arXiv:2602.00827v2 Announce Type: replace Abstract: Feature learning strength (FLS), i.e., the inverse of the effective output scaling of a model, plays a critical role in shaping the optimization dynamics of neural nets. While its impact has been extensively studied under the asymptotic regimes -- both in training time and FLS -- existing theory offers limited insight into how FLS affects generalization in practical settings, such as when training is stopped upon reaching a target training risk. In this work, we investigate the impact of FLS on generalization in deep networks under such pract
The proliferation of complex deep learning models necessitates a deeper theoretical understanding of their generalization capabilities to improve reliability and efficiency.
This research provides insights into how model parameters affect generalization, which is crucial for developing more robust and efficient AI systems and deploying them in critical applications.
Our understanding of the factors governing model generalization beyond asymptotic regimes is evolving, leading to more targeted training strategies for deep networks.
- · AI researchers
- · Deep learning practitioners
- · AI hardware manufacturers
- · Developers relying on trial-and-error optimization
Improved deep learning model architectures and training strategies will emerge.
More reliable and less resource-intensive AI models could accelerate AI adoption in various industries.
The enhanced understanding of generalization in AI models could lead to new types of explainable AI systems, boosting trust and deployment in regulated sectors.
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