Up to 100 Gbit/s intellectual packets processing Discover people behavior under traffic
Deep learning technique FRiS-methodology and other kernel functions Cutting edge face recognition technology Speaker identification Speech recognition Text classification
True Data Scientist solves the problem by combining the hardcore science and breakthrough data mining technologies with inexplicable art of human understanding. We propse an unique combination of Deep learning technique with a strict explainability of original FRiS method. Function of Rival Similarity allows us to make discovered patterns undestandable, which makes it a competetive advantage in case of reasonable decision making.
Storage Technologies, Project management, Python.
Business Analysis, Business Scaling, Knowledge Management, Data Warehousing.
Statistics & Algorithms, Machine Learning, Natural Language Processing, Artificial Intelligence, Decision-Making Theory, Signal Processing, and Deep Learning.
We provide an entry point to the network of hi-qualified companies and institutions of Siberian Branch of Russian Academy of Sciences.
MSc, Research Engineer
MSc, Reseearch Engineer
MSc, Research engineer, superpostition expert
Welcome to our team!
1. Sheng L., Pavlovskiy E. N. Reducing over-smoothness in speech synthesis using Generative Adversarial Networks // Data Science and Engineering (SSDSE), 2018 Siberian Symposium on. IEEE. — 2018. URL: https://arxiv.org/pdf/1810.10989.pdf
2. E.N.Pavlovskiy. Problems and prospectives of Big Data storage and processing standardization // Data Science and Engineering (SSDSE), 2018 Siberian Symposium on. IEEE. — 2018.
3. A. Sivaswamy, E.N.Pavlovskiy. Comparing Speech Classification of Russian digits by HMM and LSTM on small training set // Data Science and Engineering (SSDSE), 2018 Siberian Symposium on. IEEE. — 2018.
4. S.A. Alyamkin, N.A. Nikolenko, E.N. Pavlovskiy, V.V. Dyubanov. FRiS-censoring of reference sample in face recognition task by deep neural networks // Data Science and Engineering (SSDSE), 2017 Siberian Symposium on. IEEE. — 2017. URL: http://ieeexplore.ieee.org/document/8071961/.
5. Pavlovsky E. N., Pakulich D. V., Pospelov S. O. Restoration of the 3D Skull Defect Model Based on Deep Neural Networks. Vestnik NSU. Series: Information Technologies, 2017, vol. 15, no. 3, p. 74–78. (In Russ.) URL: http://nsu.ru/xmlui/bitstream/handle/nsu/13448/08.pdf?sequence=1&isAllowed=y.
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