Zobrazeno 1 - 10
of 26 366
pro vyhledávání: '"LIU, Lin"'
Autor:
Furqon, Muhammad Tanzil, Pratama, Mahardhika, Shiddiqi, Ary Mazharuddin, Liu, Lin, Habibullah, Habibullah, Dogancay, Kutluyil
The issue of source-free time-series domain adaptations still gains scarce research attentions. On the other hand, existing approaches rely solely on time-domain features ignoring frequency components providing complementary information. This paper p
Externí odkaz:
http://arxiv.org/abs/2410.17511
Intervention intuition is often used in model explanation where the intervention effect of a feature on the outcome is quantified by the difference of a model prediction when the feature value is changed from the current value to the baseline value.
Externí odkaz:
http://arxiv.org/abs/2410.15648
Recommender systems are extensively utilised across various areas to predict user preferences for personalised experiences and enhanced user engagement and satisfaction. Traditional recommender systems, however, are complicated by confounding bias, p
Externí odkaz:
http://arxiv.org/abs/2410.12451
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
Federated learning (FL) is a collaborative technique for training large-scale models while protecting user data privacy. Despite its substantial benefits, the free-riding behavior raises a major challenge for the formation of FL, especially in compet
Externí odkaz:
http://arxiv.org/abs/2410.12723
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
Closed-source large language models deliver strong performance but have limited downstream customizability. Semi-open models, combining both closed-source and public layers, were introduced to improve customizability. However, parameters in the close
Externí odkaz:
http://arxiv.org/abs/2410.11182
Autor:
Li, Ziyu, Gu, Tianyi, Wei, Wenqi, Yuan, Yang, Wang, Zhuo, Luo, Kangjian, Pan, Yupeng, Xie, Jianfeng, Zhang, Shaozhe, Peng, Tao, Liu, Lin, Chen, Qi, Han, Xiaotao, Luo, Yongkang, Li, Liang
Conductor materials with good mechanical performance as well as high electrical- and thermal-conductivities are particularly important to break through the current bottle-neck limit ($\sim 100$ T) of pulsed magnets. Here we perform systematic studies
Externí odkaz:
http://arxiv.org/abs/2410.09376
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:
Gao, Wentao, Xu, Ziqi, Li, Jiuyong, Liu, Lin, Liu, Jixue, Le, Thuc Duy, Cheng, Debo, Zhao, Yanchang, Chen, Yun
As the growing demand for long sequence time-series forecasting in real-world applications, such as electricity consumption planning, the significance of time series forecasting becomes increasingly crucial across various domains. This is highlighted
Externí odkaz:
http://arxiv.org/abs/2409.19871