1 code implementation • 21 Mar 2024 • Wei Chen, Yuxuan Liang, Yuanshao Zhu, Yanchuan Chang, Kang Luo, Haomin Wen, Lei LI, Yanwei Yu, Qingsong Wen, Chao Chen, Kai Zheng, Yunjun Gao, Xiaofang Zhou, Yu Zheng
In this paper, we present a comprehensive review of the development and recent advances in deep learning for trajectory computing (DL4Traj).
1 code implementation • 5 Feb 2024 • Qidong Liu, Xian Wu, Xiangyu Zhao, Yuanshao Zhu, Zijian Zhang, Feng Tian, Yefeng Zheng
In this paper, we introduce a novel approach called Large Language Model Distilling Medication Recommendation (LEADER).
no code implementations • 24 Oct 2023 • Yuanshao Zhu, Yongchao Ye, Xiangyu Zhao, James J. Q. Yu
Our approach focuses on enhancing the quality of the input data for traffic prediction models, which is a critical yet often overlooked aspect in the field.
1 code implementation • 21 Oct 2023 • Qidong Liu, Xian Wu, Xiangyu Zhao, Yuanshao Zhu, Derong Xu, Feng Tian, Yefeng Zheng
Additionally, we propose a task-motivated gate function for all MOELoRA layers that can regulate the contributions of each expert and generate distinct parameters for various tasks.
no code implementations • 13 Mar 2023 • Yunjie Huang, Xiaozhuang Song, Yuanshao Zhu, Shiyao Zhang, James J. Q. Yu
In modern traffic management, one of the most essential yet challenging tasks is accurately and timely predicting traffic.
no code implementations • 12 Mar 2023 • Weilin Lin, Xiangyu Zhao, Yejing Wang, Yuanshao Zhu, Wanyu Wang
In the searching phase, we aim to train the policy network with the capacity of instance denoising; in the validation phase, we find out and evaluate the denoised subset of data instances selected by the trained policy network, so as to validate its denoising ability.
no code implementations • 14 Oct 2021 • Yi Liu, Yuanshao Zhu, James J. Q. Yu
Similarly, due to the heterogeneity of the connected remote devices, FEEL faces the challenge of heterogeneous data and non-IID (Independent and Identically Distributed) data.