111 citations · 310 across the 38 of their papers we have counts for
9 papers · 1 filter
Deep Learning-Based Frequency Offset Estimation
Tao Chen, Shilian Zheng, Jiawei Zhu +2
In wireless communication systems, the asynchronization of the oscillators in the transmitter and the receiver along with the Doppler shift due to relative movement may lead to the…
Deep Image Semantic Communication Model for Artificial Intelligent Internet of Things
Li Ping Qian, Yi Zhang, Sikai Lyu +4
With the rapid development of Artificial Intelligent Internet of Things (AIoT), the image data from AIoT devices has been witnessing the explosive increasing. In this paper, a nove…
Augmenting Radio Signals with Wavelet Transform for Deep Learning-Based Modulation Recognition
Tao Chen, Shilian Zheng, Kunfeng Qiu +3
The use of deep learning for radio modulation recognition has become prevalent in recent years. This approach automatically extracts high-dimensional features from large datasets,…
Can pre-trained models assist in dataset distillation?
Yao Lu, Xuguang Chen, Yuchen Zhang +7
Dataset Distillation (DD) is a prominent technique that encapsulates knowledge from a large-scale original dataset into a small synthetic dataset for efficient training. Meanwhile,…
Photonic time-delayed reservoir computing based on series coupled microring resonators with high memory capacity
Yijia Li, Ming Li, MingYi Gao +7
On-chip microring resonators (MRRs) have been proposed to construct the time-delayed reservoir computing (RC), which offers promising configurations available for computation with…
AIR: Threats of Adversarial Attacks on Deep Learning-Based Information Recovery
Jinyin Chen, Jie Ge, Shilian Zheng +5
A wireless communications system usually consists of a transmitter which transmits the information and a receiver which recovers the original information from the received distorte…
PathMLP: Smooth Path Towards High-order Homophily
Jiajun Zhou, Chenxuan Xie, Shengbo Gong +4
Real-world graphs exhibit increasing heterophily, where nodes no longer tend to be connected to nodes with the same label, challenging the homophily assumption of classical graph n…
Subgraph Networks Based Contrastive Learning
Jinhuan Wang, Jiafei Shao, Zeyu Wang +3
Graph contrastive learning (GCL), as a self-supervised learning method, can solve the problem of annotated data scarcity. It mines explicit features in unannotated graphs to genera…
Clarify Confused Nodes via Separated Learning
Jiajun Zhou, Shengbo Gong, Xuanze Chen +4
Graph neural networks (GNNs) have achieved remarkable advances in graph-oriented tasks. However, real-world graphs invariably contain a certain proportion of heterophilous nodes, c…