Multiplicative learning from observation-prediction ratios

ArXi:2503.10144v2 Announce Type: replace-cross Additive parameter updates, as used in gradient descent and its adaptive extensions, underpin most modern machine-learning optimization. Yet, such additive schemes often demand numerous iterations and intricate learning-rate schedules to cope with scale and curvature of loss functions. Here we