Negative Log Likelihood

Negative log-likelihood (NLL) is a crucial metric in statistical modeling, used to evaluate the goodness of fit of a probability distribution to observed data; minimizing NLL is equivalent to maximizing likelihood. Current research focuses on improving NLL-based training for various models, including generative models (like diffusion models and VAEs), regression models (incorporating heteroscedasticity and addressing covariance estimation challenges), and even spiking neural networks. These advancements aim to enhance model robustness to noise, improve accuracy in complex tasks such as image generation and anomaly detection, and lead to more efficient and reliable algorithms across diverse applications.