3 citations · 7 across the 16 of their papers we have counts for
16 papers
Textual Self-attention Network: Test-Time Preference Optimization through Textual Gradient-based Attention
Shibing Mo, Haoyang Ruan, Kai Wu +1
Large Language Models (LLMs) have demonstrated remarkable generalization capabilities, but aligning their outputs with human preferences typically requires expensive supervised fin…
Silencer: Robust Community Detection by Silencing of Noisy Pixels
Kai Wu, Ziang Xie, Jing Liu
Real-world networks carry all kinds of noise, resulting in numerous challenges for community detection. Further improving the performance and robustness of community detection has…
AutoSGNN: Automatic Propagation Mechanism Discovery for Spectral Graph Neural Networks
Shibing Mo, Kai Wu, Qixuan Gao +2
In real-world applications, spectral Graph Neural Networks (GNNs) are powerful tools for processing diverse types of graphs. However, a single GNN often struggles to handle differe…
Exploring Knowledge Transfer in Evolutionary Many-task Optimization: A Complex Network Perspective
Yudong Yang, Kai Wu, Xiangyi Teng +3
The field of evolutionary many-task optimization (EMaTO) is increasingly recognized for its ability to streamline the resolution of optimization challenges with repetitive characte…
Automated Loss function Search for Class-imbalanced Node Classification
Xinyu Guo, Kai Wu, Xiaoyu Zhang +1
Class-imbalanced node classification tasks are prevalent in real-world scenarios. Due to the uneven distribution of nodes across different classes, learning high-quality node repre…
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…
Higher-order Knowledge Transfer for Dynamic Community Detection with Great Changes
Huixin Ma, Kai Wu, Handing Wang +1
Network structure evolves with time in the real world, and the discovery of changing communities in dynamic networks is an important research topic that poses challenging tasks. Mo…
A Multi-Transformation Evolutionary Framework for Influence Maximization in Social Networks
Chao Wang, Jiaxuan Zhao, Lingling Li +3
Influence maximization is a crucial issue for mining the deep information of social networks, which aims to select a seed set from the network to maximize the number of influenced…
Evolutionary Multitasking AUC Optimization
Chao Wang, Kai Wu, Jing Liu
Learning to optimize the area under the receiver operating characteristics curve (AUC) performance for imbalanced data has attracted much attention in recent years. Although there…
Network Collaborator: Knowledge Transfer Between Network Reconstruction and Community Detection
Kai Wu, Chao Wang, Junyuan Chen +1
This paper focuses on jointly inferring network and community structures from the dynamics of complex systems. Although many approaches have been designed to solve these two proble…
Learning to Generate Time Series Conditioned Graphs with Generative Adversarial Nets
Shanchao Yang, Jing Liu, Kai Wu +1
Deep learning based approaches have been utilized to model and generate graphs subjected to different distributions recently. However, they are typically unsupervised learning base…