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New theory explains neural network training instabilities

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new theoretical framework using non-Hermitian operator theory to explain and predict training instabilities in deep neural networks. The study identifies that common optimizers like Adam and SGD with momentum exhibit non-normal update operators, which can lead to transient amplification and loss spikes. The proposed pseudospectral precursor bound, using kappa(V) as an indicator, effectively distinguishes between stable and unstable training phases, outperforming traditional spectral radius measures in experiments. AI

IMPACT Provides a new theoretical lens for understanding and potentially mitigating common training failures in deep learning models.

RANK_REASON The cluster contains an academic paper detailing a new theoretical framework for understanding neural network training dynamics.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 · Souvik Ghosh ·

    Non-normal spectral signatures of instability in neural network training dynamics

    arXiv:2605.23476v1 Announce Type: new Abstract: Training instabilities in deep networks - loss spikes, oscillatory convergence, and gradient pathologies - are empirically prevalent but lack a rigorous operator-theoretic explanation. We show that the linearized update operators fo…

  2. arXiv cs.LG TIER_1 · Souvik Ghosh ·

    Non-normal spectral signatures of instability in neural network training dynamics

    Training instabilities in deep networks - loss spikes, oscillatory convergence, and gradient pathologies - are empirically prevalent but lack a rigorous operator-theoretic explanation. We show that the linearized update operators for practically used optimizers are generically no…

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