Test-time local training of neural network for tabular data.

ABSTRACT: Generally, a neural network is globally trained using the dataset provided in the training phase to optimize its parameters. The trained neural network is then used to make predictions for query instances in the inference phase. This global learning approach leads to a neural network that performs well universally across various query instances. However, it may overlook local structures in some low-density data regions, potentially degrading generalization performance in these regions. Although several test-time adaptation methods have been explored in recent years, they are typically designed for vision domains and are not intended for or do not readily transfer to tabular data. In this study, we propose a test-time local training method, specifically tailored for tabular data, to make th

SUBMITTER: Kang M 

PROVIDER: S-EPMC12804743 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature