arXiv:2602.02157v2 Announce Type: replace Abstract: Neural Controlled Differential Equations (Neural CDEs) provide a powerful continuous-time framework for sequence modeling, yet the roughness of the driving control path often restricts their efficiency. Standard splines introduce high-frequency variations that force adaptive solvers to take excessively small steps, driving up the Number of Function Evaluations (NFE). We propose a novel approach to Neural CDE path construction that replaces exact interpolation with Kernel and Gaussian Process (GP) smoothing, enabling explicit control over traj
This research addresses a known efficiency bottleneck in Neural CDEs, a crucial component for continuous-time sequence modeling, which has become more prominent with increasing demand for sophisticated AI models.
Improved efficiency in Neural CDEs via kernel smoothing can accelerate the development and deployment of advanced AI models, impacting domains requiring continuous data analysis and potentially lowering computational costs.
The proposed method allows for more efficient training and inference of Neural CDE models by reducing the Number of Function Evaluations, making them more practical for real-world applications.
- · AI researchers and developers
- · Companies using continuous-time AI models
- · Cloud computing providers (through increased utilization)
- · Inefficient Neural CDE implementations
Reduced computational resources required for training and deploying certain types of AI models.
Faster development cycles for AI systems that rely on continuous data streams and sequence modeling.
Broader adoption of Neural CDEs in sectors like finance, healthcare, and autonomous systems due to improved practicality.
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Read at arXiv cs.LG