4.3k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Seoul National UniversityKR138 papers
- The University of TokyoJP133 papers
- Centre National de la Recherche ScientifiqueFR113 papers
- Pusan National UniversityKR110 papers
- Massachusetts Institute of TechnologyUS105 papers
- Korea Astronomy and Space Science InstituteKR104 papers
- California Institute of TechnologyUS99 papers
- Université Libre de BruxellesBE99 papers
- University of ChicagoUS99 papers
- National Astronomical Observatory of JapanJP97 papers
- Lomonosov Moscow State UniversityRU96 papers
- Universität HamburgDE94 papers
6 papers · 2 filters
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…
From Patterns to Predictions: A Shapelet-Based Framework for Directional Forecasting in Noisy Financial Markets
Juwon Kim, Hyunwook Lee, Hyotaek Jeon +2
Directional forecasting in financial markets requires both accuracy and interpretability. Before the advent of deep learning, interpretable approaches based on human-defined patter…
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…
ST-MTM: Masked Time Series Modeling with Seasonal-Trend Decomposition for Time Series Forecasting
Hyunwoo Seo, Chiehyeon Lim
Forecasting complex time series is an important yet challenging problem that involves various industrial applications. Recently, masked time-series modeling has been proposed to ef…
Conditional Temporal Neural Processes with Covariance Loss
Boseon Yoo, Jiwoo Lee, Janghoon Ju +3
We introduce a novel loss function, Covariance Loss, which is conceptually equivalent to conditional neural processes and has a form of regularization so that is applicable to many…