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A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?, и . SIBGRAPI, стр. 9-16. IEEE, (2020)Multi-objective optimization applied to unified second level cache memory hierarchy tuning aiming at energy and performance optimization., и . Appl. Soft Comput., (2016)Recognizing Handwritten Mathematical Expressions of Vertical Addition and Subtraction., , , , , , , , и . SIBGRAPI, стр. 73-78. IEEE, (2023)ANNE: Adaptive Nearest Neighbours and Eigenvector-based sample selection for robust learning with noisy labels., и . Pattern Recognit., (2025)An ESL Approach for Energy Consumption Analysis of Cache Memories in SoC Platforms., , , , , , и . Int. J. Reconfigurable Comput., (2011)CCT: A Cyclic Co-Teaching Approach to Train Deep Neural Networks With Noisy Labels., , , и . IEEE Access, (2025)EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels., , , , и . WACV, стр. 3606-3614. IEEE, (2021)Self-supervised Mean Teacher for Semi-supervised Chest X-Ray Classification., , , , , и . MLMI@MICCAI, том 12966 из Lecture Notes in Computer Science, стр. 426-436. Springer, (2021)Segmentation of Mammography by Applying GrowCut for Mass Detection., , и . MedInfo, том 192 из Studies in Health Technology and Informatics, стр. 87-91. IOS Press, (2013)An Optimization Mechanism Intended for Two-Level Cache Hierarchy to Improve Energy and Performance Using the NSGAII Algorithm., , , , и . SBAC-PAD, стр. 19-26. IEEE Computer Society, (2008)