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A Unified Framework for Assessing Energy Efficiency of Machine Learning.

, , , и . PKDD/ECML Workshops (1), том 1752 из Communications in Computer and Information Science, стр. 39-54. Springer, (2022)

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Matthias Jakobs

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Explainable online ensemble of deep neural network pruning for time series forecasting., , и . Mach. Learn., 111 (9): 3459-3487 (2022)Explainable Quantum Machine Learning., , , , , и . CoRR, (2023)Evaluation of the Application of Smart Glasses for Decentralized Control Systems in Logistics., , , , и . ITSC, стр. 4470-4476. IEEE, (2019)Shapley Values with Uncertain Value Functions., , , , и . IDA, том 13876 из Lecture Notes in Computer Science, стр. 156-168. Springer, (2023)Online Deep Hybrid Ensemble Learning for Time Series Forecasting., и . ECML/PKDD (5), том 14173 из Lecture Notes in Computer Science, стр. 156-171. Springer, (2023)SancScreen: Towards a Real-world Dataset for Evaluating Explainability Methods., , , и . LWDA, том 3341 из CEUR Workshop Proceedings, стр. 33-44. CEUR-WS.org, (2022)Explainable Adaptive Tree-based Model Selection for Time-Series Forecasting., и . ICDM, стр. 180-189. IEEE, (2023)Harnessing Prior Knowledge for Explainable Machine Learning: An Overview., , , , , , , , и . SaTML, стр. 450-463. IEEE, (2023)Explainable Online Deep Neural Network Selection Using Adaptive Saliency Maps for Time Series Forecasting., , и . ECML/PKDD (1), том 12975 из Lecture Notes in Computer Science, стр. 404-420. Springer, (2021)A Unified Framework for Assessing Energy Efficiency of Machine Learning., , , и . PKDD/ECML Workshops (1), том 1752 из Communications in Computer and Information Science, стр. 39-54. Springer, (2022)