arXiv:2605.27991v1 Announce Type: cross Abstract: Deep neural networks (DNNs) have achieved remarkable empirical success, yet their training dynamics remain understood mainly from optimization rather than statistical principles. Here we develop a statistical framework for DNN training in the over-parameterized regime by showing that the prediction induced by continuous-time neural tangent kernel (NTK) gradient flow is exactly equivalent to that from a classical random-effects model. In this framework, training time acts as a variance component, or equivalently an empirical Bayes covariance hyp
The paper attempts to bridge the gap between empirical success and theoretical understanding of deep neural networks, a crucial area of research as AI systems become more prevalent.
A deeper theoretical understanding of DNN training dynamics can lead to more robust, efficient, and interpretable AI systems, impacting their development and deployment.
This theoretical framework re-frames DNN training through a statistical lens, potentially guiding future architectural designs and training methodologies far beyond current 'rules of thumb'.
- · AI researchers
- · AI developers
- · Machine learning platforms
- · High-performance computing providers
- · Ad-hoc AI development
- · Black-box model practices
Improved understanding and interpretability of highly complex AI models in the over-parameterized regime.
Development of new, theoretically grounded optimization algorithms and network architectures that are more efficient or robust.
Accelerated deployment of AI in critical applications where transparency and reliability are paramount, leading to broader societal integration.
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Read at arXiv cs.LG