CAREER: Mitigating the Lack of Labeled Training Data in Machine Learning Based on Multi-le — NSF Award to University of California
Machine learning has demonstrated great success in numerous applications such as autonomous driving, early detection of diseases, drug design, etc. The accuracy of machine learning models highly depends on the accessibility of large-scale, human-labeled training data. However, such data is often very challenging to acq
| Award title | CAREER: Mitigating the Lack of Labeled Training Data in Machine Learning Based on Multi-le |
|---|---|
| Award ID | 2339216 |
| Awardee | University of California-San Diego |
| City | LA JOLLA |
| State | CA |
| Amount obligated | $370,000 |
| Principal investigator | Pengtao Xie |
| Program | Info Integration & Informatics |
| Start date | 09/01/2024 |
| Abstract | Machine learning has demonstrated great success in numerous applications such as autonomous driving, early detection of diseases, drug design, etc. The accuracy of machine learning models highly depends on the accessibility of large-scale, human-labeled training data. However, such data is often very challenging to acquire in specialized domains such as healthcare, legislation, environmental sciences due to the high costs involved in obtaining high-grade human labels and data privacy concerns. T |
| Source | NSF Awards |
$799/mo
Try NSFGrants →