4 citations · 4 across the 7 of their papers we have counts for
8 papers
MercerFlow: Flow Matching in a Kernel-Induced Latent Space for Probabilistic Forecasting
Ilya Kuleshov, Egor Serov, Alexey Zaytsev
Recent work has shown that probabilistic flow matching for time series forecasting benefits from a data-matched prior. The resulting prior introduces local correlations, which a se…
CLaST: Context-aware Contrastive VAE for Probabilistic Time Series Forecasting
Alexander Marusov, Dmitry Anikin, Petr Sokerin +3
Probabilistic forecasting models are widely used for time series forecasting in domains such as energy systems, finance, medicine, and transportation. In recent years, deep generat…
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…
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…
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…
DOoM: Difficult Olympiads of Math
Ilya Kuleshov, Ilin Pavel, Nikolay Kompanets +2
This paper introduces DOoM, a new open-source benchmark designed to assess the capabilities of language models in solving mathematics and physics problems in Russian. The benchmark…
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…
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…