arXiv:2607.06290v1 Announce Type: new Abstract: We study the infinite-width Gaussian-process limit of random neural networks through the lens of tensor programs, and we provide a quantitative convergence theory in Wasserstein distance. Our main result gives explicit finite-width error bounds, of order inverse square-root of the widths between finite-network executions and their Gaussian-process limits. The framework is architecture-agnostic and covers feed-forward models together with weight-sharing schemes relevant for recurrent and transformer-type architectures.
The paper provides a quantitative framework for understanding the behavior of random neural networks, an area of active research, offering convergence theory and error bounds for Gaussian-process limits.
This research provides a deeper theoretical understanding of neural networks, which can lead to more robust, predictable, and potentially more efficient AI model design.
The explicit finite-width error bounds allow for better quantitative analysis of AI models, shifting from purely empirical observations to theoretically grounded predictions about their behavior.
- · AI researchers
- · Machine learning engineers
- · AI platform developers
- · Empirical AI development
- · Black-box AI approaches
Improved theoretical understanding of neural network limits.
Development of new AI architectures and training methodologies that leverage this theoretical insight.
More reliable and interpretable AI systems, potentially accelerating regulatory clarity and adoption in critical sectors.
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