Parameter-Efficient Neural CDEs via Implicit Function Jacobians

2025/11/25 by Ilya Kuleshov, Alexey Zaytsev, Kuleshov, Ilya +1
Computer Science · Engineering · Physics and Astronomy · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Industrial Technology and Control Systems #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Neural Networks and Applications

paper · doi:10.48550/arxiv.2512.20625

openalex publication_date 2025/11/25 · openalex created_date 2025/12/26 · openalex updated_date 2026/07/28

Abstract

Neural Controlled Differential Equations (Neural CDEs, NCDEs) are a unique branch of methods, specifically tailored for analysing temporal sequences. However, they come with drawbacks, the main one being the number of parameters, required for the method's operation. In this paper, we propose an alternative, parameter-efficient look at Neural CDEs. It requires much fewer parameters, while also presenting a very logical analogy as the "Continuous RNN", which the Neural CDEs aspire to.

Citations

• Neural Controlled Differential Equations for Irregular Time Series
• GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series
• Surrogate Gradient Learning in Spiking Neural Networks
• Neural Ordinary Differential Equations

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