The Power of Few: Accelerating and Enhancing Data Reweighting with Coreset Selection

arXiv:2403.12166v1 Announce Type: new
Abstract: As machine learning tasks continue to evolve, the trend has been to gather larger datasets and train increasingly larger models. While this has led to advancements in accuracy, it has also escalated computational costs to unsustainable levels. Addressing this, our work aims to strike a delicate balance between computational efficiency and model accuracy, a persisting challenge in the field. We introduce a novel method that employs core subset selection for reweighting, effectively optimizing both computational …

abstract accuracy arxiv computational costs cs.lg data datasets gather larger models machine machine learning power stat.ml tasks train trend type work