Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks. - Alexander Shevchenko; Marco Mondelli (ICML, 2020)

Security management in large-scale heterogeneous network systems based on intelligent information security services. - Valery Lipatnikov; Dmitry Saharov; Valery Tikhonov; Alexander Shevchenko; Anna Polyanicheva (ICFNDS, 2021)

Noninvasive Glioma Grading with Deep Learning: A Pilot Study. - Gleb Danilov; Vladislav Korolev; Michael Shifrin; Eugene Ilyushin; Narek Maloyan; Daniel Saada; Timur Ishankulov; Ramin Afandiev; Alexander Shevchenko; Tatyana Konakova; Tatyana Tsukanova; Svetlana Shugay; et al. (MedInfo, 2021)

Didactic Adaptation of Medical Information for the Formation of Valeological Competence in Engineering and Pedagogical Training. - Denys Kovalenko; Alexander Shevchenko; Juergen Koeberlein-Kerler; Liudmyla V. Shtefan; Viktoriia Kovalska (ICL (2), 2022)

Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods. - Alexander Shevchenko; Kevin Kögler; Hamed Hassani; Marco Mondelli (CoRR, 2022)

Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks. - Alexander Shevchenko; Vyacheslav Kungurtsev; Marco Mondelli (J. Mach. Learn. Res., 2022)

Reproducibility of Radiomic Features in Glial Brain Tumors. - Gleb Danilov; Alexander Shevchenko; Ramin Afandiev; Artem Batalov; Eudard Pogosbekyan; Natalia Zakharova; Svetlana Shugay; Igor Pronin (MIE, 2024)

Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth. - Kevin Kögler; Alexander Shevchenko; Hamed Hassani; Marco Mondelli (CoRR, 2024)