no code implementations • 25 Mar 2025 • YuHan Wang, Silu He, Qinyao Luo, Hongyuan Yuan, Ling Zhao, Jiawei Zhu, Haifeng Li
We propose a feature-structure mixed invariant representation learning (FSM-IRL) model that accounts for both feature distribution shifts and structural distribution shifts.
no code implementations • 15 Jan 2025 • Xiaoyu Bao, Kailin Xu, Jiawei Zhu, Haiyun Huang, Kangning Li, Qiyun Huang, Yuanqing Li
In this study, we propose a mindfulness brain-computer interface (BCI), specifically designed to redirect attention with the aim of mitigating seasickness symptoms in real-world settings.
1 code implementation • CVPR 2025 • Linke Ouyang, Yuan Qu, Hongbin Zhou, Jiawei Zhu, Rui Zhang, Qunshu Lin, Bin Wang, Zhiyuan Zhao, Man Jiang, Xiaomeng Zhao, Jin Shi, Fan Wu, Pei Chu, Minghao Liu, Zhenxiang Li, Chao Xu, Bo Zhang, Botian Shi, Zhongying Tu, Conghui He
Document content extraction is a critical task in computer vision, underpinning the data needs of large language models (LLMs) and retrieval-augmented generation (RAG) systems.
1 code implementation • 23 Nov 2024 • Yunlei Liang, Jiawei Zhu, Wen Ye, Song Gao
The region2vec methods generate node neural embeddings based on attribute similarity, geographic adjacency and spatial interactions, and then extract network communities based on node embeddings using agglomerative clustering.
no code implementations • 18 Sep 2024 • Peng Liu, Jiawei Zhu, Cong Xu, Ming Zhao, Bin Wang
However, limited by their modeling pattern, all the current RL-MTF methods can only utilize user features as the state to generate actions for each user, but unable to make use of item features and other valuable features, which leads to suboptimal results.
no code implementations • 15 Aug 2024 • Yibo Jin, Tao Wang, Huimin Lin, Mingyang Song, Peiyang Li, Yipeng Ma, Yicheng Shan, Zhengfan Yuan, Cailong Li, Yajing Sun, Tiandeng Wu, Xing Chu, Ruizhi Huan, Li Ma, Xiao You, Wenting Zhou, Yunpeng Ye, Wen Liu, Xiangkun Xu, Yongsheng Zhang, Tiantian Dong, Jiawei Zhu, Zhe Wang, Xijian Ju, Jianxun Song, Haoliang Cheng, Xiaojing Li, Jiandong Ding, Hefei Guo, Zhengyong Zhang
To overcome previous problems, this paper proposes an end-to-end system P/D-Serve, complying with the paradigm of MLOps (machine learning operations), which models end-to-end (E2E) P/D performance and enables: 1) fine-grained P/D organization, mapping the service with RoCE (RDMA over converged ethernet) as needed, to facilitate similar processing and dynamic adjustments on P/D ratios; 2) on-demand forwarding upon rejections for idle prefill, decoupling the scheduler from regular inaccurate reports and local queues, to avoid timeouts in prefill; and 3) efficient KVCache transfer via optimized D2D access.
no code implementations • 19 Apr 2024 • Peng Liu, Cong Xu, Ming Zhao, Jiawei Zhu, Bin Wang, Yi Ren
IntegratedRL-MTF integrates offline RL model with our online exploration policy to relax overstrict and complicated constraints, which significantly improves its performance.
no code implementations • 8 Nov 2023 • Tao Chen, Shilian Zheng, Jiawei Zhu, Qi Xuan, Xiaoniu Yang
In wireless communication systems, the asynchronization of the oscillators in the transmitter and the receiver along with the Doppler shift due to relative movement may lead to the presence of carrier frequency offset (CFO) in the received signals.
no code implementations • 8 Dec 2022 • Jiawei Zhu, Mei Hong, Ronghua Du, Haifeng Li
Then we compute the structural equivalence of node pairs based on their topological features.
1 code implementation • 15 Oct 2022 • Haifeng Li, Jun Cao, Jiawei Zhu, Qinyao Luo, Silu He, Xuyin Wang
iGCL designs the invariant-discriminative loss (ID loss) to learn invariant and discriminative representations.
2 code implementations • 10 Oct 2022 • Yunlei Liang, Jiawei Zhu, Wen Ye, Song Gao
Community Detection algorithms are used to detect densely connected components in complex networks and reveal underlying relationships among components.
no code implementations • 30 Jun 2021 • Haifeng Li, Jun Cao, Jiawei Zhu, Yu Liu, Qing Zhu, Guohua Wu
And we propose Curvature Graph Neural Network (CGNN), which effectively improves the adaptive locality ability of GNNs by leveraging the structural property of graph curvature.
no code implementations • 2 Mar 2021 • Haifeng Li, Jun Cao, Jiawei Zhu, Qing Zhu, Guohua Wu
A class of GNNs solves this problem by learning implicit weights to represent the importance of neighbor nodes, which we call implicit GNNs such as Graph Attention Network.
1 code implementation • 26 Nov 2020 • Jiawei Zhu, Xin Han, Hanhan Deng, Chao Tao, Ling Zhao, Pu Wang, Lin Tao, Haifeng Li
On this background, this study presents a knowledge representation-driven traffic forecasting method based on spatial-temporal graph convolutional networks.
no code implementations • 22 Nov 2020 • Jiawei Zhu, Chao Tao, Hanhan Deng, Ling Zhao, Pu Wang, Tao Lin, Haifeng Li
Traffic forecasting is a fundamental and challenging task in the field of intelligent transportation.
no code implementations • 28 Sep 2020 • Haifeng Li, Zhenqi Cui, Zhiqing Zhu, Li Chen, Jiawei Zhu, Haozhe Huang, Chao Tao
On the one hand, RS-MetaNet raises the level of learning from the sample to the task by organizing training in a meta way, and it learns to learn a metric space that can well classify remote sensing scenes from a series of tasks.
2 code implementations • 20 Jun 2020 • Jiawei Zhu, Yujiao Song, Ling Zhao, Haifeng Li
In this study, an attention temporal graph convolutional network (A3T-GCN) traffic forecasting method was proposed to simultaneously capture global temporal dynamics and spatial correlations.
2 code implementations • 7 Mar 2020 • Jie Chen, Ziyang Yuan, Jian Peng, Li Chen, Haozhe Huang, Jiawei Zhu, Yu Liu, Haifeng Li
However, the available methods focus mainly on the difference information between multitemporal remote sensing images and lack robustness to pseudo-change information.
no code implementations • 27 Jan 2020 • Jie Chen, Haozhe Huang, Jian Peng, Jiawei Zhu, Li Chen, Wenbo Li, Binyu Sun, Haifeng Li
The feature-learning procedure of CNN largely depends on the architecture of CNN.
2 code implementations • 22 Oct 2018 • Enqiang Guo, Xinsha Fu, Jiawei Zhu, Min Deng, Yu Liu, Qing Zhu, Haifeng Li
A critical challenge problem of scene change detection is that noisy changes generated by varying illumination, shadows and camera viewpoint make variances of a scene difficult to define and measure since the noisy changes and semantic ones are entangled.