Zobrazeno 1 - 10
of 404
pro vyhledávání: '"LIU Jiaxu"'
While Hyperbolic Graph Neural Network (HGNN) has recently emerged as a powerful tool dealing with hierarchical graph data, the limitations of scalability and efficiency hinder itself from generalizing to deep models. In this paper, by envisioning dep
Externí odkaz:
http://arxiv.org/abs/2406.01282
With the proliferation of red-teaming strategies for Large Language Models (LLMs), the deficiency in the literature about improving the safety and robustness of LLM defense strategies is becoming increasingly pronounced. This paper introduces the LLM
Externí odkaz:
http://arxiv.org/abs/2405.12604
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. Yet, a notable challenge with existing convolutional neural network (CNN)-b
Externí odkaz:
http://arxiv.org/abs/2404.08412
While Goal-Conditioned Reinforcement Learning (GCRL) has gained attention, its algorithmic robustness against adversarial perturbations remains unexplored. The attacks and robust representation training methods that are designed for traditional RL be
Externí odkaz:
http://arxiv.org/abs/2312.07392
Contemporary point cloud segmentation approaches largely rely on richly annotated 3D training data. However, it is both time-consuming and challenging to obtain consistently accurate annotations for such 3D scene data. Moreover, there is still a lack
Externí odkaz:
http://arxiv.org/abs/2311.06018
Autor:
Zhou, Zhanke, Yao, Jiangchao, Liu, Jiaxu, Guo, Xiawei, Yao, Quanming, He, Li, Wang, Liang, Zheng, Bo, Han, Bo
Although link prediction on graphs has achieved great success with the development of graph neural networks (GNNs), the potential robustness under the edge noise is still less investigated. To close this gap, we first conduct an empirical study to di
Externí odkaz:
http://arxiv.org/abs/2311.01196
Hyperbolic graph convolutional networks (HGCNs) have demonstrated significant potential in extracting information from hierarchical graphs. However, existing HGCNs are limited to shallow architectures due to the computational expense of hyperbolic op
Externí odkaz:
http://arxiv.org/abs/2310.02027
In traditional Graph Neural Networks (GNNs), the assumption of a fixed embedding manifold often limits their adaptability to diverse graph geometries. Recently, Hamiltonian system-inspired GNNs have been proposed to address the dynamic nature of such
Externí odkaz:
http://arxiv.org/abs/2309.04885
Publikováno v:
Advanced Energy Materials, 2023, 13, 2301331
Bilayer borophene, very recently synthesized on Ag and Cu, possesses extremely flat large surface and excellent conductivity. Besides, the van der Waals gap of bilayer borophene can be intercalated by metal atoms, thereby tailoring the properties of
Externí odkaz:
http://arxiv.org/abs/2309.02963
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
Externí odkaz:
http://arxiv.org/abs/2304.08051