32 citations · 61 across the 8 of their papers we have counts for
7 papers · 1 filter
HINTS: Extraction of Human Insights from Time-Series Without External Sources
Sheo Yon Jhin, Noseong Park
Human decision-making, emotions, and collective psychology are complex factors that shape the temporal dynamics observed in financial and economic systems. Many recent time series…
Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization
Sheo Yon Jhin, Seojin Kim, Noseong Park
Time series forecasting has been an essential field in many different application areas, including economic analysis, meteorology, and so forth. The majority of time series forecas…
Learnable Path in Neural Controlled Differential Equations
Sheo Yon Jhin, Minju Jo, Seungji Kook +3
Neural controlled differential equations (NCDEs), which are continuous analogues to recurrent neural networks (RNNs), are a specialized model in (irregular) time-series processing.…
LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations
Jaehoon Lee, Jinsung Jeon, Sheo yon Jhin +5
The problem of processing very long time-series data (e.g., a length of more than 10,000) is a long-standing research problem in machine learning. Recently, one breakthrough, calle…
EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting
Sheo Yon Jhin, Jaehoon Lee, Minju Jo +5
Deep learning inspired by differential equations is a recent research trend and has marked the state of the art performance for many machine learning tasks. Among them, time-series…
Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting
Sheo Yon Jhin, Heejoo Shin, Seoyoung Hong +2
Neural networks inspired by differential equations have proliferated for the past several years. Neural ordinary differential equations (NODEs) and neural controlled differential e…
ACE-NODE: Attentive Co-Evolving Neural Ordinary Differential Equations
Sheo Yon Jhin, Minju Jo, Taeyong Kong +2
Neural ordinary differential equations (NODEs) presented a new paradigm to construct (continuous-time) neural networks. While showing several good characteristics in terms of the n…