The minimal width of neural networks and immersions of real projective space

Abstract:We investigate the minimal width of deep neural networks that can approximate all continuous functions from $\mathbb{R}^n$ to $\mathbb{R}^m$ in the topology of uniform convergence on compact subsets. We show that whenever the real projective space $\mathbb{RP}^{n-1}$ admits a smooth immersion into $\mathbb{R}^n$, the minimal width is at least $n+\min\{n,m\}$, assuming that the activation function is continuous and monotone. As a consequence, the minimal width of ReLU networks is $n+m$ whenever $n$ is equal to 2, 3, 4, 7 or 8 and $1\le m \le n$, extending results of Hanin-Sellke and Hwang. This suggests an intriguing connection between the approximation theory of neural networks and differential topology.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2610.20229 [stat.ML]

(or arXiv:2610.20229v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2610.20229 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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