Differentiable Loss

Differentiable loss functions are crucial for training machine learning models, enabling efficient optimization through gradient-based methods. Recent research focuses on developing differentiable surrogates for non-differentiable metrics (like F-beta score and confusion matrix-based metrics) and designing novel loss functions tailored to specific tasks, such as improving model calibration in image segmentation or enhancing the efficiency of search algorithms like A*. This work addresses limitations of existing loss functions, leading to improved model performance, better interpretability, and more efficient training processes across various applications. The resulting advancements have significant implications for diverse fields, including medical image analysis and automated planning.