1 code implementation • 30 Oct 2024 • Hanyang Chen, Yang Jiang, Shengnan Guo, Xiaowei Mao, Youfang Lin, Huaiyu Wan
The application of reinforcement learning in traffic signal control (TSC) has been extensively researched and yielded notable achievements.
no code implementations • 14 Dec 2023 • Shaocong Wang, Yizhao Gao, Yi Li, Woyu Zhang, Yifei Yu, Bo wang, Ning Lin, Hegan Chen, Yue Zhang, Yang Jiang, Dingchen Wang, Jia Chen, Peng Dai, Hao Jiang, Peng Lin, Xumeng Zhang, Xiaojuan Qi, Xiaoxin Xu, Hayden So, Zhongrui Wang, Dashan Shang, Qi Liu, Kwang-Ting Cheng, Ming Liu
Our random resistive memory-based deep extreme point learning machine may pave the way for energy-efficient and training-friendly edge AI across various data modalities and tasks.
no code implementations • 18 Oct 2023 • Manna Dai, Yang Jiang, Feng Yang, Joyjit Chattoraj, Yingzhi Xia, Xinxing Xu, Weijiang Zhao, My Ha Dao, Yong liu
Metasurfaces have widespread applications in fifth-generation (5G) microwave communication.
no code implementations • 6 Jun 2023 • Mingyang Sun, Dingkang Yang, Dongliang Kou, Yang Jiang, Weihua Shan, Zhe Yan, Lihua Zhang
This paper comprehensively reviews the application of implicit neural representation in human body modeling.
no code implementations • CVPR 2023 • Xiaotian Yu, Yang Jiang, Tianqi Shi, Zunlei Feng, Yuexuan Wang, Mingli Song, Li Sun
To address this problem, the proposed GSS alleviates the damage by switching the current gradient direction of each sample to a new direction selected from a gradient direction pool, which contains all-class gradient directions with different probabilities.
no code implementations • CVPR 2023 • Shengxuming Zhang, Tianqi Shi, Yang Jiang, Xiuming Zhang, Jie Lei, Zunlei Feng, Mingli Song
The loopback between two branches enables the category label to supervise the cell locating branch to learn the locating ability for cancerous areas.
no code implementations • 31 Dec 2022 • Yiran Luo, Li-Ta Hsu, Yang Jiang, Baoyu Liu, Zhetao Zhang, Yan Xiang, Naser El-Sheimy
First, an absolute code phase is predicted from base station information, and integrated solution of the INS DR and real-time kinematic (RTK) results through an extended Kalman filter (EKF).
no code implementations • 10 Oct 2022 • Sean Grullon, Vaughn Spurrier, Jiayi Zhao, Corey Chivers, Yang Jiang, Kiran Motaparthi, Michael Bonham, Julianna Ianni
We also investigated the performance of using the predicted concordance rate as a malignancy classifier, and achieved a precision and recall of 0. 85 +/- 0. 05 and 0. 61 +/- 0. 06, respectively, on the test set.
no code implementations • 11 Apr 2022 • Yang Jiang, Zhe Xue, Ang Li
Since the era of big data, the Internet has been flooded with all kinds of information.
no code implementations • 21 Mar 2022 • Yang Jiang, Zhe Xue, Ang Li
In the era of big data, it is possible to carry out cooperative research on the research results of researchers through papers, patents and other data, so as to study the role of researchers, and produce results in the analysis of results.
no code implementations • 16 Mar 2022 • Ang Li, Junping Du, Feifei Kou, Zhe Xue, Xin Xu, Mingying Xu, Yang Jiang
In light of this, we propose a scientific and technological information oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval method (SMCR) to find an effective common subspace.
no code implementations • 20 Jan 2022 • Junyan Yang, Yang Jiang, Shuoyao Wang
As video streaming quality is mainly affected by video compression, we demonstrate that the video enhancement algorithm outperforms the super-resolution algorithm in terms of signal-to-noise ratio and frames per second, suggesting a better solution for client processing in video streaming.
no code implementations • 7 May 2019 • Yang Jiang, Cong Zhao, Zeyang Dou, Lei Pang
Based on this correlation, we further demonstrate that, though the reward of NAS is sparse, the policy gradient method implicitly assign the reward to all operations and skip connections based on the sampling frequency.
no code implementations • 31 Jul 2017 • Yang Jiang, Zeyang Dou, Qun Hao, Jie Cao, Kun Gao, Xi Chen
In this paper, we propose the nonlinearity generation method to speed up and stabilize the training of deep convolutional neural networks.