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  1. Deep learning approaches to mid-term forecasting of social-economic and demographic effects of a pandemic

    Devyatkin, D., Otmakhova, Y., Usenko, N., Sochenkov, I. & Budzko, V., Jul 2021, In: Procedia Computer Science. 190, p. 156-163 8 p.

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

  2. Deep learning approach to the estimation of the ratio of reproductive modes in a partially clonal population

    Nikolaeva, T. A., Poroshina, A. A. & Sherbakov, D. Y., Jun 2025, In: Vavilovskii Zhurnal Genetiki i Selektsii. 29, 3, p. 467-473 7 p., 14.

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

  3. Deep Learning for the Precise Peak Detection in High-Resolution LC-MS Data

    Melnikov, A. D., Tsentalovich, Y. P. & Yanshole, V. V., 7 Jan 2020, In: Analytical Chemistry. 92, 1, p. 588-592 5 p.

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

  4. Deep learning segmentation to analyze bubble dynamics and heat transfer during boiling at various pressures

    Malakhov, I., Seredkin, A., Chernyavskiy, A., Serdyukov, V., Mullyadzanov, R. & Surtaev, A., May 2023, In: International Journal of Multiphase Flow. 162, 104402.

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

  5. Deep learning with synthetic photonic lattices for equalization in optical transmission systems

    Pankov, A. V., Sidelnikov, O. S., Vatnik, I. D., Sukhorukov, A. A. & Churkin, D. V., 20 Nov 2019, Real-Time Photonic Measurements, Data Management, and Processing IV. Li, M., Jalali, B. & Asghari, M. H. (eds.). The International Society for Optical Engineering, p. 24 11 p. 111920N. (Proceedings of SPIE - The International Society for Optical Engineering; vol. 11192).

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

  6. Deep machine learning for STEM image analysis

    Nartova, A. V., Matveev, A. V., Kovtunova, L. M. & Okunev, A. G., Nov 2024, In: Mendeleev Communications. 34, 6, p. 774-775 2 p.

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

  7. Deep macroscopic pure-optical potential for laser cooling and trapping of neutral atoms

    Prudnikov, O. N., Ilenkov, R. Y., Taichenachev, A. V., Yudin, V. I. & Bagaev, S. N., Oct 2023, In: Physical Review A. 108, 4, 043107.

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

  8. Deep Methane Oxidation over Pd–Glass Fiber Catalysts Prepared by Chemical Vapor Deposition

    Suknev, A. P., Dorovskikh, S. I., Borisova, D. A., Sadovskaya, E. M., Zhezhera, M., Maximovskii, E. A., Svintsitskii, D. A., Derevshchikov, V. S. & Vikulova, E. S., 17 Aug 2026, In: Catalysis in Industry. 18, 3, p. 292-302 11 p.

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

  9. Deep Multimodal Fusion Network for the Retinogeniculate Visual Pathway Segmentation

    Xie, L., Yang, L., Zeng, Q., He, J., Huang, J., Feng, Y., Amelina, E. & Amelin, M., 2023, Chinese Control Conference, CCC. Institute of Electrical and Electronics Engineers Inc., p. 7946-7950 5 p.

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

  10. Deep neural networks with time-domain synthetic photonic lattices

    Pankov, A. V., Sidelnikov, O. S., Vatnik, I. D., Churkin, D. V. & Sukhorukov, A. A., 2021, European Quantum Electronics Conference, EQEC 2021. The Optical Society, jsiv_p_3. (Optics InfoBase Conference Papers).

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

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