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Generalization Bounds for Deep Learning

Generalization bounds explain why deep neural networks can perform well on unseen data despite having many parameters.

#generalization bounds#pac-bayes#compression bounds+12
📚TheoryAdvanced

PAC-Bayes Theory

PAC-Bayes provides high-probability generalization bounds for randomized predictors by comparing a data-dependent posterior Q to a fixed, data-independent prior P through KL(Q||P).

#pac-bayes
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📚TheoryAdvanced

Deep Learning Generalization Theory

Deep learning generalization theory tries to explain why overparameterized networks can fit (interpolate) training data yet still perform well on new data.

#generalization#implicit regularization#minimum norm+12