Zobrazeno 1 - 10
of 2 620
pro vyhledávání: '"Zhang Shichao"'
Graph Neural Networks (GNNs) have demonstrated strong performance in graph representation learning across various real-world applications. However, they often produce biased predictions caused by sensitive attributes, such as religion or gender, an i
Externí odkaz:
http://arxiv.org/abs/2412.00382
Querying causal effects from time-series data is important across various fields, including healthcare, economics, climate science, and epidemiology. However, this task becomes complex in the existence of time-varying latent confounders, which affect
Externí odkaz:
http://arxiv.org/abs/2411.17774
Autor:
Zhang, Chengyuan, Zhang, Yilin, Zhu, Lei, Liu, Deyin, Wu, Lin, Li, Bo, Zhang, Shichao, Bennamoun, Mohammed, Boussaid, Farid
This paper introduces a novel framework for unified incremental few-shot object detection (iFSOD) and instance segmentation (iFSIS) using the Transformer architecture. Our goal is to create an optimal solution for situations where only a few examples
Externí odkaz:
http://arxiv.org/abs/2411.08569
In recommender systems, various latent confounding factors (e.g., user social environment and item public attractiveness) can affect user behavior, item exposure, and feedback in distinct ways. These factors may directly or indirectly impact user fee
Externí odkaz:
http://arxiv.org/abs/2410.12366
With the widespread use of Graph Neural Networks (GNNs) for representation learning from network data, the fairness of GNN models has raised great attention lately. Fair GNNs aim to ensure that node representations can be accurately classified, but n
Externí odkaz:
http://arxiv.org/abs/2410.11493
Quantum federated learning has brought about the improvement of privacy image classification, while the lack of personality of the client model may contribute to the suboptimal of quantum federated learning. A personalized quantum federated learning
Externí odkaz:
http://arxiv.org/abs/2410.02547
The rapid development of Large Language Models (LLMs) creates new opportunities for recommender systems, especially by exploiting the side information (e.g., descriptions and analyses of items) generated by these models. However, aligning this side i
Externí odkaz:
http://arxiv.org/abs/2409.20052
Autor:
Bai, Yuqing, Xiang, Xinji, Pan, Shuang, Zhang, Shichao, Chen, Haifeng Chen Xi, Han, Zhida, Xu, Guizhou, Xu, Feng
As a promising candidate for altermagnet, CrSb possesses a distinctive compensated spin split band structure that could bring groundbreaking concepts to the field of spintronics. In this work, we have grown high-quality CrSb single crystals and compr
Externí odkaz:
http://arxiv.org/abs/2409.14855
Graph unlearning technology has become increasingly important since the advent of the `right to be forgotten' and the growing concerns about the privacy and security of artificial intelligence. Graph unlearning aims to quickly eliminate the effects o
Externí odkaz:
http://arxiv.org/abs/2408.09705
In recommender systems, latent variables can cause user-item interaction data to deviate from true user preferences. This biased data is then used to train recommendation models, further amplifying the bias and ultimately compromising both recommenda
Externí odkaz:
http://arxiv.org/abs/2408.09651