SoP is a universal calibration strategy that resolves multi-target learning conflicts by optimizing each prediction target independently while keeping the backbone frozen. Achieves up to 22% improvement even with simple MLP Plugs.
Features
Forecasting Models - Non-collective calibrating strategy for forecasting.
