no code implementations • 20 Jan 2024 • Min Dai, Hanqing Jin, Xi Yang
We propose an innovative data-driven option pricing methodology that relies exclusively on the dataset of historical underlying asset prices.
no code implementations • 19 Dec 2023 • Min Dai, Yuchao Dong, Yanwei Jia, Xun Yu Zhou
We study Merton's expected utility maximization problem in an incomplete market, characterized by a factor process in addition to the stock price process, where all the model primitives are unknown.
no code implementations • 29 Jun 2023 • Yue Shi, Liangxiu Han, Pablo González-Moreno, Darren Dancey, Wenjiang Huang, Zhiqiang Zhang, Yuanyuan Liu, Mengning Huan, Hong Miao, Min Dai
Specifically, unlike the existing CNN models, the main components of the proposed model include: 1) a fast Fourier convolutional block, a newly fast Fourier transformation kernel as the basic perception unit, to substitute the traditional convolutional kernel to capture both local and global responses to plant stress in various time-scale and improve computing efficiency with reduced learning parameters in Fourier domain; 2) Capsule Feature Encoder to encapsulate the extracted features into a series of vector features to represent part-to-whole relationship with the hierarchical structure of the host-stress interactions of the specific stress.
1 code implementation • 9 Mar 2023 • Luxuan Yang, Ting Gao, Wei Wei, Min Dai, Cheng Fang, Jinqiao Duan
To address the above issues, we create a label correction method to time series data with meta-learning under a multi-task framework.
no code implementations • ICCV 2023 • Lei Wang, Min Dai, Jianan He, Jingwei Huang
Our key idea is using primitive graph as a unified representation of vector maps and formulating shape regularization and topology reconstruction as primitive graph reconstruction problems that can be solved in the same framework.
no code implementations • 28 Jun 2022 • Lei Wang, Min Dai, Jianan He, Jingwei Huang, Mingwei Sun
Then, we convert vector shape prediction, regularization, and topology reconstruction into a unique primitive graph learning problem.
no code implementations • 9 May 2022 • Lingyu Feng, Ting Gao, Min Dai, Jinqiao Duan
Multiscale stochastic dynamical systems have been widely adopted to a variety of scientific and engineering problems due to their capability of depicting complex phenomena in many real world applications.
1 code implementation • 24 Dec 2020 • Yaquan Zhang, Qi Wu, Nanbo Peng, Min Dai, Jing Zhang, Hu Wang
The essence of multivariate sequential learning is all about how to extract dependencies in data.
no code implementations • 26 May 2020 • Feng Ye, Zhijie Xia, Min Dai, Zhisheng Zhang
Sensor signals acquired in the industrial process contain rich information which can be analyzed to facilitate effective monitoring of the process, early detection of system anomalies, quick diagnosis of fault root causes, and intelligent system design and control.