53 citations · 149 across the 23 of their papers we have counts for
7 papers · 1 filter
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…
Mining Causality from Continuous-time Dynamics Models: An Application to Tsunami Forecasting
Fan Wu, Sanghyun Hong, Donsub Rim +2
Continuous-time dynamics models, such as neural ordinary differential equations, have enabled the modeling of underlying dynamics in time-series data and accurate forecasting. Howe…
Not All Asians are the Same: A Disaggregated Approach to Identifying Anti-Asian Racism in Social Media
Fan Wu, Sanyam Lakhanpal, Qian Li +4
Recent policy initiatives have acknowledged the importance of disaggregating data pertaining to diverse Asian ethnic communities to gain a more comprehensive understanding of their…
Parameter-varying neural ordinary differential equations with partition-of-unity networks
Kookjin Lee, Nathaniel Trask
In this study, we propose parameter-varying neural ordinary differential equations (NODEs) where the evolution of model parameters is represented by partition-of-unity networks (PO…
AdamNODEs: When Neural ODE Meets Adaptive Moment Estimation
Suneghyeon Cho, Sanghyun Hong, Kookjin Lee +1
Recent work by Xia et al. leveraged the continuous-limit of the classical momentum accelerated gradient descent and proposed heavy-ball neural ODEs. While this model offers computa…
Stochastic Galerkin methods for linear stability analysis of systems with parametric uncertainty
Bedřich Sousedík, Kookjin Lee
We present a method for linear stability analysis of systems with parametric uncertainty formulated in the stochastic Galerkin framework. Specifically, we assume that for a model p…
Unsupervised physics-informed disentanglement of multimodal data for high-throughput scientific discovery
Nathaniel Trask, Carianne Martinez, Kookjin Lee +1
We introduce physics-informed multimodal autoencoders (PIMA) - a variational inference framework for discovering shared information in multimodal scientific datasets representative…