10 citations · 14 across the 5 of their papers we have counts for
6 papers · 1 filter
TSGM: Regular and Irregular Time-series Generation using Score-based Generative Models
Haksoo Lim, Jaehoon Lee, Sewon Park +2
Score-based generative models (SGMs) have demonstrated unparalleled sampling quality and diversity in numerous fields, such as image generation, voice synthesis, and tabular data s…
MadSGM: Multivariate Anomaly Detection with Score-based Generative Models
Haksoo Lim, Sewon Park, Minjung Kim +3
The time-series anomaly detection is one of the most fundamental tasks for time-series. Unlike the time-series forecasting and classification, the time-series anomaly detection typ…
Time Series Forecasting with Hypernetworks Generating Parameters in Advance
Jaehoon Lee, Chan Kim, Gyumin Lee +6
Forecasting future outcomes from recent time series data is not easy, especially when the future data are different from the past (i.e. time series are under temporal drifts). Exis…
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…
Invertible Tabular GANs: Killing Two Birds with OneStone for Tabular Data Synthesis
Jaehoon Lee, Jihyeon Hyeong, Jinsung Jeon +2
Tabular data synthesis has received wide attention in the literature. This is because available data is often limited, incomplete, or cannot be obtained easily, and data privacy is…
OCT-GAN: Neural ODE-based Conditional Tabular GANs
Jayoung Kim, Jinsung Jeon, Jaehoon Lee +2
Synthesizing tabular data is attracting much attention these days for various purposes. With sophisticate synthetic data, for instance, one can augment its training data. For the p…