21 citations · 54 across the 14 of their papers we have counts for
20 papers
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…
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…
It's All In the Teacher: Zero-Shot Quantization Brought Closer to the Teacher
Kanghyun Choi, Hye Yoon Lee, Deokki Hong +4
Model quantization is considered as a promising method to greatly reduce the resource requirements of deep neural networks. To deal with the performance drop induced by quantizatio…
Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples
Kanghyun Choi, Deokki Hong, Noseong Park +2
Model quantization is known as a promising method to compress deep neural networks, especially for inferences on lightweight mobile or edge devices. However, model quantization usu…