Fetching the paper…
Reading the bibliography…
Deep neural networks have been shown to be very powerful modeling tools for many supervised learning tasks involving complex input patterns.
Methods of reducing sample size in monte carlo computations
Kahn, Herman and Marshall, Andy W · 1953
Earlier work this paper cites.
Learning from noisy examples
Angluin, Dana and Laird, Philip · 1988
Earlier work this paper cites.
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Yoav and Schapire, Robert E · 1997
Earlier work this paper cites.
Learning to Learn
Thrun, Sebastian and Pratt, Lorien · 1998
Earlier work this paper cites.
A comparative study of cost-sensitive boosting algorithms
Ting, Kai Ming · 2000
Earlier work this paper cites.
SMOTE: synthetic minority over-sampling technique
Chawla, Nitesh V., Bowyer, Kevin W., Hall, Lawrence O., and Kegelmeyer, W. Philip · 2002
Earlier work this paper cites.
Curriculum learning
Bengio, Yoshua, Louradour, Jérôme, Collobert, Ronan, and Weston, Jason · 2009
Earlier work this paper cites.
Self-paced learning for latent variable models
Kumar, M. Pawan, Packer, Benjamin, and Koller, Daphne · 2010
Earlier work this paper cites.
Ensemble of exemplar-svms for object detection and beyond
Malisiewicz, Tomasz, Gupta, Abhinav, and Efros, Alexei A · 2011
Earlier work this paper cites.
Learning with noisy labels
Natarajan, Nagarajan, Dhillon, Inderjit S., Ravikumar, Pradeep, and Tewari, Ambuj · 2013
Earlier work this paper cites.
Training deep neural networks on noisy labels with bootstrapping
Reed, Scott E., Lee, Honglak, Anguelov, Dragomir, Szegedy, Christian, Erhan, Dumitru, and Rabinovich, Andrew · 2014
Earlier work this paper cites.
Learning from noisy labels with deep neural networks
Sukhbaatar, Sainbayar and Fergus, Rob · 2014
Earlier work this paper cites.
Webly supervised learning of convolutional networks
Chen, Xinlei and Gupta, Abhinav · 2015
Earlier work this paper cites.
Self-paced curriculum learning
Jiang, Lu, Meng, Deyu, Zhao, Qian, Shan, Shiguang, and Hauptmann, Alexander G · 2015
Earlier work this paper cites.
Cost sensitive learning of deep feature representations from imbalanced data
Khan, Salman Hameed, Bennamoun, Mohammed, Sohel, Ferdous Ahmed, and Togneri, Roberto · 2015
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, Olga, Deng, Jia, Su, Hao, Krause, Jonathan, Satheesh, Sanjeev, Ma, Sean, Huang, Zhiheng, Karpathy, Andrej, Khosla, Aditya, Bernstein, Michael, Berg, Alexander C., and Fei-Fei, Li · 2015
Cited alongside, same era.
Learning from massive noisy labeled data for image classification
Xiao, Tong, Xia, Tian, Yang, Yi, Huang, Chang, and Wang, Xiaogang · 2015
Cited alongside, same era.
Tensorflow: A system for large-scale machine learning
Abadi, Martín, Barham, Paul, Chen, Jianmin, Chen, Zhifeng, Davis, Andy, Dean, Jeffrey, Devin, Matthieu, Ghemawat, Sanjay, Irving, Geoffrey, Isard, Michael, Kudlur, Manjunath, Levenberg, Josh, Monga, Rajat, Moore, Sherry, Murray, Derek Gordon, Steiner, Benoit, Tucker, Paul A., Vasudevan, Vijay, Warden, Pete, Wicke, Martin, Yu, Yuan, and Zheng, Xiaoqiang · 2016
Cited alongside, same era.
Learning to learn by gradient descent by gradient descent
Andrychowicz, Marcin, Denil, Misha, Colmenarejo, Sergio Gomez, Hoffman, Matthew W., Pfau, David, Schaul, Tom, and de Freitas, Nando · 2016
Cited alongside, same era.
