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Data augmentation has been widely used to improve generalizability of machine learning models.
A comprehensive survey on graph neural networks
Wu, Z.; Pan, S.; Chen, F.; Long, G.; Zhang, C.; and Yu, P. S. 2019 · 1901
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Data Augmentation via Dependency Tree Morphing for Low-Resource Languages
Şahin, G. G.; and Steedman, M. 2019 · 1903
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A survey on face data augmentation
Wang, X.; Wang, K.; and Lian, S. 2019 · 1904
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Unsupervised Data Augmentation for Consistency Training
Xie, Q.; Dai, Z.; Hovy, E.; Luong, M.-T.; and Le, Q. V. 2019 · 1904
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Population based augmentation: Efficient learning of augmentation policy schedules
Ho, D.; Liang, E.; Stoica, I.; Abbeel, P.; and Chen, X. 2019 · 1905
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Chen, D.; Lin, Y.; Li, W.; Li, P.; Zhou, J.; and Sun, X. 2019 · 1909
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Graphmix: Regularized training of graph neural networks for semi-supervised learning
Verma, V.; Qu, M.; Lamb, A.; Bengio, Y.; Kannala, J.; and Tang, J. 2019 · 1909
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; and Salakhutdinov, R. 2014 · 1958
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SMOTE: synthetic minority over-sampling technique
Chawla, N. V.; Bowyer, K. W.; Hall, L. O.; and Kegelmeyer, W. P. 2002 · 2002
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The imbalanced training sample problem: Under or over sampling?
Barandela, R.; Valdovinos, R. M.; Sánchez, J. S.; and Ferri, F. J. 2004 · 2004
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Open graph benchmark: Datasets for machine learning on graphs
Hu, W.; Fey, M.; Zitnik, M.; Dong, Y.; Ren, H.; Liu, B.; Catasta, M.; and Leskovec, J. 2020 · 2005
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A Unified View on Graph Neural Networks as Graph Signal Denoising
Ma, Y.; Liu, X.; Zhao, T.; Liu, Y.; Tang, J.; and Shah, N. 2020 · 2010
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Data mining: concepts and techniques
Han, J.; Pei, J.; and Kamber, M. 2011 · 2011
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Estimating or propagating gradients through stochastic neurons for conditional computation
Bengio, Y.; Léonard, N.; and Courville, A. 2013 · 2013
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Spectral networks and locally connected networks on graphs
Bruna, J.; Zaremba, W.; Szlam, A.; and LeCun, Y. 2013 · 2013
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Generative adversarial nets
Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; and Bengio, Y. 2014 · 2014
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Deepwalk: Online learning of social representations
Perozzi, B.; Al-Rfou, R.; and Skiena, S. 2014 · 2014
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Deep convolutional networks on graph-structured data
Henaff, M.; Bruna, J.; and LeCun, Y. 2015 · 2015
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Line: Large-scale information network embedding
Tang, J.; Qu, M.; Wang, M.; Zhang, M.; Yan, J.; and Mei, Q. 2015 · 2015
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Character-level Convolutional Networks for Text Classification
Zhang, X.; Zhao, J.; and LeCun, Y. 2015 · 2015
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M.; Bresson, X.; and Vandergheynst, P. 2016 · 2016
Cited alongside, same era.
Categorical reparameterization with gumbel-softmax
Jang, E.; Gu, S.; and Poole, B. 2016 · 2016
Cited alongside, same era.
The concrete distribution: A continuous relaxation of discrete random variables
Maddison, C. J.; Mnih, A.; and Teh, Y. W. 2016 · 2016
Cited alongside, same era.
Learning convolutional neural networks for graphs
Niepert, M.; Ahmed, M.; and Kutzkov, K. 2016 · 2016
Cited alongside, same era.
Random erasing data augmentation
Zhong, Z.; Zheng, L.; Kang, G.; Li, S.; and Yang, Y. 2017 · 2017
Later among the works it cites.
