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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

Statistical Learning Theory

Statistical learning theory explains why a model that fits training data can still predict well on unseen data by relating true risk to empirical risk plus a complexity term.

#statistical learning theory
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Filtering by:
#generalization bounds
#empirical risk minimization
#structural risk minimization
+11
📚TheoryAdvanced

Rademacher Complexity

Rademacher complexity is a data-dependent measure of how well a function class can fit random noise on a given sample.

#rademacher complexity#empirical rademacher#generalization bounds+12