Name
Title | ||
|---|---|---|
A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting. |
Abstract | ||
|---|---|---|
We study epidemic forecasting on real-world health data by a graph-structured recurrent neural network (GSRNN). We achieve state-of-the-art forecasting accuracy on the benchmark CDC dataset. To improve model efficiency, we sparsify the network weights via transformed-$ell_1$ penalty and maintain prediction accuracy at the same level with 70% of the network weights being zero. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-21803-4_73 | WCGO |
Field | DocType | Volume |
Graph,Recurrent neural network,Artificial intelligence,Mathematics,Machine learning | Journal | abs/1902.05113 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhijian Li | 1 | 0 | 0.34 |
| Xiyang Luo | 2 | 17 | 5.09 |
| Bao Wang | 3 | 59 | 6.09 |
| Andrea L. Bertozzi | 4 | 486 | 61.55 |
| Jack Xin | 5 | 212 | 25.49 |