# VNODE: A Piecewise Continuous Volterra Neural Network

Source: [VNODE: A Piecewise Continuous Volterra Neural Network](<https://www.sota2.com/research/paper/68fa7bb81e209131cea8a235>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T05:47:25.110Z

Authors: Siddharth Roheda, Aniruddha Bala, Rohit Chowdhury, Rohan Jaiswal

Published: 2025-09-29T12:05:57.000Z

## 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 state of the art models with improved computational complexity as exemplified on benchmark datasets like CIFAR10 and Imagenet1K.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2509.24659>)

[PDF](<https://arxiv.org/pdf/2509.24659.pdf>)