6 citations · 12 across the 4 of their papers we have counts for
7 papers
FlowPath: Learning Data-Driven Manifolds with Invertible Flows for Robust Irregularly-sampled Time Series Classification
YongKyung Oh, Dong-Young Lim, Sungil Kim
Modeling continuous-time dynamics from sparse and irregularly-sampled time series remains a fundamental challenge. Neural controlled differential equations provide a principled fra…
TANDEM: Temporal Attention-guided Neural Differential Equations for Missingness in Time Series Classification
YongKyung Oh, Dong-Young Lim, Sungil Kim +1
Handling missing data in time series classification remains a significant challenge in various domains. Traditional methods often rely on imputation, which may introduce bias or fa…
Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data
YongKyung Oh, Dong-Young Lim, Sungil Kim
Irregular sampling intervals and missing values in real-world time series data present challenges for conventional methods that assume consistent intervals and complete data. Neura…
DualDynamics: Synergizing Implicit and Explicit Methods for Robust Irregular Time Series Analysis
YongKyung Oh, Dong-Young Lim, Sungil Kim
Real-world time series analysis faces significant challenges when dealing with irregular and incomplete data. While Neural Differential Equation (NDE) based methods have shown prom…
Modeling Irregular Astronomical Time Series with Neural Stochastic Delay Differential Equations
YongKyung Oh, Seungsu Kam, Dong-Young Lim +1
Astronomical time series from large-scale surveys like LSST are often irregularly sampled and incomplete, posing challenges for classification and anomaly detection. We introduce a…
Continuum Dropout for Neural Differential Equations
Jonghun Lee, YongKyung Oh, Sungil Kim +1
Neural Differential Equations (NDEs) excel at modeling continuous-time dynamics, effectively handling challenges such as irregular observations, missing values, and noise. Despite…