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Data Selection: A General Principle for Building Small Interpretable Models
April 30, 2024, 4:44 a.m. | Abhishek Ghose
cs.LG updates on arXiv.org arxiv.org
Abstract: We present convincing empirical evidence for an effective and general strategy for building accurate small models. Such models are attractive for interpretability and also find use in resource-constrained environments. The strategy is to learn the training distribution and sample accordingly from the provided training data. The distribution learning algorithm is not a contribution of this work; our contribution is a rigorous demonstration of the broad utility of this strategy in various practical settings. We apply …
abstract arxiv building cs.lg data distribution environments evidence general interpretability learn sample small strategy training training data type
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