32 citations · 80 across the 14 of their papers we have counts for
14 papers
UltraVSR: Achieving Ultra-Realistic Video Super-Resolution with Efficient One-Step Diffusion Space
Yong Liu, Jinshan Pan, Yinchuan Li +4
Diffusion models have shown great potential in generating realistic image detail. However, adapting these models to video super-resolution (VSR) remains challenging due to their in…
Tensor Generalized Approximate Message Passing
Yinchuan Li, Guangchen Lan, Xiaodong Wang
We propose a tensor generalized approximate message passing (TeG-AMP) algorithm for low-rank tensor inference, which can be used to solve tensor completion and decomposition proble…
Multi-Agent Continuous Control with Generative Flow Networks
Shuang Luo, Yinchuan Li, Shunyu Liu +3
Generative Flow Networks (GFlowNets) aim to generate diverse trajectories from a distribution in which the final states of the trajectories are proportional to the reward, serving…
MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes
Xin-Chun Li, Shaoming Song, Yinchuan Li +4
In some real-world applications, data samples are usually distributed on local devices, where federated learning (FL) techniques are proposed to coordinate decentralized clients wi…
Device Activity Detection and Channel Estimation for Millimeter-Wave Massive MIMO
Yinchuan Li, Yuancheng Zhan, Le Zheng +1
Millimeter-Wave Massive MIMO is important for beyond 5G or 6G wireless communication networks. The goal of this paper is to establish successful communication between the cellular…
Generalized Universal Domain Adaptation with Generative Flow Networks
Didi Zhu, Yinchuan Li, Yunfeng Shao +5
We introduce a new problem in unsupervised domain adaptation, termed as Generalized Universal Domain Adaptation (GUDA), which aims to achieve precise prediction of all target label…
Sparse Federated Learning with Hierarchical Personalized Models
Xiaofeng Liu, Qing Wang, Yunfeng Shao +1
Federated learning (FL) can achieve privacy-safe and reliable collaborative training without collecting users' private data. Its excellent privacy security potential promotes a wid…
Sparse Personalized Federated Learning
Xiaofeng Liu, Yinchuan Li, Qing Wang +3
Federated Learning (FL) is a collaborative machine learning technique to train a global model without obtaining clients' private data. The main challenges in FL are statistical div…
Unfolded Deep Neural Network (UDNN) for High Mobility Channel Estimation
Yinchuan Li, Xiaodong Wang, Robert L. Olesen
High mobility channel estimation is crucial for beyond 5G (B5G) or 6G wireless communication networks. This paper is concerned with channel estimation of high mobility OFDM communi…
ADMM-Net for Communication Interference Removal in Stepped-Frequency Radar
Jeremy Johnston, Yinchuan Li, Marco Lops +1
Complex ADMM-Net, a complex-valued neural network architecture inspired by the alternating direction method of multipliers (ADMM), is designed for interference removal in super-res…
Multi-dimensional Spectral Super-Resolution with Prior Knowledge via Frequency-Selective Vandermonde Decomposition and ADMM
Yinchuan Li, Xiaodong Wang, Zegang Ding
This paper is concerned with estimation of multiple frequencies from incomplete and/or noisy samples based on a low-CP-rank tensor data model where each CP vector is an array respo…
Compressive Multidimensional Harmonic Retrieval with Prior Knowledge
Yinchuan Li, Xu Zhang, Zegang Ding +1
This paper concerns the problem of estimating multidimensional (MD) frequencies using prior knowledge of the signal spectral sparsity from partial time samples. In many application…
Interference Removal for Radar/Communication Co-existence: the Random Scattering Case
Yinchuan Li, Le Zheng, Marco Lops +1
In this paper we consider an un-cooperative spectrum sharing scenario, wherein a radar system is to be overlaid to a pre-existing wireless communication system. Given the order of…
Multi-target Position and Velocity Estimation Using OFDM Communication Signals
Yinchuan Li, Xiaodong Wang, Zegang Ding
In this paper, we consider a passive radar system that estimates the positions and velocities of multiple moving targets by using OFDM signals transmitted by a totally un-coordinat…