Autoregressive Flow

Autoregressive flows are a class of generative models using invertible transformations to efficiently estimate complex probability distributions. Current research focuses on improving scalability and training stability through novel architectures like transformer-based flows and structured networks that incorporate conditional independence assumptions, as well as exploring alternative training objectives beyond maximum likelihood estimation, such as those based on energy functions or quantile regression. These advancements enhance the accuracy and efficiency of density estimation, impacting diverse fields including high-energy physics, causal inference, and time series forecasting by enabling more accurate probabilistic modeling of high-dimensional data.