Efficient sampling generation from explicit densities via Normalizing Flows
Institut de Física d´Altes Energies (IFAE) - Barcelona Institute of Science and Technology (BIST)（高能物理研究所（IFAE）-巴塞罗那科学技术研究院（BIST））；Aplicaciones en Informática Avanzada (AIA)（高级信息技术应用公司（AIA））；University of Geneva（日内瓦大学）
英文摘要：
For many applications, such as computing the expected value of different magnitudes, sampling from a known probability density function, the target density, is crucial but challenging through the inverse transform. In these cases, rejection and importance sampling require suitable proposal densities, which can be evaluated and sampled from efficiently. We will present a method based on normalizing flows, proposing a solution for the common problem of exploding reverse Kullback-Leibler divergence due to the target density having values of 0 in regions of the flow transformation. The performance of the method will be demonstrated using a multi-mode complex density function.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
