5 papers
Efficient Neural Controlled Differential Equations via Attentive Kernel Smoothing
Egor Serov, Ilya Kuleshov, Alexey Zaytsev
Neural Controlled Differential Equations (Neural CDEs) provide a powerful continuous-time framework for sequence modeling, yet the roughness of the driving control path often restr…
U-Former ODE: Fast Probabilistic Forecasting of Irregular Time Series
Ilya Kuleshov, Alexander Marusov, Alexey Zaytsev
Probabilistic forecasting of irregularly sampled time series is crucial in domains such as healthcare and finance, yet it remains a formidable challenge. Existing Neural Controlled…
DeNOTS: Stable Deep Neural ODEs for Time Series
Ilya Kuleshov, Evgenia Romanenkova, Vladislav Zhuzhel +3
Neural CDEs provide a natural way to process the temporal evolution of irregular time series. The number of function evaluations (NFE) is these systems' natural analog of depth (th…
Parameter-Efficient Neural CDEs via Implicit Function Jacobians
Ilya Kuleshov, Alexey Zaytsev
Neural Controlled Differential Equations (Neural CDEs, NCDEs) are a unique branch of methods, specifically tailored for analysing temporal sequences. However, they come with drawba…
Learning Transactions Representations for Information Management in Banks: Mastering Local, Global, and External Knowledge
Alexandra Bazarova, Maria Kovaleva, Ilya Kuleshov +7
In today's world, banks use artificial intelligence to optimize diverse business processes, aiming to improve customer experience. Most of the customer-related tasks can be categor…