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The method that trains neural networks by passing errors backward through the layers and adjusting each connection. A 1986 paper by Rumelhart, Hinton and Williams made it widely used.
What stayed with me was the way he positioned the paper's historical significance, as the earliest real-world application of a neural net trained end-to-end with backpropagation, and as something that, apart from the small dataset and small network, reads remarkably modern.
Nick Oh, Seeing Machines: Luba Elliott on the 2026 CVPR Art Gallery