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Learning with Volterra Neural Networks: A System Theoretic Perspective
Higher-order interaction components are important for signal, image, and video modeling, but explicit high-order operators often suffer from rapidly increasing parameter and computational costs. This paper presents kVNN, a learnable kernelized Volterra Neural operator for compact higher-order filtering. The motivation is to use kernelization to improve the efficiency of Volterra-type neural operators while providing a structured interpretation of their higher-order components. The proposed formulation combines the order-wise structure of Volterra filtering with learnable polynomial-kernel atom
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-01T22:58:05.000Z
First collected: 2026-09-21T05:51:54.566Z. This is not the publication date.