no code implementations • 9 Oct 2023 • Peng Zhang, Ting Gao, Jin Guo, Jinqiao Duan, Sergey Nikolenko
Early warning for epilepsy patients is crucial for their safety and well-being, in particular to prevent or minimize the severity of seizures.
no code implementations • 7 Sep 2023 • Lingyu Feng, Ting Gao, Wang Xiao, Jinqiao Duan
Detecting early warning indicators for abrupt dynamical transitions in complex systems or high-dimensional observation data is essential in many real-world applications, such as brain diseases, natural disasters, and engineering reliability.
no code implementations • 12 Jul 2023 • Jin Guo, Ting Gao, Yufu Lan, Peng Zhang, Sikun Yang, Jinqiao Duan
To that end, the observed randomness and spatial-correlations are captured by learning the drift and diffusion terms of the stochastic differential equation with a Gumble matrix embedding, respectively.
1 code implementation • 1 May 2023 • Cheng Fang, Yubin Lu, Ting Gao, Jinqiao Duan
The prediction of stochastic dynamical systems and the capture of dynamical behaviors are profound problems.
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 • 23 Jan 2023 • Huifang Huang, Ting Gao, Pengbo Li, Jin Guo, Peng Zhang, Nan Du
With the fast development of quantitative portfolio optimization in financial engineering, lots of AI-based algorithmic trading strategies have demonstrated promising results, among which reinforcement learning begins to manifest competitive advantages.
no code implementations • 12 Dec 2022 • Ting Gao, Rodrigo Kappes Marques, Lei Yu
We then present a novel model named Graph Spatial-Temporal Network Based on Causal Insight (GT-CausIn), where prior learned causal information is integrated with graph diffusion layers and temporal convolutional network (TCN) layers.
no code implementations • 30 May 2022 • Huifang Huang, Ting Gao, Yi Gui, Jin Guo, Peng Zhang
Reinforcement learning (RL) is gaining attention by more and more researchers in quantitative finance as the agent-environment interaction framework is aligned with decision making process in many business problems.
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 • 31 Mar 2022 • Wei Wei, Ting Gao, Jinqiao Duan, Xiaoli Chen
One of the challenges to calculate the most likely transition path for stochastic dynamical systems under non-Gaussian L\'evy noise is that the associated rate function can not be explicitly expressed by paths.
1 code implementation • 31 Jan 2022 • Cheng Fang, Yubin Lu, Ting Gao, Jinqiao Duan
Recently, extracting data-driven governing laws of dynamical systems through deep learning frameworks has gained a lot of attention in various fields.
1 code implementation • 25 Nov 2021 • Luxuan Yang, Ting Gao, Yubin Lu, Jinqiao Duan, Tao Liu
In this article, we employ a collection of stochastic differential equations with drift and diffusion coefficients approximated by neural networks to predict the trend of chaotic time series which has big jump properties.
no code implementations • 29 Jul 2021 • Yubin Lu, Romit Maulik, Ting Gao, Felix Dietrich, Ioannis G. Kevrekidis, Jinqiao Duan
Specifically, the learned map is a multivariate normalizing flow that deforms the support of the reference density to the support of each and every density snapshot in time.