Zobrazeno 21 - 30
of 642
pro vyhledávání: '"Pan, Shirui"'
Autor:
Peng, Zhaopeng, Fan, Xiaoliang, Chen, Yufan, Wang, Zheng, Pan, Shirui, Wen, Chenglu, Zhang, Ruisheng, Wang, Cheng
Adapting Foundation Models (FMs) for downstream tasks through Federated Learning (FL) emerges a promising strategy for protecting data privacy and valuable FMs. Existing methods fine-tune FM by allocating sub-FM to clients in FL, however, leading to
Externí odkaz:
http://arxiv.org/abs/2404.11536
Temporal Knowledge Graphs (TKGs) incorporate a temporal dimension, allowing for a precise capture of the evolution of knowledge and reflecting the dynamic nature of the real world. Typically, TKGs contain complex geometric structures, with various ge
Externí odkaz:
http://arxiv.org/abs/2403.19881
Autor:
Liang, Yuxuan, Wen, Haomin, Nie, Yuqi, Jiang, Yushan, Jin, Ming, Song, Dongjin, Pan, Shirui, Wen, Qingsong
Time series analysis stands as a focal point within the data mining community, serving as a cornerstone for extracting valuable insights crucial to a myriad of real-world applications. Recent advances in Foundation Models (FMs) have fundamentally res
Externí odkaz:
http://arxiv.org/abs/2403.14735
Evaluating the performance of a well-trained GNN model on real-world graphs is a pivotal step for reliable GNN online deployment and serving. Due to a lack of test node labels and unknown potential training-test graph data distribution shifts, conven
Externí odkaz:
http://arxiv.org/abs/2403.09953
Model extraction attacks (MEAs) enable an attacker to replicate the functionality of a victim deep neural network (DNN) model by only querying its API service remotely, posing a severe threat to the security and integrity of pay-per-query DNN-based s
Externí odkaz:
http://arxiv.org/abs/2403.07673
Autor:
Liu, Xin, Zhang, Yuxiang, Wu, Meng, Yan, Mingyu, He, Kun, Yan, Wei, Pan, Shirui, Ye, Xiaochun, Fan, Dongrui
Edge perturbation is a basic method to modify graph structures. It can be categorized into two veins based on their effects on the performance of graph neural networks (GNNs), i.e., graph data augmentation and attack. Surprisingly, both veins of edge
Externí odkaz:
http://arxiv.org/abs/2403.07943
Open-set graph learning is a practical task that aims to classify the known class nodes and to identify unknown class samples as unknowns. Conventional node classification methods usually perform unsatisfactorily in open-set scenarios due to the comp
Externí odkaz:
http://arxiv.org/abs/2402.18495
Graph learning plays a pivotal role and has gained significant attention in various application scenarios, from social network analysis to recommendation systems, for its effectiveness in modeling complex data relations represented by graph structura
Externí odkaz:
http://arxiv.org/abs/2402.16374
In long-term time series forecasting (LTSF) tasks, an increasing number of models have acknowledged that discrete time series originate from continuous dynamic systems and have attempted to model their dynamical structures. Recognizing the chaotic na
Externí odkaz:
http://arxiv.org/abs/2402.11463
Autor:
Jin, Ming, Zhang, Yifan, Chen, Wei, Zhang, Kexin, Liang, Yuxuan, Yang, Bin, Wang, Jindong, Pan, Shirui, Wen, Qingsong
Time series analysis is essential for comprehending the complexities inherent in various realworld systems and applications. Although large language models (LLMs) have recently made significant strides, the development of artificial general intellige
Externí odkaz:
http://arxiv.org/abs/2402.02713