87 citations · 221 across the 37 of their papers we have counts for
10 papers · 1 filter
Early Warning Prediction with Automatic Labeling in Epilepsy Patients
Peng Zhang, Ting Gao, Jin Guo +2
Early warning for epilepsy patients is crucial for their safety and well-being, in particular to prevent or minimize the severity of seizures. Through the patients' EEG data, we pr…
Efficient Privacy-Preserving Convolutional Spiking Neural Networks with FHE
Pengbo Li, Huifang Huang, Ting Gao +2
With the rapid development of AI technology, we have witnessed numerous innovations and conveniences. However, along with these advancements come privacy threats and risks. Fully H…
Well-posedness and averaging principle for Lévy-type McKean-Vlasov stochastic differential equations under local Lipschitz conditions
Ying Chao, Jinqiao Duan, Ting Gao +1
In this paper, we investigate a class of McKean-Vlasov stochastic differential equations under Lévy-type perturbations. We first establish the existence and uniqueness theorem for…
Early warning indicators via latent stochastic dynamical systems
Lingyu Feng, Ting Gao, Wang Xiao +1
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 br…
Privacy-Preserving Discretized Spiking Neural Networks
Pengbo Li, Ting Gao, Huifang Huang +4
The rapid development of artificial intelligence has brought considerable convenience, yet also introduces significant security risks. One of the research hotspots is to balance da…
Learning Stochastic Dynamical Systems as an Implicit Regularization with Graph Neural Networks
Jin Guo, Ting Gao, Yufu Lan +3
Stochastic Gumbel graph networks are proposed to learn high-dimensional time series, where the observed dimensions are often spatially correlated. To that end, the observed randomn…
Reservoir Computing with Error Correction: Long-term Behaviors of Stochastic Dynamical Systems
Cheng Fang, Yubin Lu, Ting Gao +1
The prediction of stochastic dynamical systems and the capture of dynamical behaviors are profound problems. In this article, we propose a data-driven framework combining Reservoir…
Deep Reinforcement Learning in Finite-Horizon to Explore the Most Probable Transition Pathway
Jin Guo, Ting Gao, Peng Zhang +2
In many scientific and engineering problems, noise and nonlinearity are unavoidable, which could induce interesting mathematical problem such as transition phenomena. This paper fo…
Multi-task Meta Label Correction for Time Series Prediction
Luxuan Yang, Ting Gao, Wei Wei +3
Time series classification faces two unavoidable problems. One is partial feature information and the other is poor label quality, which may affect model performance. To address th…
Detecting the Most Probable High Dimensional Transition Pathway Based on Optimal Control Theory
Jianyu Chen, Ting Gao, Yang Li +1
Many natural systems exhibit phase transition where external environmental conditions spark a shift to a new and sometimes quite different state. Therefore, detecting the behavior…