no code implementations • 28 Feb 2024 • Xun Huang, Hai Wu, Xin Li, Xiaoliang Fan, Chenglu Wen, Cheng Wang
LiDAR-based 3D object detection models have traditionally struggled under rainy conditions due to the degraded and noisy scanning signals.
no code implementations • 7 Dec 2023 • Fengze Sun, Jianzhong Qi, Yanchuan Chang, Xiaoliang Fan, Shanika Karunasekera, Egemen Tanin
Our model is powered by a dual-feature attentive fusion module named DAFusion, which fuses embeddings from different region features to learn higher-order correlations between the regions as well as between the different types of region features.
no code implementations • 21 Jun 2023 • Zheng Wang, Xiaoliang Fan, Zhaopeng Peng, Xueheng Li, Ziqi Yang, Mingkuan Feng, Zhicheng Yang, Xiao Liu, Cheng Wang
Federated learning (FL) has found numerous applications in healthcare, finance, and IoT scenarios.
1 code implementation • 6 Feb 2023 • Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, Jianzhong Qi, Chaochao Chen, Longbiao Chen
It aims to infer knowledge for (the things at) unobserved locations using the data from (the things at) observed locations during a given time period of interest.
1 code implementation • 25 Nov 2022 • Zheng Wang, Xiaoliang Fan, Jianzhong Qi, Haibing Jin, Peizhen Yang, Siqi Shen, Cheng Wang
Second, constrained by the far-distance in data distribution of the sampled clients, we further minimize the variance of the numbers of times that the clients are sampled, to mitigate long-term bias.
1 code implementation • 24 Jan 2022 • Shangbin Wu, Xu Yan, Xiaoliang Fan, Shirui Pan, Shichao Zhu, Chuanpan Zheng, Ming Cheng, Cheng Wang
Human mobility data contains rich but abundant information, which yields to the comprehensive region embeddings for cross domain tasks.
no code implementations • 25 Nov 2021 • Chuanpan Zheng, Xiaoliang Fan, Shirui Pan, Haibing Jin, Zhaopeng Peng, Zonghan Wu, Cheng Wang, Philip S. Yu
However, this approach failed to explicitly reflect the correlations between different nodes at different time steps, thus limiting the learning capability of graph neural networks.
no code implementations • 27 Sep 2021 • Xu Yan, Xiaoliang Fan, Peizhen Yang, Zonghan Wu, Shirui Pan, Longbiao Chen, Yu Zang, Cheng Wang
Representation learning on temporal interaction graphs (TIG) is to model complex networks with the dynamic evolution of interactions arising in a broad spectrum of problems.
1 code implementation • 30 Apr 2021 • Zheng Wang, Xiaoliang Fan, Jianzhong Qi, Chenglu Wen, Cheng Wang, Rongshan Yu
Fairness has emerged as a critical problem in federated learning (FL).
6 code implementations • 11 Nov 2019 • Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, Jianzhong Qi
Between the encoder and the decoder, a transform attention layer is applied to convert the encoded traffic features to generate the sequence representations of future time steps as the input of the decoder.
Ranked #2 on Image Dehazing on KITTI