most citedGT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks

21 citations · 39 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG202221 cited

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…

cs.LG20221 cited

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…

cs.IR20216 cited

LT-OCF: Learnable-Time ODE-based Collaborative Filtering

Jeongwhan Choi, Jinsung Jeon, Noseong Park

Collaborative filtering (CF) is a long-standing problem of recommender systems. Many novel methods have been proposed, ranging from classical matrix factorization to recent graph c…

cs.LG20213 cited

LightMove: A Lightweight Next-POI Recommendation for Taxicab Rooftop Advertising

Jinsung Jeon, Soyoung Kang, Minju Jo +4

Mobile digital billboards are an effective way to augment brand-awareness. Among various such mobile billboards, taxicab rooftop devices are emerging in the market as a brand new m…

cs.LG20211 cited

Large-Scale Data-Driven Airline Market Influence Maximization

Duanshun Li, Jing Liu, Jinsung Jeon +4

We present a prediction-driven optimization framework to maximize the market influence in the US domestic air passenger transportation market by adjusting flight frequencies. At th…

cs.LG20211 cited

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…