Fastgcn: fast learning with graph convolutional networks via importance sampling
Chen, J.; Ma, T.; and Xiao, C. 2018 · 2018
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Understanding Back-Translation at Scale
Edunov, S.; Ott, M.; Auli, M.; and Grangier, D. 2018 · 2018
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Large-scale learnable graph convolutional networks
Gao, H.; Wang, Z.; and Ji, S. 2018 · 2018
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Cayleynets: Graph convolutional neural networks with complex rational spectral filters
Levie, R.; Monti, F.; Bresson, X.; and Bronstein, M. M. 2018 · 2018
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Improving Neural Machine Translation Models with Monolingual Data
Sennrich, R.; Haddow, B.; and Birch, A. 2016 · 2016
Cited alongside, same era.
Structural deep network embedding
Wang, D.; Cui, P.; and Zhu, W. 2016 · 2016
Cited alongside, same era.
Data augmentation generative adversarial networks
Antoniou, A.; Storkey, A.; and Edwards, H. 2017 · 2017
Cited alongside, same era.
Improved regularization of convolutional neural networks with cutout
DeVries, T.; and Taylor, G. W. 2017 · 2017
Cited alongside, same era.
Data augmentation for low-resource neural machine translation
Fadaee, M.; Bisazza, A.; and Monz, C. 2017 · 2017
Cited alongside, same era.
Accurate, large minibatch sgd: Training imagenet in 1 hour
Goyal, P.; Dollár, P.; Girshick, R.; Noordhuis, P.; Wesolowski, L.; Kyrola, A.; Tulloch, A.; Jia, Y.; and He, K. 2017 · 2017
Cited alongside, same era.
Inductive representation learning on large graphs
Hamilton, W.; Ying, Z.; and Leskovec, J. 2017 · 2017
Cited alongside, same era.
Li, R.; Wang, S.; Zhu, F.; and Huang, J. 2018 · 2018
Later among the works it cites.
Graph convolutional neural networks for web-scale recommender systems
Ying, R.; He, R.; Chen, K.; Eksombatchai, P.; Hamilton, W. L.; and Leskovec, J. 2018 · 2018
Later among the works it cites.
Deep learning on graphs: A survey
Zhang, Z.; Cui, P.; and Zhu, W. 2018 · 2018
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Optuna: A next-generation hyperparameter optimization framework
Akiba, T.; Sano, S.; Yanase, T.; Ohta, T.; and Koyama, M. 2019 · 2019
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Autoaugment: Learning augmentation strategies from data
Cubuk, E. D.; Zoph, B.; Mane, D.; Vasudevan, V.; and Le, Q. V. 2019 · 2019
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Deepgcns: Can gcns go as deep as cnns?
Li, G.; Muller, M.; Thabet, A.; and Ghanem, B. 2019 · 2019
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DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
Rong, Y.; Huang, W.; Xu, T.; and Huang, J. 2019 · 2019
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A survey on image data augmentation for deep learning
Shorten, C.; and Khoshgoftaar, T. M. 2019 · 2019
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DEMO-Net: Degree-specific graph neural networks for node and graph classification
Wu, J.; He, J.; and Xu, J. 2019 · 2019
Later among the works it cites.
Data augmentation using learned transformations for one-shot medical image segmentation
Zhao, A.; Balakrishnan, G.; Durand, F.; Guttag, J. V.; and Dalca, A. V. 2019 · 2019
Later among the works it cites.
Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors
Wang, D.; Jiang, M.; Syed, M.; Conway, O.; Juneja, V.; Subramanian, S.; and Chawla, N. V. 2020 · 2020
Closest in time.
Identifying referential intention with heterogeneous contexts
Yu, W.; Yu, M.; Zhao, T.; and Jiang, M. 2020 · 2020
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Error-Bounded Graph Anomaly Loss for GNNs
Zhao, T.; Deng, C.; Yu, K.; Jiang, T.; Wang, D.; and Jiang, M. 2020 · 2020
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