Amortized Variational Inference

Amortized variational inference (AVI) is a machine learning technique aiming to efficiently approximate complex posterior distributions in probabilistic models, particularly in high-dimensional or large-dataset scenarios. Current research focuses on improving the accuracy and scalability of AVI, often employing neural networks (e.g., normalizing flows, variational autoencoders) to learn a function mapping observations directly to approximate posterior parameters, thereby avoiding repeated computations for each data point. This approach finds applications in diverse fields, including inverse problems, cryo-electron microscopy, and generative modeling, offering significant speedups and enabling real-time inference in previously intractable settings. The development of more robust and accurate AVI methods, along with theoretical guarantees on their performance, remains a key focus.