Researchers have developed a new theoretical framework using non-Hermitian operator theory to explain prevalent training instabilities in deep neural networks, such as loss spikes and gradient pathologies. The study identifies that optimizers like Adam and SGD with momentum are generically non-normal, meaning their update operators can lead to transient amplification of errors. This non-normality, quantified by a pseudospectral precursor bound, serves as an early warning indicator for instability, outperforming traditional spectral radius measures in predicting training phases. AI
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IMPACT Provides a new theoretical lens for understanding and potentially mitigating training instabilities in deep learning models.
RANK_REASON Academic paper detailing a new theoretical framework for understanding neural network training dynamics. [lever_c_demoted from research: ic=1 ai=1.0]