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  1. Machine Learning Methods for Compensating Signal Distortions in Fiber-Optic Communication Lines

    Sidelnikov, O. S., Redyuk, A. A. & Fedoruk, M. P., Feb 2024, In: Optoelectronics, Instrumentation and Data Processing. 60, 1, p. 1-10 10 p.

    Research output: Contribution to journal › Article › peer-review

  2. Machine learning for studying mineral-component composition of the Bazhenov Formation based on well-logging and core data

    Temnikova, E. Y., Grubas, S. I. & Glinskikh, V. N., Dec 2021, In: Neftyanoe khozyaystvo - Oil Industry. 12, p. 88-91 4 p., 17.

    Research output: Contribution to journal › Article › peer-review

  3. Machine learning-based pulse characterization in figure-eight mode-locked lasers

    Kokhanovskiy, A., Bednyakova, A., Kuprikov, E., Ivanenko, A., Dyatlov, M., Lotkov, D., Kobtsev, S. & Turitsyn, S., 1 Jul 2019, In: Optics Letters. 44, 13, p. 3410-3413 4 p.

    Research output: Contribution to journal › Article › peer-review

  4. Machine Learning Based Characterisation of Dissipative Solitons

    Кохановский, А. Ю., Беднякова, А. Е., Куприков, Е. А., Иваненко, А. В. & Турицын, С. К., 17 Oct 2019, 2019 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2019. Institute of Electrical and Electronics Engineers Inc., 1 p. 8873378. (2019 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2019).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

  5. Machine learning based characterisation of dissipative solitons

    Kokhanovskiy, A., Bednyakova, A., Kuprikov, E., Ivanenko, A. & Turitsyn, S., 1 Jan 2019, The European Conference on Lasers and Electro-Optics, CLEO_Europe_2019. OSA Publishing, 2019-cj_2_6. (Optics InfoBase Conference Papers; vol. Part F140-CLEO_Europe 2019).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

  6. Machine learning based background rejection for Baikal-GVD neutrino telescope

    Kalashev, O., Kharuk, I. & Rubtsov, G., 2023, In: Journal of Physics: Conference Series. 2438, 1, 5 p., 012099.

    Research output: Contribution to journal › Conference article › peer-review

  7. Machine Learning As a Tool to Accelerate the Search for New Materials for Metal-Ion Batteries

    Osipov, V. T., Gongola, M. I., Morkhova, Y. A., Nemudryi, A. P. & Kabanov, A. A., Dec 2023, In: Doklady Mathematics. 108, Suppl 2, p. S476-S483 8 p.

    Research output: Contribution to journal › Article › peer-review

  8. Machine learning and applications in ultrafast photonics

    Genty, G., Salmela, L., Dudley, J. M., Brunner, D., Kokhanovskiy, A., Kobtsev, S. & Turitsyn, S. K., Feb 2021, In: Nature Photonics. 15, 2, p. 91-101 11 p.

    Research output: Contribution to journal › Review article › peer-review

  9. Lyubavinskoe Gold Deposit (Eastern Transbaikalia): Sources of the Formation and Petrogeochemical Features of Rocks and Ores

    Abramov, B. N., Posohov, V. F. & Kalinin, Y. A., 1 Apr 2019, In: Doklady Earth Sciences. 485, 2, p. 432-438 7 p.

    Research output: Contribution to journal › Article › peer-review

  10. Lymph nodes morphology as predictor natural and premature aging

    Gorchakova, O., Горчаков, В. Н. & Demchenko, G., Jul 2020, Bioinformatics of Genome Regulation and Structure Systems Biology (BGRS/SB-2020): The Twelfth International Multiconference (06-10 July 2020, Novosibirsk, Russia). Novosibirsk: ICG SB RAS: Институт цитологии и генетики СО РАН, p. 642-643 2 p.

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

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