490 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- China University of GeosciencesCN22 papers
- University of FreiburgDE12 papers
- Chinese Academy of SciencesCN9 papers
- University of Naples Federico IIIT7 papers
- Beijing Normal UniversityCN5 papers
- University of GrazAT5 papers
- Wuhan UniversityCN5 papers
- Tsinghua UniversityCN4 papers
- University of Chinese Academy of SciencesCN4 papers
- Macau University of Science and TechnologyMO3 papers
- National Space Science CenterCN3 papers
- Nawi GrazAT3 papers
6 papers · 1 filter
Echoes: Unsupervised Debiasing via Pseudo-bias Labeling in an Echo Chamber
Rui Hu, Yahan Tu, Jitao Sang
Neural networks often learn spurious correlations when exposed to biased training data, leading to poor performance on out-of-distribution data. A biased dataset can be divided, ac…
An Efficient Deep Learning Approach Using Improved Generative Adversarial Networks for Incomplete Information Completion of Self-driving
Jingzhi Tu, Gang Mei, Francesco Piccialli
Autonomous driving is the key technology of intelligent logistics in Industrial Internet of Things (IIoT). In autonomous driving, the appearance of incomplete point clouds losing g…
Heterogeneous Data Fusion Considering Spatial Correlations using Graph Convolutional Networks and its Application in Air Quality Prediction
Zhengjing Ma, Gang Mei, Salvatore Cuomo +1
Heterogeneous data are commonly adopted as the inputs for some models that predict the future trends of some observations. Existing predictive models typically ignore the inconsist…
Deep Reinforcement Learning (DRL): Another Perspective for Unsupervised Wireless Localization
You Li, Xin Hu, Yuan Zhuang +3
Location is key to spatialize internet-of-things (IoT) data. However, it is challenging to use low-cost IoT devices for robust unsupervised localization (i.e., localization without…
Julia Language in Machine Learning: Algorithms, Applications, and Open Issues
Kaifeng Gao, Gang Mei, Francesco Piccialli +3
Machine learning is driving development across many fields in science and engineering. A simple and efficient programming language could accelerate applications of machine learning…
A Frobenius norm regularization method for convolutional kernels to avoid unstable gradient problem
Pei-Chang Guo
Convolutional neural network is a very important model of deep learning. It can help avoid the exploding/vanishing gradient problem and improve the generalizability of a neural net…