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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 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

  3. 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

  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, 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 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

  6. 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

  7. 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

  8. 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

  9. Deep Neural Networks with Time-Domain Synthetic Photonic Lattices

    Pankov, A. V., Sidelnikov, O. S., Vatnik, I. D., Churkin, D. V. & Sukhorukov, A. A., Jun 2021, 2021 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2021. Institute of Electrical and Electronics Engineers Inc., (2021 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2021).

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

  10. Deep-red phosphorescent organic–inorganic hybrid Mn(II) complexes based on 2-(diphenylphosphoryl)-N,N-diethylacetamide ligand

    Artem'ev, A. V., Berezin, A. S., Brel, V. K., Morgalyuk, V. P. & Samsonenko, D. G., 1 Jul 2018, In: Polyhedron. 148, p. 184-188 5 p.

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

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