Flow Based Sampling

Flow-based sampling leverages invertible neural networks, often normalizing flows, to efficiently generate samples from complex probability distributions, addressing limitations of traditional methods like Markov Chain Monte Carlo. Current research focuses on improving the accuracy and speed of these samplers, particularly within energy-based models and for applications in lattice field theories and image processing, exploring architectures that mitigate issues like mode collapse and ensure efficient Jacobian determinant calculations. This approach holds significant promise for accelerating simulations in diverse fields, from high-energy physics to traffic optimization, by enabling more accurate and faster sampling of high-dimensional probability distributions.