AI Research
Moment-Guided Optimizers for Noisy Deep Learning: MGO-SC as a Simple Consistency Gate for Adam-Style Updates
MGO-SC is presented as a consistency gate for Adam-style updates under noisy conditions.
Adaptive optimizers are widely used in deep learning, yet their behavior can degrade under heavy stochasticity and label noise where the sign and magnitude of gradients can be unstable.
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ArXiv CS.LG
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ArXiv CS.LG
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Gradient instability can undermine adaptive optimizers when training data or updates are noisy. A consistency gate for Adam-style updates could be relevant to making optimization more robust in those conditions.
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