112 citations to https://www.mathnet.ru/rus/danma350
  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. Math., 108:S2 (2023), S529  crossref
  2. 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  crossref
  3. I. K. Romanovskaya, “Planetary biotechnospheres, biotechnosignatures and the search for extraterrestrial intelligence”, International Journal of Astrobiology, 22:6 (2023), 663  crossref
  4. 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  crossref
  5. 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  crossref
  6. 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  crossref
  7. 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  crossref
  8. 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  crossref
  9. D. Castellanos-Nieves, L. García-Forte, “Improving automated machine-learning systems through Green AI”, Applied Sciences, 13:20 (2023), 11583  crossref
  10. 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  crossref
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