550 citations · 718 across the 45 of their papers we have counts for
9 papers · 1 filter
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…
Graph Neural Controlled Differential Equations for Traffic Forecasting
Jeongwhan Choi, Hwangyong Choi, Jeehyun Hwang +1
Traffic forecasting is one of the most popular spatio-temporal tasks in the field of machine learning. A prevalent approach in the field is to combine graph convolutional networks…
Climate Modeling with Neural Diffusion Equations
Jeehyun Hwang, Jeongwhan Choi, Hwangyong Choi +3
Owing to the remarkable development of deep learning technology, there have been a series of efforts to build deep learning-based climate models. Whereas most of them utilize recur…
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…
Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting
Sheo Yon Jhin, Heejoo Shin, Seoyoung Hong +2
Neural networks inspired by differential equations have proliferated for the past several years. Neural ordinary differential equations (NODEs) and neural controlled differential e…
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…
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…
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…
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…