A Stochastic Modified Limited Memory BFGS for Training Deep Neural Networks. - Mahsa Yousefi; Ángeles Martínez Calomardo (SAI (2), 2022)

A stochastic nonmonotone trust-region training algorithm for image classification. - Mahsa Yousefi; Ángeles Martínez Calomardo (SITIS, 2022)

On the efficiency of Stochastic Quasi-Newton Methods for Deep Learning. - Mahsa Yousefi; Angeles Martinez 001 (CoRR, 2022)

Combined First- and Second-Order Directions for Deep Neural Networks Training. - Ángeles Martínez 001; Marco Viola; Mahsa Yousefi (NUMTA (1), 2023)

Deep Neural Networks Training by Stochastic Quasi-Newton Trust-Region Methods. - Mahsa Yousefi; Angeles Martinez 001 (Algorithms, 2023)

Majorization-Minimization for sparse SVMs. - Alessandro Benfenati; Emilie Chouzenoux; Giorgia Franchini; Salla Latva-Aijo; Dominik Narnhofer; Jean-Christophe Pesquet; Sebastian J. Scott; Mahsa Yousefi (CoRR, 2023)

Soil analysis with machine-learning-based processing of stepped-frequency GPR field measurements: Preliminary study. - Chunlei Xu; Michael Pregesbauer; Naga Sravani Chilukuri; Daniel Windhager; Mahsa Yousefi; Pedro Julián; Lothar Ratschbacher (CoRR, 2024)

Fully stochastic trust-region methods with Barzilai-Borwein steplengths. - Stefania Bellavia; Benedetta Morini; Mahsa Yousefi (CoRR, 2024)

A non-monotone trust-region method with noisy oracles and additional sampling. - Natasa Krejic; Natasa Krklec Jerinkic; Angeles Martinez 001; Mahsa Yousefi (Comput. Optim. Appl., 2024)