no code implementations • 16 Apr 2024 • Kafeng Wang, Jianfei Chen, He Li, Zhenpeng Mi, Jun Zhu
Diffusion models have been extensively used in data generation tasks and are recognized as one of the best generative models.
no code implementations • 26 Dec 2022 • Kafeng Wang, Pengyang Wang, Chengzhong Xu
Specifically, we construct the AFE pipeline based on reinforcement learning setting, where each feature is assigned an agent to perform feature transformation \com{and} selection, and the evaluation score of the produced features in downstream tasks serve as the reward to update the policy.
no code implementations • 2 Mar 2022 • Yi Gu, Hongzhi Cheng, Kafeng Wang, Dejing Dou, Chengzhong Xu, Hui Kong
In this paper, we propose a learning-based moving-object tracking method utilizing our newly developed LiDAR sensor, Frequency Modulated Continuous Wave (FMCW) LiDAR.
no code implementations • 24 Oct 2021 • Kafeng Wang, Haoyi Xiong, Jie Zhang, Hongyang Chen, Dejing Dou, Cheng-Zhong Xu
Extensive experiment based on real-word field deployment (on the highways in Shenzhen, China) shows that SenseMag significantly outperforms the existing methods in both classification accuracy and the granularity of vehicle types (i. e., 7 types by SenseMag versus 4 types by the existing work in comparisons).
no code implementations • 29 Sep 2021 • Dongping Liao, Xitong Gao, Yiren Zhao, Hao Dai, Li Li, Kafeng Wang, Kejiang Ye, Yang Wang, Cheng-Zhong Xu
Federated learning (FL) enables edge clients to train collaboratively while preserving individual's data privacy.
no code implementations • ICLR 2020 • Kafeng Wang, Xitong Gao, Yiren Zhao, Xingjian Li, Dejing Dou, Cheng-Zhong Xu
Deep convolutional neural networks are now widely deployed in vision applications, but a limited size of training data can restrict their task performance.