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Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond
We study the data selection problem, whose aim is to select a small representative subset of data that can be used to efficiently train a machine learning model.
Deep Fusion: Efficient Network Training via Pre-trained Initializations
Finally, we validate our theoretical framework, which guides the optimal use of Deep Fusion, showing that with carefully optimized training dynamics, it significantly reduces both training time and resource consumption.
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