Grokking in linear DNNs is explained as hysteresis in L2 phase transitions where SGD noise enables escape from low-accuracy metastable phases with Arrhenius scaling; the same mechanism is suggested for nonlinear networks.
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Noise-Driven Escape from Metastable Phases explains Grokking in Deep Neural Networks
Grokking in linear DNNs is explained as hysteresis in L2 phase transitions where SGD noise enables escape from low-accuracy metastable phases with Arrhenius scaling; the same mechanism is suggested for nonlinear networks.