| Machine learning approaches to mild cognitive impairment detection based on structural MRI data and morphometric features MO Zubrikhina, OV Abramova, VE Yarkin, VL Ushakov, AG Ochneva, ... Cognitive Systems Research 78, 87-95, 2023 | 8 | 2023 |
| Ensemble of 3D CNN regressors with data fusion for fluid intelligence prediction M Pominova, A Kuzina, E Kondrateva, S Sushchinskaya, E Burnaev, ... Adolescent Brain Cognitive Development Neurocognitive Prediction: First …, 2019 | 6 | 2019 |
| Resting-state fMRI in preoperative non-invasive mapping in patients with left hemisphere glioma AS Smirnov, TV Melnikova-Pitskhelauri, MG Sharaev, VY Zhukov, ... Zhurnal Voprosy Neirokhirurgii Imeni NN Burdenko 84 (4), 17-25, 2020 | 5 | 2020 |
| Minimax error of interpolation and optimal design of experiments for variable fidelity data A Zaytsev, E Burnaev arXiv preprint arXiv:1610.06731, 2016 | 3 | 2016 |
| Brain Cognitive Architectures Mapping for Neurosurgery: Resting-State fMRI and Intraoperative Validation M Sharaev, T Melnikova-Pitskhelauri, A Smirnov, A Bozhenko, V Yarkin, ... Brain-Inspired Cognitive Architectures for Artificial Intelligence: BICA* AI …, 2021 | 2 | 2021 |
| A Comparison of Regional Brain Volumes in Older Adults With and Without History of COVID-19 TS Syunyakov, MG Sharaev, VB Savilov, OA Karpenko, MV Kurmyshev, ... Consortium Psychiatricum 3 (1), 76-87, 2022 | 1 | 2022 |
| BRATS2021: Exploring Each Sequence in Multi-modal Input for Baseline U-net Performance P Druzhinina, E Kondrateva, A Bozhenko, V Yarkin, M Sharaev, ... International MICCAI Brainlesion Workshop, 194-203, 2021 | 1 | 2021 |
| Topologically-based Variational Autoencoder for Time Series Classification R Rivera-Castro, S Moustafa, P Pilyugina, E Burnaev latinxinai. org, 2020 | 1 | 2020 |
| ABC: A Big CAD Model Dataset For Geometric Deep Learning Supplementary Material S Koch, A Matveev, I Skoltech, Z Jiang, F Williams, A Artemov, E Burnaev, ... DOI: https://doi. org/10.1109/CVPR, 2019 | 1 | 2019 |
| Comparison of resting state and task-based functional MRI in preoperative mapping in patients with brain gliomas AS Smirnov, TV Melnikova-Pitskhelauri, MG Sharaev, VE Yarkin, ... Zhurnal Voprosy Neirokhirurgii Imeni NN Burdenko 86 (4), 33-40, 2022 | | 2022 |
| Machine learning for resting state fMRI-based preoperative mapping: comparison with task-based fMRI and direct cortical stimulation IN Pronin, MG Sharaev, TV Melnikova-Pitskhelauri, AS Smirnov, ... Zhurnal Voprosy Neirokhirurgii Imeni NN Burdenko 86 (4), 25-32, 2022 | | 2022 |
| Towards forecast techniques for business analysts of large commercial data sets using matrix factorization methods R Rivera-Castro, I Nazarov, E Burnaev arXiv preprint arXiv:2009.04359, 2020 | | 2020 |
| fMRI: preprocessing, classification and pattern recognition M Sharaev, A Andreev, A Artemov, A Bernstein, E Burnaev, ... arXiv preprint arXiv:1804.10167, 2018 | | 2018 |
| Meta-Learning for Construction of Resampling Recommendation Systems E Burnaev, P Erofeev, A Papanov CoRR, 2017 | | 2017 |
| 138. DATAFUSION FOR BIOLOGICAL SIMULATIONS: APPLICATION TO TOXINS A Laugerotte, A Zaytsev, E Burnaev, D Khominich, L Pons, S Alestr Abstracts/Toxicon 123 (S2eS90), S2eS90, 2016 | | 2016 |
| Datafusion for biological simulations: Application to toxins A Laugerotte, A Zaytsev, E Burnaev, D Khominich, L Pons, S Alestr Toxicon, S52-S53, 2016 | | 2016 |
| Surrogate models for spacecraft aerodynamic problems M Belyaev, E Burnaev, E Kapushev, S Alestra, M Dormieux, A Cavailles, ... 11th World Congress on Computational Mechanics, WCCM 2014, 5th European …, 2014 | | 2014 |
| Surrogate models for helicopter loads A Struzik, E Burnaev, P Prikhodko, JC Auzet, A Mayan, S Morozov, ... | | 2013 |
| Automated focal cortical dysplasia detection on brain MR images using image analysis N Alsahanova, V Yarkin, M Sharaev, O Bronov, V Bychenko, A Marinets, ... | | |
| Statistical Problems of Manifold Learning for Predictive Modeling EV Burnaev | | |
