On the Comparison of Optimizers for Imbalanced Learning
This work studies idealized optimizers geometries in continuous time to model small-step training in deep learning and characterize a region where majority losses are optimized regardless of the admissible minority structure.
Clément Lezane, François Bachoc, Jérôme Bolte et al.
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