no code implementations • 6 Sep 2023 • Zhenyuan Liu, Tong Jia, Xingyu Xing, Jianfeng Wu, Junyi Chen
RCCycleGAN is based on the generative adversarial network (GAN) and can generate images of light, medium, and heavy rain.
no code implementations • 24 Jun 2023 • Zhenyuan Liu, Bart P. G. Van Parys, Henry Lam
In data-driven optimization, sample average approximation (SAA) is known to suffer from the so-called optimizer's curse that causes an over-optimistic evaluation of the solution performance.
no code implementations • 4 Apr 2022 • Mansur Arief, Zhepeng Cen, Zhenyuan Liu, Zhiyuang Huang, Henry Lam, Bo Li, Ding Zhao
In this work, we present Deep Importance Sampling (Deep IS) framework that utilizes a deep neural network to obtain an efficient IS that is on par with the state-of-the-art, capable of reducing the required sample size 43 times smaller than the naive sampling method to achieve 10% relative error and while producing an estimate that is much less conservative.