arXiv:2605.29823v1 Announce Type: new Abstract: Deep networks often exhibit a preference for "simple" solutions, and such a simplicity bias is widely believed to play a key role in generalization. Yet a broadly applicable, quantitative measure of simplicity remains elusive. We introduce polynomial representations as a distribution-aware, low-dimensional surrogate for neural functions: we approximate a network's predictive behavior along data-dependent interpolation paths using orthogonal polynomial bases, yielding a compact functional representation. We show that the effective degree of this r
The continuous evolution of deep learning architectures necessitates better tools to understand their internal mechanisms and generalization properties, driving research into quantitative simplicity metrics.
Understanding and quantifying simplicity in deep networks is crucial for building more reliable, interpretable, and efficient AI systems with improved generalization capabilities.
This research provides a novel, quantifiable method to assess the 'simplicity' of AI solutions, potentially leading to new optimization techniques and a deeper theoretical understanding of neural networks.
- · AI researchers
- · Deep learning practitioners
- · Companies developing AI models
- · Approaches relying solely on heuristic simplicity measures
Improved network interpretability and predictability of generalization performance.
Development of new AI training algorithms that explicitly optimize for 'simplicity' alongside performance.
More robust, less 'black box' AI systems across critical applications, fostering greater trust and adoption.
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.AI