Construct · 1 fields · 1 citations
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In machine learning, edge of stability refers to during full-batch gradient descent the sharpness of the loss rises until it hovers near two divided by the step size, while loss keeps falling non-monotonically. This meaning comes from Cohen (2021) in arXiv.[1]
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