Likelihood Loss

Likelihood loss functions are central to training many machine learning models, aiming to maximize the probability of observed data given the model's parameters. Current research focuses on improving the efficiency and accuracy of likelihood-based training, exploring alternatives like strictly proper scoring rules and modifications such as the Rician likelihood loss for specific data types (e.g., MRI) and incorporating them into various architectures including neural networks (e.g., convolutional, transformer, and graph neural networks). These advancements enhance model performance in diverse applications, from language generation and precipitation downscaling to medical image analysis and financial forecasting, by providing more accurate and uncertainty-aware predictions.