C Score

"D-Score" and "C-Score" represent distinct metrics used in diverse machine learning applications. Research focuses on improving the accuracy and efficiency of these scores, for example, by developing architecture-agnostic versions (a-DCF) or by incorporating neuroscientific principles (synapse-inspired D-Score). These scores are crucial for evaluating model performance, particularly in safety-critical domains like autonomous driving and speaker verification, and for optimizing training processes such as curriculum learning. Their refinement promises to enhance the reliability and robustness of machine learning models across various fields.