arXiv:2505.21423v3 Announce Type: replace Abstract: The remarkable generalization properties of overparameterized networks are often attributed to implicit biases, such as norm minimization at small learning rates and low sharpness in the Edge-of-Stability regime. In this work, we argue that a comprehensive understanding of the generalization performance of gradient descent requires analyzing the interaction between these various forms of implicit regularization. We empirically demonstrate that the learning rate interpolates between low parameter norm and low sharpness of the trained model. We
The paper provides new insights into the fundamental learning dynamics of overparameterized neural networks, leveraging recent advancements in understanding implicit biases.
Understanding the interplay between different regularization mechanisms in AI training is crucial for designing more efficient, robust, and generalizable models, impacting performance and resource utilization.
Our theoretical understanding of why deep learning models generalize so well deepens, potentially leading to more deliberate and less empirical optimization strategies.
- · AI researchers
- · Deep learning framework developers
- · AI-driven industries
- · Empirical hyperparameter tuners
Improved theoretical models for deep learning generalization become available.
More principled approaches to hyperparameter optimization, particularly learning rate selection, emerge.
The development of new AI architectures or training methodologies that explicitly leverage these nuanced bias interactions for superior performance accelerates.
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