Zobrazeno 1 - 10
of 26 180
pro vyhledávání: '"Liu, Lin"'
Autor:
Deho, Oscar Blessed, Bewong, Michael, Kwashie, Selasi, Li, Jiuyong, Liu, Jixue, Liu, Lin, Joksimovic, Srecko
Over the last few decades, machine learning (ML) applications have grown exponentially, yielding several benefits to society. However, these benefits are tempered with concerns of discriminatory behaviours exhibited by ML models. In this regard, fair
Externí odkaz:
http://arxiv.org/abs/2409.12428
Super-resolution (SR) aims to enhance the quality of low-resolution images and has been widely applied in medical imaging. We found that the design principles of most existing methods are influenced by SR tasks based on real-world images and do not t
Externí odkaz:
http://arxiv.org/abs/2409.07092
Data scarcity poses a serious threat to modern machine learning and artificial intelligence, as their practical success typically relies on the availability of big datasets. One effective strategy to mitigate the issue of insufficient data is to firs
Externí odkaz:
http://arxiv.org/abs/2409.02708
Autor:
Chen, Yihao, Wu, Haochen, Jiang, Nan, Xia, Xiang, Gu, Qing, Hao, Yunqi, Cai, Pengfei, Guan, Yu, Wang, Jialong, Xie, Weilin, Fang, Lei, Fang, Sian, Song, Yan, Guo, Wu, Liu, Lin, Xu, Minqiang
This paper describes the USTC-KXDIGIT system submitted to the ASVspoof5 Challenge for Track 1 (speech deepfake detection) and Track 2 (spoofing-robust automatic speaker verification, SASV). Track 1 showcases a diverse range of technical qualities fro
Externí odkaz:
http://arxiv.org/abs/2409.01695
Estimating causal effects is crucial for decision-makers in many applications, but it is particularly challenging with observational network data due to peer interactions. Many algorithms have been proposed to estimate causal effects involving networ
Externí odkaz:
http://arxiv.org/abs/2408.11492
Autor:
Gao, Wentao, Li, Jiuyong, Cheng, Debo, Liu, Lin, Liu, Jixue, Le, Thuc Duy, Du, Xiaojing, Chen, Xiongren, Zhao, Yanchang, Chen, Yun
Global Climate Models (GCMs) are crucial for predicting future climate changes by simulating the Earth systems. However, GCM outputs exhibit systematic biases due to model uncertainties, parameterization simplifications, and inadequate representation
Externí odkaz:
http://arxiv.org/abs/2408.12063
Autor:
Qu, Yun, Wang, Boyuan, Shao, Jianzhun, Jiang, Yuhang, Chen, Chen, Ye, Zhenbin, Liu, Lin, Yang, Junfeng, Lai, Lin, Qin, Hongyang, Deng, Minwen, Zhuo, Juchao, Ye, Deheng, Fu, Qiang, Yang, Wei, Yang, Guang, Huang, Lanxiao, Ji, Xiangyang
The advancement of Offline Reinforcement Learning (RL) and Offline Multi-Agent Reinforcement Learning (MARL) critically depends on the availability of high-quality, pre-collected offline datasets that represent real-world complexities and practical a
Externí odkaz:
http://arxiv.org/abs/2408.10556
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
In recommender systems, popularity and conformity biases undermine recommender effectiveness by disproportionately favouring popular items, leading to their over-representation in recommendation lists and causing an unbalanced distribution of user-it
Externí odkaz:
http://arxiv.org/abs/2408.09646
Estimating causal effects from observational data is challenging, especially in the presence of latent confounders. Much work has been done on addressing this challenge, but most of the existing research ignores the bias introduced by the post-treatm
Externí odkaz:
http://arxiv.org/abs/2408.07219