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20212026
most citedGT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks

21 citations · 45 across the 16 of their papers we have counts for

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Showing 2021Show all

6 papers · 1 filter

cs.IR2021★ 1 cited

Linear, or Non-Linear, That is the Question!

Taeyong Kong, Taeri Kim, Jinsung Jeon +4

There were fierce debates on whether the non-linear embedding propagation of GCNs is appropriate to GCN-based recommender systems. It was recently found that the linear embedding p…

cs.IR2021★ 6 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.LG2021★ 3 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.LG2021★ 1 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.LG2021★ 1 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…

cs.LG2021★ 6 cited

ACE-NODE: Attentive Co-Evolving Neural Ordinary Differential Equations

Sheo Yon Jhin, Minju Jo, Taeyong Kong +2

Neural ordinary differential equations (NODEs) presented a new paradigm to construct (continuous-time) neural networks. While showing several good characteristics in terms of the n…