3 papers
cs.LG2025
Comparing BFGS and OGR for Second-Order Optimization
Adrian Przybysz, MikoÅaj KoÅek, Franciszek Sobota +1
Estimating the Hessian matrix, especially for neural network training, is a challenging problem due to high dimensionality and cost. In this work, we compare the classical Sherman-…
cs.LG2025
Linear cost mutual information estimation and independence test of similar performance as HSIC
Jarek Duda, Jagoda Bracha, Adrian Przybysz
Evaluation of statistical dependencies between two data samples is a basic problem of data science/machine learning, and HSIC (Hilbert-Schmidt Information Criterion)~\cite{HSIC} is…
cs.LG2025
Improving KAN with CDF normalization to quantiles
Jakub Strawa, Jarek Duda
Data normalization is crucial in machine learning, usually performed by subtracting the mean and dividing by standard deviation, or by rescaling to a fixed range. In copula theory,…