7 citations · 15 across the 9 of their papers we have counts for
9 papers
Rarity-Gated Context Conditioning for Offline Imitation Learning-Based Maritime Anomaly Detection
Yongmin Kim, ByeongHoon Jeon, Sungil Kim
Contextual anomaly detection aims to identify abnormal behavior conditional on context variables, but practical deployments often face highly imbalanced context distributions where…
Multi-Field Hybrid Retrieval-Augmented Generation for Maritime Accident Root Cause Analysis
Seongjin Kim, Sungil Kim
Maritime accident adjudication reports contain critical tribunal findings for root cause analysis (RCA), yet retrieving relevant precedents and drafting consistent reports from dec…
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…
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…