Automated Author ProfileYu, Jianxing
Yu, Jianxing
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.4 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This dataset is used to evaluate the effectiveness of the Growing-MoE learning framework. The dataset contains tasks across computer vision (CV) and natural language processing (NLP).The dataset includes CV tasks such as CIFAR, ImageNet, Cars, and Flowers, as well as NLP tasks including English Wikipedia and GLUE benchmarks. Our learning framework aims to accelerate training of large Mixture-of-Experts models, which employs a progressive way to learn the model from local to global. We verify its performance with several popular networks, such as DeiT, Swin, GPT-2 on the tasks across CV and NLP. We also conduct transfer learning to prove reflected the versatility and flexibility of our framework.
Authors
- Yu, Jianxing ;
- Jiang, Haowei