AI RESEARCH
Deep Learning using Rectified Linear Units (ReLU)
arXiv CS.LG
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ArXi:1803.08375v3 Announce Type: replace-cross The Rectified Linear Unit (ReLU) is a foundational activation function in artficial neural networks. Recent literature frequently misattributes its origin to the 2018 (initial) version of this paper, which exclusively investigated ReLU at the classification layer. This paper formally corrects the citation record by tracing the mathematical lineage of piecewise linear functions from early biological models to their definitive integration into deep learning by Nair & Hinton.