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Publications in Math-Net.Ru |
Citations |
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2023 |
| 1. |
A. Kazakov, S. Denisova, I. Barsola, E. Kalugina, I. Molchanova, I. Egorov, A. Kosterina, E. Tereshchenko, L. Shutikhina, I. Doroshchenko, N. Sotiriadi, S. Budennyy, “ESGify: Automated classification of environmental, social and corporate governance risks”, Dokl. RAN. Math. Inf. Proc. Upr., 514:2 (2023), 417–430   ; Dokl. Math., 108:suppl. 2 (2023), S529–S540 |
| 2. |
T. D. Kulikova, E. Yu. Kovtun, S. A. Budennyy, “Do we benefit from the categorization of the news flow in the stock price prediction problem?”, Dokl. RAN. Math. Inf. Proc. Upr., 514:2 (2023), 385–394 ; Dokl. Math., 108:suppl. 2 (2023), S503–S510 |
| 3. |
M. Tiutiulnikov, V. Lazarev, A. Korovin, N. Zakharenko, I. Doroshchenko, S. Budennyy, “Eco4cast: Bridging predictive scheduling and cloud computing for reduction of carbon emissions for ML models training”, Dokl. RAN. Math. Inf. Proc. Upr., 514:2 (2023), 318–332 ; Dokl. Math., 108:suppl. 2 (2023), S443–S455 |
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2022 |
| 4. |
S. A. Budennyy, V. D. Lazarev, N. N. Zakharenko, A. N. Korovin, O. A. Plosskaya, D. V. Dimitrov, V. S. Akhripkin, I. V. Pavlov, I. V. Oseledets, I. S. Barsola, I. V. Egorov, A. A. Kosterina, L. E. Zhukov, “eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI”, Dokl. RAN. Math. Inf. Proc. Upr., 508 (2022), 134–145 ; Dokl. Math., 106:suppl. 1 (2022), S118–S128 |
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