Calibration of Ordinal Regression Networks
The episode discusses "Calibration of Ordinal Regression Networks," a paper addressing two major issues in ordered classification: respecting label order and providing accurate confidence estimates. Hosts explain the proposed ORCU loss function, which successfully achieves both goals, making models safer for high-stakes fields like medicine.