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Manipulating data, such as weighting data examples or augmenting with new instances, has been increasingly used to improve model training.
Specaugment: A simple data augmentation method for automatic speech recognition
D. S. Park, W. Chan, Y. Zhang, C.-C. Chiu, B. Zoph, E. D. Cubuk, and Q. V. Le · 1904
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The" wake-sleep" algorithm for unsupervised neural networks
G. E. Hinton, P. Dayan, B. J. Frey, and R. M. Neal · 1995
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WordNet: a lexical database for English
G. A. Miller · 1995
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Using expectation-maximization for reinforcement learning
P. Dayan and G. E. Hinton · 1997
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A decision-theoretic generalization of on-line learning and an application to boosting
Y. Freund and R. E. Schapire · 1997
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Transformation invariance in pattern recognition—tangent distance and tangent propagation
P. Y. Simard, Y. A. LeCun, J. S. Denker, and B. Victorri · 1998
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A unifying review of linear gaussian models
S. Roweis and Z. Ghahramani · 1999
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Learning question classifiers
X. Li and D. Roth · 2002
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Posterior regularization for structured latent variable models
K. Ganchev, J. Gillenwater, B. Taskar, et al · 2010
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Self-paced learning for latent variable models
M. P. Kumar, B. Packer, and D. Koller · 2010
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Learning word vectors for sentiment analysis
A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts · 2011
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Ensemble of exemplar-SVMs for object detection and beyond
T. Malisiewicz, A. Gupta, A. A. Efros, et al · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Unified expectation maximization
R. Samdani, M.-W. Chang, and D. Roth · 2012
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Auto-encoding variational Bayes
D. P. Kingma and M. Welling · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Ng, and C. Potts · 2013
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Conditional generative adversarial nets
M. Mirza and S. Osindero · 2014
Cited alongside, same era.
Audio augmentation for speech recognition
T. Ko, V. Peddinti, D. Povey, and S. Khudanpur · 2015
Cited alongside, same era.
Learning to compose neural networks for question answering
J. Andreas, M. Rohrbach, T. Darrell, and D. Klein · 2016
Cited alongside, same era.
C. Finn, P. Christiano, P. Abbeel, and S. Levine · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Maximum a posteriori policy optimisation
A. Abdolmaleki, J. T. Springenberg, Y. Tassa, R. Munos, N. Heess, and M. Riedmiller · 2018
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Learning to teach
Y. Fan, F. Tian, T. Qin, X.-Y. Li, and T.-Y. Liu · 2018
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Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
L. Jiang, Z. Zhou, T. Leung, L.-J. Li, and L. Fei-Fei · 2018
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Not all samples are created equal: Deep learning with importance sampling
A. Katharopoulos and F. Fleuret · 2018
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Contextual augmentation: Data augmentation by words with paradigmatic relations
S. Kobayashi · 2018
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Reinforcement learning and control as probabilistic inference: Tutorial and review
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Harnessing deep neural networks with logic rules
Z. Hu, X. Ma, Z. Liu, E. Hovy, and E. Xing · 2016
Cited alongside, same era.
Categorical reparameterization with gumbel-softmax
E. Jang, S. Gu, and B. Poole · 2016
Cited alongside, same era.
Reward augmented maximum likelihood for neural structured prediction
M. Norouzi, S. Bengio, N. Jaitly, M. Schuster, Y. Wu, D. Schuurmans, et al · 2016
Cited alongside, same era.
Improving neural machine translation models with monolingual data
R. Sennrich, B. Haddow, and A. Birch · 2016
Cited alongside, same era.
Training region-based object detectors with online hard example mining
A. Shrivastava, A. Gupta, and R. Girshick · 2016
Cited alongside, same era.
Adversarial transformation networks: Learning to generate adversarial examples
S. Baluja and I. Fischer · 2017
Cited alongside, same era.
Active bias: Training more accurate neural networks by emphasizing high variance samples
H.-S. Chang, E. Learned-Miller, and A. McCallum · 2017
Cited alongside, same era.
S. Levine · 2018
Later among the works it cites.
Jointly optimize data augmentation and network training: Adversarial data augmentation in human pose estimation
X. Peng, Z. Tang, F. Yang, R. S. Feris, and D. Metaxas · 2018
Later among the works it cites.
Learning to reweight examples for robust deep learning
M. Ren, W. Zeng, B. Yang, and R. Urtasun · 2018
Later among the works it cites.
Connecting the dots between MLE and RL for sequence generation
B. Tan, Z. Hu, Z. Yang, R. Salakhutdinov, and E. Xing · 2018
Later among the works it cites.
Conditional BERT contextual augmentation
X. Wu, S. Lv, L. Zang, J. Han, and S. Hu · 2018
Later among the works it cites.
On learning intrinsic rewards for policy gradient methods
Z. Zheng, J. Oh, and S. Singh · 2018
Later among the works it cites.
Autoaugment: Learning augmentation policies from data
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le · 2019
Closest in time.
BERT: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2019
Closest in time.
A study of various text augmentation techniques for relation classification in free text
P. K. B. Giridhara, M. Chinmaya, R. K. M. Venkataramana, S. S. Bukhari, and A. Dengel · 2019
Closest in time.
EDA: Easy data augmentation techniques for boosting performance on text classification tasks
J. W. Wei and K. Zou · 2019
Closest in time.
Unsupervised data augmentation
Q. Xie, Z. Dai, E. Hovy, M.-T. Luong, and L. Q. V · 2019
Closest in time.