Fetching the paper…
Reading the bibliography…
Large datasets have been crucial to the success of deep learning models in the recent years, which keep performing better as they are trained with more labelled data.
Tencent ml-images: A large-scale multi-label image database for visual representation learning
Wu, B., Chen, W., Fan, Y., Zhang, Y., Hou, J., Huang, J., Liu, W., and Zhang, T. (2019) · 1901
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014) · 1958
Earlier work this paper cites.
Submodularity in data subset selection and active learning
Wei, K., Iyer, R., and Bilmes, J. (2015) · 1963
Earlier work this paper cites.
An efficient algorithm for a complete link method
Defays, D. (1977) · 1977
Earlier work this paper cites.
Improving machine learning performance by removing redundant cases in medical data sets
Ohno-Machado, L., Fraser, H. S., and Ohrn, A. (1998) · 1998
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009) · 2009
Earlier work this paper cites.
The unreasonable effectiveness of data
Halevy, A., Norvig, P., and Pereira, F. (2009) · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G. (2009) · 2009
Earlier work this paper cites.
Scikit-learn: Machine learning in python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al. (2011) · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012) · 2012
Earlier work this paper cites.
Are all training examples equally valuable?
Lapedriza, A., Pirsiavash, H., Bylinskii, Z., and Torralba, A. (2013) · 2013
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A. (2014) · 2014
Cited alongside, same era.
Tensorflow: a system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · 2016
Cited alongside, same era.
Neural data filter for bootstrapping stochastic gradient descent
Fan, Y., Tian, F., Qin, T., and Liu, T.-Y. (2016) · 2016
Cited alongside, same era.
Sgdr: Stochastic gradient descent with warm restarts
Loshchilov, I. and Hutter, F. (2016) · 2016
Cited alongside, same era.
Variational autoencoder for deep learning of images, labels and captions
Pu, Y., Gan, Z., Henao, R., Yuan, X., Li, C., Stevens, A., and Carin, L. (2016) · 2016
Cited alongside, same era.
Domain randomization for transferring deep neural networks from simulation to the real world
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., and Abbeel, P. (2017) · 2017
Later among the works it cites.
Prototypical examples in deep learning: Metrics, characteristics, and utility
Carlini, N., Erlingsson, U., and Papernot, N. (2018) · 2018
Later among the works it cites.
Gpipe: Efficient training of giant neural networks using pipeline parallelism
Huang, Y., Cheng, Y., Chen, D., Lee, H., Ngiam, J., Le, Q. V., and Chen, Z. (2018) · 2018
Later among the works it cites.
Not all samples are created equal: Deep learning with importance sampling
Katharopoulos, A. and Fleuret, F. (2018) · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Sajjadi, M., Javanmardi, M., and Tasdizen, T. (2016) · 2016
Cited alongside, same era.
Gastaldi, X. (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.
Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q. (2017) · 2017
Cited alongside, same era.
Revisiting unreasonable effectiveness of data in deep learning era
Sun, C., Shrivastava, A., Singh, S., and Gupta, A. (2017) · 2017
Cited alongside, same era.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Tarvainen, A. and Valpola, H. (2017) · 2017
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016a)
Cited in the paper.
Kaushal, V., Sahoo, A., Doctor, K., Raju, N., Shetty, S., Singh, P., Iyer, R., and Ramakrishnan, G. (2018) · 2018
Later among the works it cites.
Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Duerig, T., et al. (2018) · 2018
Later among the works it cites.
Focal loss for dense object detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P. (2018) · 2018
Later among the works it cites.
Deep co-training for semi-supervised image recognition
Qiao, S., Shen, W., Zhang, Z., Wang, B., and Yuille, A. L. (2018) · 2018
Later among the works it cites.
Meta-learning for semi-supervised few-shot classification
Ren, M., Ravi, S., Triantafillou, E., Snell, J., Swersky, K., Tenenbaum, J. B., Larochelle, H., and Zemel, R. S. (2018) · 2018
Later among the works it cites.
Tensor2tensor for neural machine translation
Vaswani, A., Bengio, S., Brevdo, E., Chollet, F., Gomez, A. N., Gouws, S., Jones, L., Kaiser, Ł., Kalchbrenner, N., Parmar, N., et al. (2018) · 2018
Later among the works it cites.
Are all training examples created equal? an empirical study
Vodrahalli, K., Li, K., and Malik, J. (2018) · 2018
Later among the works it cites.