8 citations · 19 across the 8 of their papers we have counts for
8 papers
Unified Backbone Refinement for Diffusion Models via Internal-Latent Analysis
Haksoo Lim, Myeongjin Lee, Wonjoon Chang +1
Diffusion models have achieved remarkable success across diverse domains, with performance closely related to the denoising backbones that parameterize the score function. In this…
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…
Parameterized Physics-informed Neural Networks for Parameterized PDEs
Woojin Cho, Minju Jo, Haksoo Lim +4
Complex physical systems are often described by partial differential equations (PDEs) that depend on parameters such as the Reynolds number in fluid mechanics. In applications such…
Efficiently Parameterized Neural Metriplectic Systems
Anthony Gruber, Kookjin Lee, Haksoo Lim +2
Metriplectic systems are learned from data in a way that scales quadratically in both the size of the state and the rank of the metriplectic data. Besides being provably energy con…
Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations
Seonkyu Lim, Jaehyeon Park, Seojin Kim +5
Long-term time series forecasting (LTSF) is a challenging task that has been investigated in various domains such as finance investment, health care, traffic, and weather forecasti…
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…
Regular Time-series Generation using SGM
Haksoo Lim, Minjung Kim, Sewon Park +1
Score-based generative models (SGMs) are generative models that are in the spotlight these days. Time-series frequently occurs in our daily life, e.g., stock data, climate data, an…
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…