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Understanding the difficulty of training deep feedforward neural networks (2010) Authors: Xavier Glorot and Yoshua Bengio Abstract/Summary: This seminal paper analyzes why training deep neural networks is difficult. It investigates the dynamics of descending signals (backpropagation) through hidden layers. The authors demonstrate that standard random initialization causes the gradients to "vanish" or "explode" as they descend through the network. This paper is foundational for understanding "hidden updates" and the logic behind modern initialization strategies (like Xavier/Glorot initialization). If you are in the medical field, "hentaied"
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