On Information Theoretic Bounds for SGD
Read OriginalThis technical blog post discusses a theoretical approach to understanding the generalization of Stochastic Gradient Descent (SGD) using information theory. It explains a thought experiment linking the mutual information between model parameters and the training dataset to generalization performance, and outlines how KL divergences are used to derive formal bounds for SGD.
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