29 citations · 151 across the 35 of their papers we have counts for
15 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…
TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering
Seoyoung Hong, Minju Jo, Seungji Kook +4
Recommender systems are a long-standing research problem in data mining and machine learning. They are incremental in nature, as new user-item interaction logs arrive. In real-worl…
GREAD: Graph Neural Reaction-Diffusion Networks
Jeongwhan Choi, Seoyoung Hong, Noseong Park +1
Graph neural networks (GNNs) are one of the most popular research topics for deep learning. GNN methods typically have been designed on top of the graph signal processing theory. I…
Blurring-Sharpening Process Models for Collaborative Filtering
Jeongwhan Choi, Seoyoung Hong, Noseong Park +1
Collaborative filtering is one of the most fundamental topics for recommender systems. Various methods have been proposed for collaborative filtering, ranging from matrix factoriza…
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…
GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks
Jinsung Jeon, Jeonghak Kim, Haryong Song +2
Time series synthesis is an important research topic in the field of deep learning, which can be used for data augmentation. Time series data types can be broadly classified into r…