Conditional Model

Conditional models aim to learn the probability distribution of a target variable given one or more conditioning variables, enabling tailored predictions and generation. Current research focuses on improving the efficiency and accuracy of these models across diverse applications, employing architectures like autoregressive models, normalizing flows, and diffusion models, often enhanced by techniques such as conditional Karhunen-Loève expansions or teacher-forcing. This field is significant for its broad applicability, ranging from personalized video summarization and controllable image generation to robust anomaly detection in complex systems like particle accelerators and improved data assimilation in inverse problems. The development of more efficient and accurate conditional models has substantial implications for various scientific disciplines and technological advancements.