arXiv:2606.17120v1 Announce Type: new Abstract: Deep neural networks (DNNs) exhibit first order phase transitions under variations of the L2 regularization strength, with each transition marking the onset of a new learnable feature. Below a critical regularization strength, all features are in principle learnable, but coexisting metastable states, separated by energy barriers, can trap the network and impede convergence. A strength of DNNs is their ability to generalize. But many open questions remain, among them the origin of so called grokking: the abrupt, delayed onset of generalization aft
This research provides a theoretical explanation for 'grokking', a phenomenon in deep learning that has previously lacked a clear mechanistic understanding.
Understanding the mechanisms behind grokking—the delayed onset of generalization—is crucial for making deep neural networks more efficient, predictable, and robust.
The explicit explanation of how noise can drive DNNs out of metastable states towards better generalization offers new avenues for algorithm design and optimization.
- · AI researchers
- · Deep learning practitioners
- · Neural network developers
Improved understanding and more effective training strategies for deep neural networks will emerge.
This could lead to accelerated development of more generalized and robust AI models across various applications.
Greater reliability and efficiency of AI systems may impact the feasibility and timeline of advanced AI applications like autonomous agents.
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Read at arXiv cs.LG