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Rodomanov A., Kropotov D.
SIAM Journal on Optimization. 2020. Vol. 30. No. 3. P. 1878-1904.
A. Boldyrev, D. Derkach, F. Ratnikov et al.
Journal of Instrumentation. 2020. Vol. 15. P. 1-7.
Dvurechensky P., Eduard Gorbunov, Gasnikov A.
European Journal of Operational Research. 2021. Vol. 290. No. 2. P. 601-621.
Kuznetsov S., Kaytoue M., Belfodil A.
In bk.: International Journal of General Systems. Iss. 49. 2020. P. 271-285.
Kaledin M., Moulines E., Naumov A. et al.
In bk.: Proceedings of Machine Learning Research. Vol. 125: Proceedings of Thirty Third Conference on Learning Theory. 2020. P. 2144-2203.
The Faculty of Computer Science was created with the goal of becoming one of the world’s leading faculties for developers and researchers in data analysis, machine learning, big data, theoretical computer science, bioinformatics, system and software engineering, system programming, and distributed computing. In cooperation with major companies like Yandex, Sberbank, SAS, Samsung, 1C, and many others, the Faculty provides both deep theoretical knowledge and hands-on practical experience in many branches of contemporary computer science.
Two faculty members have their papers accepted to International Conference on Machine Learning that will be held in New York, USA.
First paper, authored by Anton Rodomanov and Dmitry Kropotov, "A Superlinearly-Convergent Proximal Newton-type Method for the Optimization of Finite Sums" proposes a new stochastic optimization method with fast convergence properties that is especially useful in machine learning problems.
Another paper called "Meta-Learning with Memory-Augmented Neural Networks" is co-authored by seniour teaching staff member Sergey Bartunov and is a result of his collaboration with Google DeepMind. In this paper a new neural network architecture is developed that is able to quickly learn new concepts from just a few training examples.