1 code implementation • 15 Apr 2024 • Genjia Liu, Yue Hu, Chenxin Xu, Weibo Mao, Junhao Ge, Zhengxiang Huang, Yifan Lu, Yinda Xu, Junkai Xia, Yafei Wang, Siheng Chen
This effort necessitates two key foundations: a platform capable of generating data to facilitate the training and testing of V2X-AD, and a comprehensive system that integrates full driving-related functionalities with mechanisms for information sharing.
no code implementations • 7 Feb 2024 • Yafei Wang, Xinping Yi, Hongwei Hou, Wenjin Wang, Shi Jin
With the signal model in the presence of channel aging, we formulate the signal-to-noise-plus-interference ratio (SINR) balancing and minimum mean square error (MMSE) problems for robust SLP design.
no code implementations • 23 Jan 2024 • Shaoheng Fang, Rui Ye, Wenhao Wang, Zuhong Liu, Yuxiao Wang, Yafei Wang, Siheng Chen, Yanfeng Wang
In this paper, we introduce FedRSU, an innovative federated learning framework for self-supervised scene flow estimation.
no code implementations • 16 Oct 2023 • Yafei Wang, Hongwei Hou, Wenjin Wang, Xinping Yi, Shi Jin
It is observed that the received SLP signals do not always follow Gaussian distribution, rendering the conventional soft demodulation with the Gaussian assumption unsuitable for the coded SLP systems.
no code implementations • 11 Oct 2023 • Yafei Wang, Hongwei Hou, Wenjin Wang, Xinping Yi
This paper investigates symbol-level precoding (SLP) for high-order quadrature amplitude modulation (QAM) aimed at minimizing the average symbol error rate (SER), leveraging both constructive interference (CI) and noise power to gain superiority in full signal-to-noise ratio (SNR) ranges.
1 code implementation • 11 Aug 2023 • Yafei Wang, Jianguo Liu
In the optimal phase, an optimization problem based on first-order Hilbert-Schmidt independence criterion (HSIC) gives an estimated skeleton as the initial determined parents subset.
no code implementations • 24 Apr 2023 • Shunli Ren, Zixing Lei, Zi Wang, Mehrdad Dianati, Yafei Wang, Siheng Chen, Wenjun Zhang
To achieve comprehensive recovery, we design a communication-adaptive multi-scale spatial-temporal prediction model to extract multi-scale spatial-temporal features based on V2X communication conditions and capture the most significant information for the prediction of the missing information.
no code implementations • NeurIPS 2021 • Ke Sun, Yafei Wang, Yi Liu, Yingnan Zhao, Bo Pan, Shangling Jui, Bei Jiang, Linglong Kong
Anderson mixing has been heuristically applied to reinforcement learning (RL) algorithms for accelerating convergence and improving the sampling efficiency of deep RL.
no code implementations • 7 Oct 2021 • Ke Sun, Yingnan Zhao, Enze Shi, Yafei Wang, Xiaodong Yan, Bei Jiang, Linglong Kong
The theoretical advantages of distributional reinforcement learning~(RL) over classical RL remain elusive despite its remarkable empirical performance.
no code implementations • 29 Sep 2021 • Ke Sun, Yingnan Zhao, Yi Liu, Enze Shi, Yafei Wang, Aref Sadeghi, Xiaodong Yan, Bei Jiang, Linglong Kong
Distributional reinforcement learning~(RL) is a class of state-of-the-art algorithms that estimate the whole distribution of the total return rather than only its expectation.
no code implementations • 16 Jun 2020 • Matthew Walker, Bo Yan, Yiou Xiao, Yafei Wang, Ayan Acharya
Many tasks that rely on representations of nodes in graphs would benefit if those representations were faithful to distances between nodes in the graph.
no code implementations • 7 Mar 2020 • Huimin Zhang, Yafei Wang, Junjia Liu, Chengwei Li, Taiyuan Ma, Chengliang Yin
Secondly, the LSTM encoder is used to encode the historical sequences composed of the vehicle descriptor and a novel dilated convolutional social pooling is proposed to improve modeling vehicles' spatial interactions.
no code implementations • 12 Jul 2019 • Xueyan Ding, Yafei Wang, Yang Yan, Zheng Liang, Zetian Mi, Xianping Fu
Different from most of previous underwater image enhancement methods that compute light attenuation along object-camera path through hazy image formation model, we propose a novel jointly wavelength compensation and dehazing network (JWCDN) that takes into account the wavelength attenuation along surface-object path and the scattering along object-camera path simultaneously.