7 citations · 15 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…
Comprehensive Review of Neural Differential Equations for Time Series Analysis
YongKyung Oh, Seungsu Kam, Jonghun Lee +3
Time series modeling and analysis have become critical in various domains. Conventional methods such as RNNs and Transformers, while effective for discrete-time and regularly sampl…