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Ilya Kuleshov

2 accepted papers

2026

DeNOTS: Stable Deep Neural ODEs for Time Series

ICLR 2026poster

Neural Controlled Differential Equations (Neural CDEs) provide a principled framework for modelling irregular time series in continuous time. Their number of function evaluations (NFEs) acts as a natural analogue of depth in discrete neural networks and is typically controlled indirectly via solver…

Cited by 0Source ↗Scholar ↗
2026

Efficient Neural Controlled Differential Equations via Attentive Kernel Smoothing

ICML 2026poster

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 excessiv…

Cited by 0Source ↗Scholar ↗