Auxiliary image regularization for deep cnns with noisy labels
Azadi, Samaneh, Feng, Jiashi, Jegelka, Stefanie, and Darrell, Trevor · 2016
Understanding black-box predictions via influence functions
Koh, Pang Wei and Liang, Percy · 2017
Later among the works it cites.
Building machines that learn and think like people
Lake, Brenden M., Ullman, Tomer D., Tenenbaum, Joshua B., and Gershman, Samuel J · 2017
Later among the works it cites.
Learning from noisy labels with distillation
Li, Yuncheng, Yang, Jianchao, Song, Yale, Cao, Liangliang, Luo, Jiebo, and Li, Li-Jia · 2017
Later among the works it cites.
Focal loss for dense object detection
Lin, Tsung-Yi, Goyal, Priya, Girshick, Ross B., He, Kaiming, and Dollár, Piotr · 2017
Later among the works it cites.
Self-paced co-training
Ma, Fan, Meng, Deyu, Xie, Qi, Li, Zina, and Dong, Xuanyi · 2017
Later among the works it cites.
Towards poisoning of deep learning algorithms with back-gradient optimization
Muñoz-González, Luis, Biggio, Battista, Demontis, Ambra, Paudice, Andrea, Wongrassamee, Vasin, Lupu, Emil C., and Roli, Fabio · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
The cityscapes dataset for semantic urban scene understanding
Cordts, Marius, Omran, Mohamed, Ramos, Sebastian, Rehfeld, Timo, Enzweiler, Markus, Benenson, Rodrigo, Franke, Uwe, Roth, Stefan, and Schiele, Bernt · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian · 2016
Cited alongside, same era.
Learning deep representation for imbalanced classification
Huang, Chen, Li, Yining, Loy, Chen Change, and Tang, Xiaoou · 2016
Cited alongside, same era.
Stochastic variance reduction for nonconvex optimization
Reddi, Sashank J., Hefny, Ahmed, Sra, Suvrit, Póczos, Barnabás, and Smola, Alexander J · 2016
Cited alongside, same era.
Wide residual networks
Zagoruyko, Sergey and Komodakis, Nikos · 2016
Cited alongside, same era.
Active bias: Training more accurate neural networks by emphasizing high variance samples
Chang, Haw-Shiuan, Learned-Miller, Erik G., and McCallum, Andrew · 2017
Cited alongside, same era.
Class rectification hard mining for imbalanced deep learning
Dong, Qi, Gong, Shaogang, and Zhu, Xiatian · 2017
Cited alongside, same era.
Later among the works it cites.
Optimization as a model for few-shot learning
Ravi, Sachin and Larochelle, Hugo · 2017
Later among the works it cites.
Toward robustness against label noise in training deep discriminative neural networks
Vahdat, Arash · 2017
Later among the works it cites.
Robust probabilistic modeling with bayesian data reweighting
Wang, Yixin, Kucukelbir, Alp, and Blei, David M · 2017
Later among the works it cites.
Understanding deep learning requires rethinking generalization
Zhang, Chiyuan, Bengio, Samy, Hardt, Moritz, Recht, Benjamin, and Vinyals, Oriol · 2017
Later among the works it cites.
Using trusted data to train deep networks on labels corrupted by severe noise
Hendrycks, Dan, Mazeika, Mantas, Wilson, Duncan, and Gimpel, Kevin · 2018
Closest in time.
Stochastic hyperparameter optimization through hypernetworks
Lorraine, Jonathan and Duvenaud, David · 2018
Closest in time.
Meta learning for few-shot semi-supervised classification
Ren, Mengye, Triantafillou, Eleni, Ravi, Sachin, Snell, Jake, Swersky, Kevin, Tenenbaum, Joshua B., Larochelle, Hugo, and Zemel, Richard S · 2018
Closest in time.
Understanding short-horizon bias in stochastic meta-optimization
Wu, Yuhuai, Ren, Mengye, Liao, Renjie, and Grosse, Roger B · 2018
Closest in time.