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Found 4 papers, 0 papers with code
Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance
Classification models are expected to perform equally well for different classes, yet in practice, there are often large gaps in their performance.
New Equivalences Between Interpolation and SVMs: Kernels and Structured Features
The support vector machine (SVM) is a supervised learning algorithm that finds a maximum-margin linear classifier, often after mapping the data to a high-dimensional feature space via the kernel trick.
The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective
Data augmentation (DA) is a powerful workhorse for bolstering performance in modern machine learning.
Network topology change-point detection from graph signals with prior spectral signatures
We assume that signals on the nodes of the graph are regularized by the underlying graph structure via a graph filtering model, which we then leverage to distill the graph topology change-point detection problem to a subspace detection problem.
