Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Abstract
This research paper explore the methodology and findings associated with arXiv (Cornell University). The study delves into the core aspects of the research field, providing significant data and citation impact. (Full abstract processing is available via the OpenAlex API).
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References
- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification 2015
- Rectified Linear Units Improve Restricted Boltzmann Machines 2010
- Understanding the difficulty of training deep feedforward neural networks 2010
- On the importance of initialization and momentum in deep learning 2013
- Deep Image: Scaling up Image Recognition 2015