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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

Random Matrix Theory in High-Dimensional Statistics

Random Matrix Theory (RMT) explains how eigenvalues of large random matrices behave when the dimension p is comparable to the sample size n.

#random matrix theory
Advanced
Filtering by:
#spectral norm
#marchenko-pastur
#wigner semicircle
+12
📚TheoryAdvanced

Spectral Analysis of Neural Networks

Spectral analysis studies the distribution of eigenvalues and singular values of neural network weight matrices during training.

#spectral analysis#eigenvalues#singular values+12
📚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