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A glowing, intricate neural network with a prominent "skip connection" pathway, visualized as a luminous bridge, in a futuristic tech lab setting.

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Who Invented Deep Residual Learning?

Deep residual learning, a pivotal advancement in deep neural networks, was introduced by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun in their 2015 paper titled "Deep Residual Learning for Image Recognition." This work addressed the challenges of training very deep networks by incorporating residual connections, allowing models to learn residual functions relative to their inputs. This innovation significantly improved the training of deeper networks and led to the development of the ResNet architecture, which achieved first place in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2015.

The concept of residual connections has earlier roots. In 1991, Sepp Hochreiter introduced recurrent residual connections in recurrent neural networks (RNNs), facilitating better gradient flow. Later, in 2015, Highway Networks by Srivastava, Greff, and Schmidhuber incorporated gated residual connections in feedforward networks, which influenced the development of ResNet.

In summary, while the 2015 ResNet paper by He et al. popularized deep residual learning, the foundational ideas were developed over several decades, with contributions from multiple researchers.

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The prompt for this was: Who invented deep residual learning?

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