Broken Ergodicity and the Violation of the Fluctuation-Dissipation Theorem Lead to Generalization Beyond Overfitting in Machine Learning
arXiv:2607.04135v1 Announce Type: cross Abstract: The remarkable ability of modern neural networks to generalize improves with increasing network capacity, even when the number of model parameters or effective degrees of freedom exceeds the number of training data points. This phenomenon is all the more surprising given that generalization error diverges when the number of model parameters approaches a critical value from below. Here we use dynamical mean field theory to show that this so-called "double descent" behavior is the outcome of a phase transition in the stochastic field theory descr
The proliferation of overparameterized neural networks has made understanding their generalization capabilities a critical, ongoing research frontier.
This research provides a theoretical underpinning for the 'double descent' phenomenon, which explains how large models can generalize well despite overfitting training data, guiding future AI model design and optimization.
The understanding of generalization in machine learning evolves from a simple trade-off between model complexity and data to a more nuanced view involving phase transitions and broken ergodicity.
- · AI researchers
- · Deep learning practitioners
- · Companies building large AI models
- · Traditional statistical learning theories
- · AI development relying solely on classical overfitting avoidance
This theoretical advance could lead to new architectural designs or training methodologies that explicitly leverage the principles of broken ergodicity and fluctuation-dissipation theorem violations.
Improved theoretical understanding and practical methods for large model generalization will accelerate the development and deployment of more robust and capable AI systems across various applications.
This could contribute to a paradigm shift in AI research, moving towards dynamical systems and statistical physics as foundational tools for understanding advanced machine learning, fostering interdisciplinary innovation.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG