Zobrazeno 1 - 10
of 177
pro vyhledávání: '"Cui Lixin"'
Publikováno v:
Journal of Orthopaedic Surgery and Research, Vol 18, Iss 1, Pp 1-9 (2023)
Abstract Background Spinal cord injuries are extremely debilitating and fatal injuries. There is currently little research focusing on traumatic spinal cord injuries, and there is little information available about the epidemiological characteristics
Externí odkaz:
https://doaj.org/article/cf4627c793324aefa3b58c0a0269a1b8
Graph learning methods have been extensively applied in diverse application areas. However, what kind of inherent graph properties e.g. graph proximity, graph structural information has been encoded into graph representation learning for downstream t
Externí odkaz:
http://arxiv.org/abs/2408.03877
Graph Auto-Encoders (GAEs) are powerful tools for graph representation learning. In this paper, we develop a novel Hierarchical Cluster-based GAE (HC-GAE), that can learn effective structural characteristics for graph data analysis. To this end, duri
Externí odkaz:
http://arxiv.org/abs/2405.14742
Graph Neural Networks (GNNs) are powerful tools for graph classification. One important operation for GNNs is the downsampling or pooling that can learn effective embeddings from the node representations. In this paper, we propose a new hierarchical
Externí odkaz:
http://arxiv.org/abs/2405.10218
Infrared and visible image fusion (IVF) plays an important role in intelligent transportation system (ITS). The early works predominantly focus on boosting the visual appeal of the fused result, and only several recent approaches have tried to combin
Externí odkaz:
http://arxiv.org/abs/2403.16227
Autor:
Xu, Zhuo, Cui, Lixin, Li, Ming, Wang, Yue, Lyu, Ziyu, Du, Hangyuan, Bai, Lu, Yu, Philip S., Hancock, Edwin R.
In this paper, we develop a novel local graph pooling method, namely the Separated Subgraph-based Hierarchical Pooling (SSHPool), for graph classification. We commence by assigning the nodes of a sample graph into different clusters, resulting in a f
Externí odkaz:
http://arxiv.org/abs/2403.16133
Autor:
Qian, Feifei, Cui, Lixin, Li, Ming, Wang, Yue, Du, Hangyuan, Xu, Lixiang, Bai, Lu, Yu, Philip S., Hancock, Edwin R.
In this paper, we propose a new model to learn Adaptive Kernel-based Representations (AKBR) for graph classification. Unlike state-of-the-art R-convolution graph kernels that are defined by merely counting any pair of isomorphic substructures between
Externí odkaz:
http://arxiv.org/abs/2403.16130
In this work, we develop an Aligned Entropic Reproducing Kernel (AERK) for graph classification. We commence by performing the Continuous-time Quantum Walk (CTQW) on each graph structure, and computing the Averaged Mixing Matrix (AMM) to describe how
Externí odkaz:
http://arxiv.org/abs/2303.03396
In this paper, we propose a novel graph kernel, namely the Quantum-based Entropic Subtree Kernel (QESK), for Graph Classification. To this end, we commence by computing the Average Mixing Matrix (AMM) of the Continuous-time Quantum Walk (CTQW) evolve
Externí odkaz:
http://arxiv.org/abs/2212.05228
In this work, we propose a family of novel quantum kernels, namely the Hierarchical Aligned Quantum Jensen-Shannon Kernels (HAQJSK), for un-attributed graphs. Different from most existing classical graph kernels, the proposed HAQJSK kernels can incor
Externí odkaz:
http://arxiv.org/abs/2211.02904