Implicit Distribution

Implicit distribution learning focuses on representing complex probability distributions using neural networks, aiming to overcome limitations of traditional explicit methods in handling high-dimensional, multimodal, or uncertain data. Current research emphasizes the development of novel architectures like semi-implicit variational inference and implicit neural representations, often incorporating techniques such as kernel methods or score matching to improve efficiency and stability. This approach finds applications in diverse fields, including Bayesian inference, image reconstruction (e.g., CT scans), and generative modeling of structured data like graphs, offering improved accuracy and efficiency in handling uncertainty and complex relationships within data.