Variance Network

Variance networks encompass a broad range of models aiming to effectively quantify and utilize uncertainty in neural network predictions. Current research focuses on improving efficiency and accuracy of variance estimation, particularly within Bayesian neural networks and diffusion models, employing techniques like fast variance operators and step-aware network architectures. These advancements are crucial for enhancing the reliability and interpretability of machine learning models across diverse applications, from uncertainty quantification in regression tasks to improved controllability in generative models like text-to-speech systems. Furthermore, understanding how variance relates to neural representations is a key area of investigation, with a focus on disentangling factors contributing to overall model performance.