no code implementations • 19 Oct 2023 • David Liu, Zhengkun Li, Zihao Wu, Changying Li
This work specifically tackles the first challenge by proposing a novel Digital-Twin(DT)MARS-CycleGAN model for image augmentation to improve our Modular Agricultural Robotic System (MARS)'s crop object detection from complex and variable backgrounds.
no code implementations • 1 May 2023 • Lequan Lin, Zhengkun Li, Ruikun Li, Xuliang Li, Junbin Gao
Diffusion models, a family of generative models based on deep learning, have become increasingly prominent in cutting-edge machine learning research.
no code implementations • 21 Feb 2022 • Zhengkun Li
We propose two methodology, one is a semiparametric combination framework that can jointly produce combined value at risk and expected shortfall forecasts, another one is a parametric regression framework named as Quantile-ES regression that can produce combined expected shortfall forecasts.
no code implementations • 23 Jan 2020 • Zhengkun Li, Minh-Ngoc Tran, Chao Wang, Richard Gerlach, Junbin Gao
Value-at-Risk (VaR) and Expected Shortfall (ES) are widely used in the financial sector to measure the market risk and manage the extreme market movement.