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arXiv Machine Learning By Gaoxiang Tang, Huanran Chen, Ziming Liu

Beyond Quadratic Loss: The Stability Phase Diagram of Adam

Read the original on arXiv Machine Learning →

The paper studies how Adam’s two momentum timescales, β1 and β3, influence loss spikes during neural‑network training. By mapping training dynamics across the (β1,β3) plane, the authors find an approximately linear boundary, 1-β3 = C(1-β1), that separates spiky from non‑spiky behavior, with the coefficient C linked to the effective loss exponent in superquadratic loss functions. They also show that confident cross‑entropy losses create a core–wall landscape that behaves superquadratically at the scale of an optimizer update, explaining the observed spikes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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