Dropout vs. batch normalization: an empirical study of their impact to deep learning

Author:

Garbin Christian,Zhu XingquanORCID,Marques Oge

Funder

National Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Reference35 articles.

1. Bengio Y (2012) Practical recommendations for gradient-based training of deep architectures

2. Brock A, Lim T, Ritchie JM, Weston N (2017) Freezeout: accelerate training by progressively freezing layers. arXiv:1706.04983

3. Cortes C, Vapnik V (1995) Mach Learning, 273–297

4. Deng L, Hinton G, Kingsbury B (2013) New types of deep neural network learning for speech recognition and related applications: an overview. https://doi.org/10.1109/icassp.2013.6639344

5. Goodfellow IJ, Bengio Y, Courville AC (2016) Deep learning. Adaptive Computation and Machine Learning. MIT Press, Cambridge. http://www.deeplearningbook.org/

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