112 citations to https://www.mathnet.ru/rus/danma350
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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. Math., 108:S2 (2023), S529
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L. Bouza, A. Bugeau, L. Lannelongue, “How to estimate carbon footprint when training deep learning models? A guide and review”, Environ. Res. Commun., 5:11 (2023), 115014
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I. K. Romanovskaya, “Planetary biotechnospheres, biotechnosignatures and the search for extraterrestrial intelligence”, International Journal of Astrobiology, 22:6 (2023), 663
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I. Semenkov, N. Fedosov, I. Makarov, A. Ossadtchi, “Real-time low latency estimation of brain rhythms with deep neural networks”, J. Neural Eng., 20:5 (2023), 056008
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A. Ghalkha, Ch. Ben Issaid, A. Elgabli, M. Bennis, “DIN: A decentralized inexact Newton algorithm for consensus optimization”, ICC 2023 - IEEE International Conference on Communications (Rome, Italy, 2023), 2023, 4391
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J. Castaño, S. Martínez-Fernández, X. Franch, J. Bogner, “Exploring the carbon footprint of Hugging Face's ML models: a repository mining study”, 2023 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) (New Orleans, LA, USA, 2023), 2023, 1
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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. Math., 108:S2 (2023), S443
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S. Lovrenčić, “The role of knowledge management in transition to Industry 5.0”, 2023 46th MIPRO ICT and Electronics Convention (MIPRO) (Opatija, Croatia, 2023), 2023, 1076
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D. Castellanos-Nieves, L. García-Forte, “Improving automated machine-learning systems through Green AI”, Applied Sciences, 13:20 (2023), 11583
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C. Jean-Quartier, K. Bein, L. Hejny, E. Hofer, A. Holzinger, F. Jeanquartier, “The cost of understanding—XAI algorithms towards sustainable ML in the view of computational cost”, Computation, 11:5 (2023), 92