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  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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., 2025, In: Vavilovskii Zhurnal Genetiki i Selektsii. 29, 3, p. 467-473 7 p., 14.

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

  8. 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

  9. Deep laser cooling of strontium atoms on 1S0→3P0 transition

    Ya Ilenkov, R., Taichenachev, A. V., Yudin, V. I. & Prudnikov, O. N., 16 Feb 2017, In: Journal of Physics: Conference Series. 793, 1, 4 p., 012011.

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

  10. Deep laser cooling of Mg in dipole trap for frequency standard

    Prudnikov, O. N., Taichenachev, A. V., Yudin, V. I. & Rasel, E. M., 27 Oct 2017, 2017 Joint Conference of the European Frequency and Time Forum and IEEE International Frequency Control Symposium, EFTF/IFC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., p. 432-436 5 p. 8088915. (2017 Joint Conference of the European Frequency and Time Forum and IEEE International Frequency Control Symposium, EFTF/IFC 2017 - Proceedings).

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

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