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Harvard University
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Neural scaling laws reveal unexpected insights into how deep learning models defy classical statistics, reshaping our understanding of model performance in scientific applications.
A first-order phase transition in softmax attention reveals how copy mechanisms emerge abruptly during training, challenging existing notions of gradual feature learning.
