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Persistence Paradox in Dynamic Science: Evidence from the Deep Learning Revolution

Bao, Honglin et al.
arXiv (All) · Papers
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computers-and-societydigital-librariesmachine-learning
Persistence is often regarded as a virtue in science. In this paper, however, we challenge this conventional view by highlighting its contextual nature, particularly how persistence can become a liability during paradigm shifts. We focus on the deep learning revolution catalyzed by AlexNet in 2012.
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