Deep Learning using Rectified Linear Units (ReLU)

The paper corrects the misattribution of ReLU's origin to 2018, instead crediting Nair & Hinton (2010) for its integration into deep learning. The study also empirically validates that non-saturating functions like ReLU outperform saturating ones in various tasks, confirming their critical role in achieving stable convergence and high accuracy in deep neural networks.

Read the full article at arXiv stat.ML

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