GS-KAN: Parameter-Efficient Kolmogorov-Arnold Networks via Sprecher-Type Shared Basis Functions
arXiv:2512.09084v3 Announce Type: replace Abstract: The Kolmogorov-Arnold representation theorem offers a theoretical alternative to Multi-Layer Perceptrons (MLPs) by placing learnable univariate functions on edges rather than nodes. While recent implementations such as Kolmogorov-Arnold Networks (KANs) demonstrate high approximation capabilities, they suffer from significant parameter inefficiency due to the requirement of maintaining unique parameterizations for every network edge. In this work, we propose GS-KAN (Generalized Sprecher-KAN), a lightweight architecture inspired by David Sprech
The search for more efficient and robust neural network architectures in AI is ongoing, driven by computational constraints and the desire for improved interpretability.
This development proposes a more parameter-efficient alternative to current Kolmogorov-Arnold Networks (KANs), potentially leading to more scalable and compact AI models.
The proposed GS-KAN architecture could reduce the computational and memory footprint of complex AI models, making advanced AI techniques more accessible and deployable.
- · AI researchers and developers
- · Edge computing platforms
- · Companies seeking efficient AI deployment
- · Traditional MLP-based systems (relatively)
- · Hardware vendors optimized solely for dense, high-parameter models
Reduced compute requirements for training and inference of certain AI models.
Faster iteration cycles in AI model development due to lower resource consumption.
Democratization of advanced AI capabilities by making them viable on less powerful hardware and with smaller datasets.
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Read at arXiv cs.LG