| Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare P Mamoshina, L Ojomoko, Y Yanovich, A Ostrovski, A Botezatu, ... Oncotarget 9 (5), 5665, 2017 | 505 | 2017 |
| Flare: An approach to routing in lightning network P Prihodko, S Zhigulin, M Sahno, A Ostrovskiy, O Osuntokun White Paper (bitfury. com/content/5-white-papers-research …, 2016 | 192 | 2016 |
| Emotion recognition from speech with recurrent neural networks V Chernykh, G Sterling, P Prihodko arXiv preprint arXiv:1701.08071, 2017 | 182 | 2017 |
| Gtapprox: Surrogate modeling for industrial design M Belyaev, E Burnaev, E Kapushev, M Panov, P Prikhodko, D Vetrov, ... Advances in Engineering Software 102, 29-39, 2016 | 71 | 2016 |
| Surrogate modeling of stability constraints for optimization of composite structures S Grihon, E Burnaev, M Belyaev, P Prikhodko Surrogate-based modeling and optimization: Applications in engineering, 359-391, 2013 | 38 | 2013 |
| On a method for constructing ensembles of regression models. EV Burnaev, PV Prikhod'ko Autom. Remote. Control. 74 (10), 1630-1644, 2013 | 26 | 2013 |
| Flare: An approach to routing in lightning network (2016) P Prihodko, S Zhigulin, M Sahno, A Ostrovskiy, O Osuntokun URL: https://bitfury. com/content/downloads/whitepaper_flare_an_approach_to …, 2016 | 20 | 2016 |
| Методология построения суррогатных моделей для аппроксимации пространственно неоднородных функций ЕВ Бурнаев, ПВ Приходько Труды Московского физико-технического института 5 (4 (20)), 122-132, 2013 | 20 | 2013 |
| About hybrid algorithm for tuning of parameters in approximation based on linear expansions in parametric functions E Burnaev, M Belyaev, PV Prihodko Proceedings of the 8th International conference “Intelligent Information …, 2010 | 20 | 2010 |
| Comparison of different techniques for surrogate modeling of stability constraints for composite structures S Grihon, S Alestra, D Bettebghor, E Burnaev, P Prikhodko Proceedings of 1st International Conference on Composite Dynamics, Archanon …, 2012 | 19 | 2012 |
| Surrogate modeling of buckling analysis in support of composite structure optimization S Grihon, S Alestra, E Burnaev, P Prikhodko Proceedings of the 1st International Conference on Composite Dynamics …, 2012 | 16 | 2012 |
| Modeling of nonstationary covariance function of gaussian process using decomposition in dictionary of nonlinear functions E Burnaev, A Zaytsev, M Panov, P Prihodko, Y Yanovich Proceedings of the conference Information Technology and Systems, Gelendzhik …, 2011 | 12 | 2011 |
| Approximation of multidimensional dependency based on an expansion in parametric functions from the dictionary EV Burnaev, MG Belyaev, PV Prihodko Minsk: BSU, 2010 | 10 | 2010 |
| Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare. Oncotarget. 2018 Jan 19; 9 (5 … P Mamoshina, L Ojomoko, Y Yanovich, A Ostrovski, A Botezatu, ... | 10 | |
| On approximation of reserve factors dependency on loads for composite stiffened panels G Sterling, P Prikhodko, E Burnaev, M Belyaev, S Grihon Advanced materials research 1016, 85-89, 2014 | 9 | 2014 |
| Emotion recognition from speech with recurrent neural networks. arXiv 2017 V Chernykh, P Prikhodko arXiv preprint arXiv:1701.08071, 0 | 7 | |
| Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare. Oncotarget. 2018; 9 (5): 5665–90 P Mamoshina, L Ojomoko, Y Yanovich, A Ostrovski, A Botezatu, ... DOI: https://doi. org/10.18632/oncotarget 22345, 5665-90, 2019 | 6 | 2019 |
| Глубокое обучение в задаче распознавания эмоций из речи Г Стерлинг, П Приходько | 6 | 2016 |
| Neural approximation based on regression and boosting E Burnaev, M Belyaev, S Chernova, P Prihodko MMR 2009–Mathematical Methods in Reliability: Theory. Methods. Applications …, 2009 | 6 | 2009 |
| Emotion recognition from speech with recurrent neural networks (2017) V Chernykh, P Prikhodko arXiv preprint arXiv:1701.08071, 0 | 6 | |
