1-Lipschitz neural networks are more expressive with N-activations

Prach B, Lampert C. 1-Lipschitz neural networks are more expressive with N-activations. arXiv, 2311.06103.

Download (ext.)

Preprint | Submitted | English
Abstract
A crucial property for achieving secure, trustworthy and interpretable deep learning systems is their robustness: small changes to a system's inputs should not result in large changes to its outputs. Mathematically, this means one strives for networks with a small Lipschitz constant. Several recent works have focused on how to construct such Lipschitz networks, typically by imposing constraints on the weight matrices. In this work, we study an orthogonal aspect, namely the role of the activation function. We show that commonly used activation functions, such as MaxMin, as well as all piece-wise linear ones with two segments unnecessarily restrict the class of representable functions, even in the simplest one-dimensional setting. We furthermore introduce the new N-activation function that is provably more expressive than currently popular activation functions. We provide code at this https URL.
Publishing Year
Date Published
2023-11-10
Journal Title
arXiv
Article Number
2311.06103
IST-REx-ID

Cite this

Prach B, Lampert C. 1-Lipschitz neural networks are more expressive with N-activations. arXiv. doi:10.48550/ARXIV.2311.06103
Prach, B., & Lampert, C. (n.d.). 1-Lipschitz neural networks are more expressive with N-activations. arXiv. https://doi.org/10.48550/ARXIV.2311.06103
Prach, Bernd, and Christoph Lampert. “1-Lipschitz Neural Networks Are More Expressive with N-Activations.” ArXiv, n.d. https://doi.org/10.48550/ARXIV.2311.06103.
B. Prach and C. Lampert, “1-Lipschitz neural networks are more expressive with N-activations,” arXiv. .
Prach B, Lampert C. 1-Lipschitz neural networks are more expressive with N-activations. arXiv, 2311.06103.
Prach, Bernd, and Christoph Lampert. “1-Lipschitz Neural Networks Are More Expressive with N-Activations.” ArXiv, 2311.06103, doi:10.48550/ARXIV.2311.06103.
All files available under the following license(s):
Copyright Statement:
This Item is protected by copyright and/or related rights. [...]

Link(s) to Main File(s)
Access Level
OA Open Access

Export

Marked Publications

Open Data ISTA Research Explorer

Sources

arXiv 2311.06103

Search this title in

Google Scholar