3 citations · 7 across the 16 of their papers we have counts for
6 papers · 1 filter
Signed Graph Neural Ordinary Differential Equation for Modeling Continuous-time Dynamics
Lanlan Chen, Kai Wu, Jian Lou +1
Modeling continuous-time dynamics constitutes a foundational challenge, and uncovering inter-component correlations within complex systems holds promise for enhancing the efficacy…
Machine learning of network inference enhancement from noisy measurements
Kai Wu, Yuanyuan Li, Jing Liu
Inferring networks from observed time series data presents a clear glimpse into the interconnections among nodes. Network inference models, when dealing with real-world open cases,…
Rapid Plug-in Defenders
Kai Wu, Yujian Betterest Li, Jian Lou +3
In the realm of daily services, the deployment of deep neural networks underscores the paramount importance of their reliability. However, the vulnerability of these networks to ad…
B2Opt: Learning to Optimize Black-box Optimization with Little Budget
Xiaobin Li, Kai Wu, Xiaoyu Zhang +2
The core challenge of high-dimensional and expensive black-box optimization (BBO) is how to obtain better performance faster with little function evaluation cost. The essence of th…
DECN: Evolution Inspired Deep Convolution Network for Black-box Optimization
Kai Wu, Xiaobin Li, Penghui Liu +1
Evolutionary algorithms (EAs) have emerged as a powerful framework for optimization, especially for black-box optimization. Existing evolutionary algorithms struggle to comprehend…
Discover governing differential equations from evolving systems
Yuanyuan Li, Kai Wu, Jing Liu
Discovering the governing equations of evolving systems from available observations is essential and challenging. In this paper, we consider a new scenario: discovering governing e…