ARTtransfer

Adaptive and Robust Transfer Learning (ART) is a flexible framework for transfer learning that integrates information from auxiliary data sources to improve model performance on primary tasks. It is designed to be robust against negative transfer by including the non-transfer model in the candidate pool, ensuring stable performance even when auxiliary datasets are less informative. See the paper, Wang, Wu, and Ye (2023) doi:10.1002/sta4.582.

Last 30 days

Last 365 days

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.