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  1. Superposition as Data Augmentation using LSTM and HMM in Small Training Sets

    Павловский, Е. Н. & Сивасвами, А., 24 Oct 2019, Daejeon, South Korea: Cornell University, 5 p.

    Research output: Working paper

  2. Seismic Inversion for Fracture Model Reconstruction: From 1D Inversion to Machine Learning

    Protasov, M., Kenzhin, R., Dmitrachkov, D. & Pavlovskiy, E., 2023, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer, p. 99-109 11 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13957 LNCS).

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

  3. Reducing over-smoothness in speech synthesis using Generative Adversarial Networks

    Pavlovskiy, E. N. & Шэн, Л., 2018

    Research output: Other contribution › Research

  4. Reducing over-smoothness in speech synthesis using Generative Adversarial Networks

    Sheng, L. & Pavlovskiy, E. N., Oct 2019, SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings. Institute of Electrical and Electronics Engineers Inc., p. 972-974 3 p. 8957862. (SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings).

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

  5. Problems and Prospectives of Big Data Storage and Processing Standartization

    Pavlovskiy, E. N., Oct 2019, SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings. Institute of Electrical and Electronics Engineers Inc., p. 995-998 4 p. 8958046. (SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings).

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

  6. Multi-class Brain Tumor Segmentation via Multi-sequences MRI Mixture Data Preprocessing

    Letyagin, A., Golushko, S., Amelin, M., Tuchinov, B., Amelina, E., Tolstokulakov, N., Pavlovskiy, E. & Groza, V., Jul 2020, Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020: International symposium will take place in the frame of 12th International Multiconference “Bioinformatics of Genome Regulation and Structure/Systems Biology”. Institute of Electrical and Electronics Engineers Inc., p. 185-189 5 p. 9214645. (Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020).

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

  7. Multi-Class Brain Tumor Segmentation via 3d and 2d Neural Networks

    Pnev, S., Groza, V., Tuchinov, B., Amelina, E., Pavlovskiy, E., Tolstokulakov, N., Amelin, M., Golushko, S. & Letyagin, A., 2022, ISBI 2022 - Proceedings: 2022 IEEE International Symposium on Biomedical Imaging. IEEE Computer Society, 5 p. (Proceedings - International Symposium on Biomedical Imaging; vol. 2022-March).

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

  8. Improving Brain Tumor Multiclass Classification With Semantic Features

    Sao Khue, L. M. & Pavlovskiy, E., 2022, Proceedings - 2022 Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2022. Institute of Electrical and Electronics Engineers Inc., p. 150-154 5 p. (Proceedings - 2022 Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2022).

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

  9. High-quality Speech Synthesis Using Super-resolution Mel-Spectrogram

    Pavlovskiy, E., Sheng, L. & Huang, D-Y., 3 Dec 2019, 6 p.

    Research output: Working paper

  10. Harnessing Ensemble Machine Learning Models for Timely Diagnosis of Breast Cancer Metastasis: A Case Study on CatBoost, XGBoost, and LGBM

    Luu, M. S. K., Banerjee, S., Pavlovskiy, E. N. & Tuchinov, B. N., 2024, International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM. IEEE Computer Society, p. 2320-2325 6 p. (International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM).

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

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