Cross Entropy Loss

Cross-entropy loss is a widely used objective function in machine learning, primarily for training classification models by minimizing the difference between predicted and true probability distributions. Current research focuses on addressing its limitations, particularly in large-scale applications like recommender systems and large language models, where modifications like scalable or reduced cross-entropy are being developed to improve efficiency and memory usage. Furthermore, research explores alternative loss functions or combinations with other methods (e.g., contrastive learning, Wasserstein loss) to enhance model performance, calibration, and robustness, especially in scenarios with limited data or imbalanced classes. These advancements have significant implications for improving the accuracy, efficiency, and reliability of various machine learning applications.