1 |
Srivastava, Nitish, et al., "Dropout: a simple way to prevent neural networks from overfitting," Journal of Machine Learning Research 15.1, pp. 1929-1958, 2014.
|
2 |
Wan, Li, et al., "Regularization of neural networks using dropconnect." Proceedings of the 30th international conference on machine learning (ICML-13), 2013.
|
3 |
Ioffe, Sergey, and Christian Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift." International Conference on Machine Learning, 2015.
|
4 |
He, Kaiming, et al., "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition, 2016. DOI: https://doi.org/10.1109/CVPR.2016.90
|
5 |
Nair, Vinod, and Geoffrey E. Hinton, "Rectified linear units improve restricted boltzmann machines." Proceedings of the 27th international conference on machine learning (ICML-10), 2010.
|
6 |
Maas, Andrew L., Awni Y. Hannun, and Andrew Y. Ng., "Rectifier nonlinearities improve neural network acoustic models." Proc. ICML, vol. 30, no. 1, 2013.
|
7 |
He, Kaiming, et al., "Delving deep into rectifiers: Surpassing human-level performance on imagenet classification." Proceedings of the IEEE international conference on computer vision, 2015. DOI: https://doi.org/10.1109/ICCV.2015.123
DOI
|
8 |
Simonyan, Karen, and Andrew Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
|
9 |
Clevert, Djork-Arne, Thomas Unterthiner, and Sepp Hochreiter, "Fast and accurate deep network learning by exponential linear units (elus)," arXiv preprint arXiv: 1511.07289, 2015.
|