Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Shan, Caihua"'
Autor:
Dai, Xinnan, Wen, Qihao, Shen, Yifei, Wen, Hongzhi, Li, Dongsheng, Tang, Jiliang, Shan, Caihua
Large Language Models (LLMs) have achieved great success in various reasoning tasks. In this work, we focus on the graph reasoning ability of LLMs. Although theoretical studies proved that LLMs are capable of handling graph reasoning tasks, empirical
Externí odkaz:
http://arxiv.org/abs/2408.09529
Autor:
Wu, Xixi, Shen, Yifei, Shan, Caihua, Song, Kaitao, Wang, Siwei, Zhang, Bohang, Feng, Jiarui, Cheng, Hong, Chen, Wei, Xiong, Yun, Li, Dongsheng
Task planning is emerging as an important research topic alongside the development of large language models (LLMs). It aims to break down complex user requests into solvable sub-tasks, thereby fulfilling the original requests. In this context, the su
Externí odkaz:
http://arxiv.org/abs/2405.19119
Graphs are structured data that models complex relations between real-world entities. Heterophilous graphs, where linked nodes are prone to be with different labels or dissimilar features, have recently attracted significant attention and found many
Externí odkaz:
http://arxiv.org/abs/2401.09769
Precision medicine tailored to individual patients has gained significant attention in recent times. Machine learning techniques are now employed to process personalized data from various sources, including images, genetics, and assessments. These te
Externí odkaz:
http://arxiv.org/abs/2311.14304
Graph neural networks (GNNs) have recently received significant attention. Learning node-wise message propagation in GNNs aims to set personalized propagation steps for different nodes in the graph. Despite the success, existing methods ignore node p
Externí odkaz:
http://arxiv.org/abs/2311.02832
Label noise is a common challenge in large datasets, as it can significantly degrade the generalization ability of deep neural networks. Most existing studies focus on noisy labels in computer vision; however, graph models encompass both node feature
Externí odkaz:
http://arxiv.org/abs/2310.16560
LSM-trees are widely adopted as the storage backend of key-value stores. However, optimizing the system performance under dynamic workloads has not been sufficiently studied or evaluated in previous work. To fill the gap, we present RusKey, a key-val
Externí odkaz:
http://arxiv.org/abs/2308.07013
Genes are fundamental for analyzing biological systems and many recent works proposed to utilize gene expression for various biological tasks by deep learning models. Despite their promising performance, it is hard for deep neural networks to provide
Externí odkaz:
http://arxiv.org/abs/2304.04982
Generative graph self-supervised learning (SSL) aims to learn node representations by reconstructing the input graph data. However, most existing methods focus on unsupervised learning tasks only and very few work has shown its superiority over the s
Externí odkaz:
http://arxiv.org/abs/2301.12458
Autor:
Wu, Xixi, Xiong, Yun, Zhang, Yao, Jiao, Yizhu, Shan, Caihua, Sun, Yiheng, Zhu, Yangyong, Yu, Philip S.
Community detection refers to the task of discovering closely related subgraphs to understand the networks. However, traditional community detection algorithms fail to pinpoint a particular kind of community. This limits its applicability in real-wor
Externí odkaz:
http://arxiv.org/abs/2210.08274