, C. Zhang, A. Krishnamurthy, J. Langford, and A. Agarwal · 2020
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Semi-supervised learning under class distribution mismatch
Y. Chen, X. Zhu, W. Li, and S. Gong · 2020
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Core-sets: Updated survey
D. Feldman · 2020
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Safe deep semi-supervised learning for unseen-class unlabeled data
L.-Z. Guo, Z.-Y. Zhang, Y. Jiang, Y.-F. Li, and Z.-H. Zhou · 2020
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Coresets for data-efficient training of machine learning models, 2020
B. Mirzasoleiman, J. Bilmes, and J. Leskovec · 2020
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Coresets for robust training of deep neural networks against noisy labels
B. Mirzasoleiman, K. Cao, and J. Leskovec · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence, 2020
K. Sohn, D. Berthelot, C.-L. Li, Z. Zhang, N. Carlini, E. D. Cubuk, A. Kurakin, H. Zhang, and C. Raffel · 2020
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Grad-match: A gradient matching based data subset selection for efficient learning, 2021
K. Killamsetty, D. Sivasubramanian, B. Mirzasoleiman, G. Ramakrishnan, A. De, and R. Iyer · 2021
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Glister: Generalization based data subset selection for efficient and robust learning, 2021
K. Killamsetty, D. Sivasubramanian, G. Ramakrishnan, and R. Iyer · 2021
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