少数的力量：通过核心集选择加速并增强数据重加权
The Power of Few: Accelerating and Enhancing Data Reweighting with Coreset Selection
AI总结：
针对大模型训练计算成本过高的问题，提出一种利用核心集选择进行数据重加权的新方法，通过最小化异常值影响并将权重传播至全量数据，有效平衡了计算效率与模型准确率。
摘要：
随着机器学习任务不断演进，趋势是收集更大的数据集并训练越来越大的模型。虽然这带来了准确率的提升，但也使计算成本攀升至不可持续的水平。针对此问题，我们的工作旨在计算效率与模型准确率之间取得微妙的平衡，这是该领域一项持续的挑战。我们引入了一种新方法，采用核心子集选择进行重加权，有效优化了计算时间和模型性能。通过关注战略性选择的核心集，我们的方法提供了稳健的表示，因为它能高效地最小化异常值的影响。重新校准的权重随后被映射回并在整个数据集中传播。我们的实验结果证实了该方法的有效性，突显了其作为模型训练的可扩展且精确的解决方案的潜力。
英文摘要：
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 time and model performance. By focusing on a strategically selected coreset, our approach offers a robust representation, as it efficiently minimizes the influence of outliers. The re-calibrated weights are then mapped back to and propagated across the entire dataset. Our experimental results substantiate the effectiveness of this approach, underscoring its potential as a scalable and precise solution for model training.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
