Dynamic Momentum Recalibration in Online Gradient Learning

• URL: http://arxiv.org/abs/2603.06120v1
• Date: Fri, 06 Mar 2026 10:23:40 GMT
• Title: Dynamic Momentum Recalibration in Online Gradient Learning
• Abstract summary: Gradient Descent (SGD) and its momentum variants form the backbone of deep learning optimization.<n>In this work, we reinterpret gradient updates through the lens of signal processing and reveal that fixed momentum coefficients inherently distort the balance between bias and variance, leading to skewed or suboptimal parameter updates.<n>We propose SGDF (SGD with Filter), an online, time-varying gain to dynamically refine gradient estimation.
• Score: 6.6375578955686345
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Stochastic Gradient Descent (SGD) and its momentum variants form the backbone of deep learning optimization, yet the underlying dynamics of their gradient behavior remain insufficiently understood. In this work, we reinterpret gradient updates through the lens of signal processing and reveal that fixed momentum coefficients inherently distort the balance between bias and variance, leading to skewed or suboptimal parameter updates. To address this, we propose SGDF (SGD with Filter), an optimizer inspired by the principles of Optimal Linear Filtering. SGDF computes an online, time-varying gain to dynamically refine gradient estimation by minimizing the mean-squared error, thereby achieving an optimal trade-off between noise suppression and signal preservation. Furthermore, our approach could extend to other optimizers, showcasing its broad applicability to optimization frameworks. Extensive experiments across diverse architectures and benchmarks demonstrate SGDF surpasses conventional momentum methods and achieves performance on par with or surpassing state-of-the-art optimizers.

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