Today’s foundation models are trained on vast amounts of data. The quality and size of this data has a huge impact on the accuracy of these models. Selecting the right amount and variety of data for a given task, however, is a resource-intensive process. In this project, we would like to investigate various state-of-the-art data selection mechanisms from a hardware requirements and …
Supervisors: Pınar Tözün, Ties Robroek
Semester: Fall 2024
Tags: data selection, deep learning, machine learning, resource efficiency