Generative Flow

Generative flows are a class of generative models that learn to transform a simple probability distribution into a complex, target distribution by constructing a sequence of invertible transformations. Current research focuses on improving the efficiency and robustness of these flows, particularly through the development of novel architectures like GFlowNets and the integration of optimal transport methods, and their application to diverse problems including drug design, privacy-preserving machine learning, and image generation. This approach offers advantages in handling high-dimensional data, generating diverse samples, and enabling flexible conditional sampling, leading to significant advancements in various fields.