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Multi-objective optimization applied to unified second level cache memory hierarchy tuning aiming at energy and performance optimization.

, and . Appl. Soft Comput., (2016)

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Multi-objective optimization applied to unified second level cache memory hierarchy tuning aiming at energy and performance optimization., and . Appl. Soft Comput., (2016)A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?, and . SIBGRAPI, page 9-16. IEEE, (2020)Recognizing Handwritten Mathematical Expressions of Vertical Addition and Subtraction., , , , , , , , and . SIBGRAPI, page 73-78. IEEE, (2023)A novel multi-objective grammar-based framework for the generation of Convolutional Neural Networks., , , , , , , and . Expert Syst. Appl., (2023)Self-supervised Mean Teacher for Semi-supervised Chest X-Ray Classification., , , , , and . MLMI@MICCAI, volume 12966 of Lecture Notes in Computer Science, page 426-436. Springer, (2021)EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels., , , , and . WACV, page 3606-3614. IEEE, (2021)An Optimization Mechanism Intended for Two-Level Cache Hierarchy to Improve Energy and Performance Using the NSGAII Algorithm., , , , and . SBAC-PAD, page 19-26. IEEE Computer Society, (2008)Segmentation of Mammography by Applying GrowCut for Mass Detection., , and . MedInfo, volume 192 of Studies in Health Technology and Informatics, page 87-91. IOS Press, (2013)Improving Mass Detection in Mammography Images: A Study of Weakly Supervised Learning and Class Activation Map Methods., and . SIBGRAPI, page 139-144. IEEE, (2023)A Study on the Impact of Data Augmentation for Training Convolutional Neural Networks in the Presence of Noisy Labels., , and . CoRR, (2022)