14 citations · 32 across the 7 of their papers we have counts for
7 papers
Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data
Hongwei Zhang, Shuo Shao, Meixia Tao +2
Existing deep learning-enabled semantic communication systems often rely on shared background knowledge between the transmitter and receiver that includes empirical data and their…
Interference and Coverage Analysis of mmWave Inter-Vehicle Broadcast with Directional Antennas
Tianyi Zhang, Hongwei Zhang, Zhibo Meng
Thanks to the availability of large bandwidth and high-gain directional antennas at the millimeter-wave (mmWave) bands, mmWave communications have been considered as one of the pri…
Google Landmark Retrieval 2021 Competition Third Place Solution
Qishen Ha, Bo Liu, Hongwei Zhang
We present our solutions to the Google Landmark Challenges 2021, for both the retrieval and the recognition tracks. Both solutions are ensembles of transformers and ConvNet models…
AdaL: Adaptive Gradient Transformation Contributes to Convergences and Generalizations
Hongwei Zhang, Weidong Zou, Hongbo Zhao +4
Adaptive optimization methods have been widely used in deep learning. They scale the learning rates adaptively according to the past gradient, which has been shown to be effective…
ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Models
Tijin Yan, Hongwei Zhang, Tong Zhou +2
Multivariate time series prediction has attracted a lot of attention because of its wide applications such as intelligence transportation, AIOps. Generative models have achieved im…
Revisiting Graph Convolutional Network on Semi-Supervised Node Classification from an Optimization Perspective
Hongwei Zhang, Tijin Yan, Zenjun Xie +2
Graph convolutional networks (GCNs) have achieved promising performance on various graph-based tasks. However they suffer from over-smoothing when stacking more layers. In this pap…
Stochastic Graph Recurrent Neural Network
Tijin Yan, Hongwei Zhang, Zirui Li +1
Representation learning over graph structure data has been widely studied due to its wide application prospects. However, previous methods mainly focus on static graphs while many…