Zobrazeno 1 - 10
of 445
pro vyhledávání: '"He, Kunlun"'
Autor:
Xu, Minfeng, Fan, Chen-Chen, Zhou, Yan-Jie, Guo, Wenchao, Liu, Pan, Qi, Jing, Lu, Le, Chao, Hanqing, He, Kunlun
Cardiovascular diseases (CVD) remain a leading health concern and contribute significantly to global mortality rates. While clinical advancements have led to a decline in CVD mortality, accurately identifying individuals who could benefit from preven
Externí odkaz:
http://arxiv.org/abs/2410.18610
Autor:
Liu, Jiyuan, Liu, Xinwang, Wang, Siqi, Hu, Xingchen, Liao, Qing, Wan, Xinhang, Zhang, Yi, Lv, Xin, He, Kunlun
Vertical federated learning is a natural and elegant approach to integrate multi-view data vertically partitioned across devices (clients) while preserving their privacies. Apart from the model training, existing methods requires the collaboration of
Externí odkaz:
http://arxiv.org/abs/2409.04111
Multimodal electronic health record (EHR) data can offer a holistic assessment of a patient's health status, supporting various predictive healthcare tasks. Recently, several studies have embraced the multitask learning approach in the healthcare dom
Externí odkaz:
http://arxiv.org/abs/2406.11928
Autor:
Yang, Runzhao, Chen, Yinda, Zhang, Zhihong, Liu, Xiaoyu, Li, Zongren, He, Kunlun, Xiong, Zhiwei, Suo, Jinli, Dai, Qionghai
In the field of medical image compression, Implicit Neural Representation (INR) networks have shown remarkable versatility due to their flexible compression ratios, yet they are constrained by a one-to-one fitting approach that results in lengthy enc
Externí odkaz:
http://arxiv.org/abs/2405.16850
Time-series causal discovery (TSCD) is a fundamental problem of machine learning. However, existing synthetic datasets cannot properly evaluate or predict the algorithms' performance on real data. This study introduces the CausalTime pipeline to gene
Externí odkaz:
http://arxiv.org/abs/2310.01753
Although data-driven methods usually have noticeable performance on disease diagnosis and treatment, they are suspected of leakage of privacy due to collecting data for model training. Recently, federated learning provides a secure and trustable alte
Externí odkaz:
http://arxiv.org/abs/2306.14483
Autor:
Cheng, Yuxiao, Li, Lianglong, Xiao, Tingxiong, Li, Zongren, Zhong, Qin, Suo, Jinli, He, Kunlun
Causal discovery in time-series is a fundamental problem in the machine learning community, enabling causal reasoning and decision-making in complex scenarios. Recently, researchers successfully discover causality by combining neural networks with Gr
Externí odkaz:
http://arxiv.org/abs/2305.05890
Autor:
Liu, Meng, Liang, Ke, Zhao, Yawei, Tu, Wenxuan, Zhou, Sihang, Gan, Xinbiao, Liu, Xinwang, He, Kunlun
Temporal graph learning aims to generate high-quality representations for graph-based tasks with dynamic information, which has recently garnered increasing attention. In contrast to static graphs, temporal graphs are typically organized as node inte
Externí odkaz:
http://arxiv.org/abs/2302.07491
Autor:
Cheng, Yuxiao, Yang, Runzhao, Xiao, Tingxiong, Li, Zongren, Suo, Jinli, He, Kunlun, Dai, Qionghai
Publikováno v:
The Eleventh International Conference on Learning Representations, Feb. 2023
Causal discovery from time-series data has been a central task in machine learning. Recently, Granger causality inference is gaining momentum due to its good explainability and high compatibility with emerging deep neural networks. However, most exis
Externí odkaz:
http://arxiv.org/abs/2302.07458
Autor:
Liu, Yue, Xia, Jun, Zhou, Sihang, Yang, Xihong, Liang, Ke, Fan, Chenchen, Zhuang, Yan, Li, Stan Z., Liu, Xinwang, He, Kunlun
Graph clustering, which aims to divide nodes in the graph into several distinct clusters, is a fundamental yet challenging task. Benefiting from the powerful representation capability of deep learning, deep graph clustering methods have achieved grea
Externí odkaz:
http://arxiv.org/abs/2211.12875