no code implementations • 12 Apr 2024 • Siming Shan, Pengkai Wang, Song Chen, Jiaxu Liu, Chao Xu, Shengze Cai
The use of machine learning in fluid dynamics is becoming more common to expedite the computation when solving forward and inverse problems of partial differential equations.
1 code implementation • 12 Dec 2023 • Xiangyu Yin, Sihao Wu, Jiaxu Liu, Meng Fang, Xingyu Zhao, Xiaowei Huang, Wenjie Ruan
Then, to mitigate the vulnerability of existing GCRL algorithms, we introduce Adversarial Representation Tactics, which combines Semi-Contrastive Adversarial Augmentation with Sensitivity-Aware Regularizer to improve the adversarial robustness of the underlying RL agent against various types of perturbations.
no code implementations • 10 Nov 2023 • Jiaxu Liu, Zhengdi Yu, Toby P. Breckon, Hubert P. H. Shum
To achieve this, U3DS$^3$ leverages a generalized unsupervised segmentation method for both object and background across both indoor and outdoor static 3D point clouds with no requirement for model pre-training, by leveraging only the inherent information of the point cloud to achieve full 3D scene segmentation.
1 code implementation • NeurIPS 2023 • Zhanke Zhou, Jiangchao Yao, Jiaxu Liu, Xiawei Guo, Quanming Yao, Li He, Liang Wang, Bo Zheng, Bo Han
To address this dilemma, we propose an information-theory-guided principle, Robust Graph Information Bottleneck (RGIB), to extract reliable supervision signals and avoid representation collapse.
no code implementations • 3 Oct 2023 • Jiaxu Liu, Xinping Yi, Xiaowei Huang
Hyperbolic graph convolutional networks (HGCN) have demonstrated significant potential in extracting information from hierarchical graphs.
no code implementations • 9 Sep 2023 • Jiaxu Liu, Xinping Yi, Tianle Zhang, Xiaowei Huang
In traditional Graph Neural Networks (GNNs), the assumption of a fixed embedding manifold often limits their adaptability to diverse graph geometries.
no code implementations • 17 Apr 2023 • Jiaxu Liu, Song Chen, Shengze Cai, Chao Xu
In this paper, we investigate a distributed aggregative optimization problem in a network, where each agent has its own local cost function which depends not only on the local state variable but also on an aggregated function of state variables from all agents.
no code implementations • 8 Mar 2023 • Jiaxu Liu, Song Chen, Shengze Cai, Chao Xu
The vanilla fractional order gradient descent may oscillatively converge to a region around the global minimum instead of converging to the exact minimum point, or even diverge, in the case where the objective function is strongly convex.