Proposes pointwise Riemannian Dimension from feature eigenvalues to derive tighter, representation-aware generalization bounds for deep networks in the nonlinear regime.
Deep Learning is Not So Mysterious or Different, March 2025
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
verdicts
UNVERDICTED 5roles
background 2polarities
background 2representative citing papers
Gradient matching empirically recovers implicit regularization effects such as l2 penalties from early stopping and dropout in neural networks.
A model-independent upper bound on generalization gap is established that depends solely on the Rényi entropy of the data-generating distribution for histogram-determined algorithms such as ERM.
Expanding feature capacity enables discovery of sparse priced-risk structures, so nonlinear expansions plus basis pursuit outperform ridgeless methods beyond a complexity threshold.
Quantum computers may enable more natural manipulation of Fourier spectra in ML models via the Quantum Fourier Transform, potentially leading to resource-efficient spectral methods.
citing papers explorer
-
Pointwise Generalization in Deep Neural Networks
Proposes pointwise Riemannian Dimension from feature eigenvalues to derive tighter, representation-aware generalization bounds for deep networks in the nonlinear regime.
-
Estimating Implicit Regularization in Deep Learning
Gradient matching empirically recovers implicit regularization effects such as l2 penalties from early stopping and dropout in neural networks.
-
Overfitting has a limitation: a model-independent generalization gap bound based on R\'enyi entropy
A model-independent upper bound on generalization gap is established that depends solely on the Rényi entropy of the data-generating distribution for histogram-determined algorithms such as ERM.
-
The Virtue of Sparsity in Complexity
Expanding feature capacity enables discovery of sparse priced-risk structures, so nonlinear expansions plus basis pursuit outperform ridgeless methods beyond a complexity threshold.
-
Spectral methods: crucial for machine learning, natural for quantum computers?
Quantum computers may enable more natural manipulation of Fourier spectra in ML models via the Quantum Fourier Transform, potentially leading to resource-efficient spectral methods.