Explicit neural network classifiers for non-separable data
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Abstract (arXiv)
We fully characterize a large class of feedforward neural networks in terms of truncation maps. As an application, we show how a ReLU neural network can implement a feature map which separates concentric data.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 8 references that are in the map. First posted on arXiv 25 Apr 2025.
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6 of 8 references.
References · 6
- 2017Approximating Continuous Functions by ReLU Nets of Minimal Widthcited by 122
- 2017Universal Function Approximation by Deep Neural Nets with Bounded Width and ReLU Activationscited by 104
- 2018Deep, Skinny Neural Networks are not Universal Approximatorscited by 38
- 2023Geometric structure of Deep Learning networks and construction of global mathcal L2 minimizerscited by 6
- 2023The Geometric Structure of Fully-Connected ReLU Layerscited by 4
- 2024Interpretable global minima of deep ReLU neural networks on sequentially separable datacited by 4
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