Year: 2026
Type: conference-paper
Abstract: This paper introduces Volterra Neural Ordinary Differential Equations (VNODE), a piecewise continuous Volterra Neural Network that integrates nonlinear Volterra filtering with continuous-time neural ordinary differential equations for image classification. Drawing inspiration from the visual cortex, where discrete event processing is interleaved with continuous integration, VNODE alternates between discrete Volterra feature extraction and ODE-driven state evolution. This hybrid formulation captures complex patterns while requiring substantially fewer parameters than conventional deep architectures. VNODE consistently outperforms stateof-the-art models with improved computational complexity as exemplified on benchmark datasets like CIFAR-10 and Imagenet-1K.
Authors: Siddharth Roheda, Aniruddha Bala, Rohit Chowdhury, Rohan Jaiswal
Institution: Samsung (India)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4415337137
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