Deep learning

Author:

LeCun Yann,Bengio Yoshua,Hinton Geoffrey

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Reference103 articles.

1. Krizhevsky, A., Sutskever, I. & Hinton, G. ImageNet classification with deep convolutional neural networks. In Proc. Advances in Neural Information Processing Systems 25 1090–1098 (2012). This report was a breakthrough that used convolutional nets to almost halve the error rate for object recognition, and precipitated the rapid adoption of deep learning by the computer vision community.

2. Farabet, C., Couprie, C., Najman, L. & LeCun, Y. Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1915–1929 (2013).

3. Tompson, J., Jain, A., LeCun, Y. & Bregler, C. Joint training of a convolutional network and a graphical model for human pose estimation. In Proc. Advances in Neural Information Processing Systems 27 1799–1807 (2014).

4. Szegedy, C. et al. Going deeper with convolutions. Preprint at http://arxiv.org/abs/1409.4842 (2014).

5. Mikolov, T., Deoras, A., Povey, D., Burget, L. & Cernocky, J. Strategies for training large scale neural network language models. In Proc. Automatic Speech Recognition and Understanding 196–201 (2011).

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