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20212026
most citedSTaSy: Score-based Tabular data Synthesis

9 citations · 19 across the 9 of their papers we have counts for

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cs.LG2026

TabMosaic: Unified In-Context Diffusion for Tabular Generative Tasks

Jayoung Kim, Sanghyun Hong, Noseong Park

Generative modeling for tabular data has advanced to the point where models can synthesize realistic records, recover missing values, and generate samples conditioned on class labe…

cs.LG2023★ 2 cited

Graph Convolutions Enrich the Self-Attention in Transformers!

Jeongwhan Choi, Hyowon Wi, Jayoung Kim +4

Transformers, renowned for their self-attention mechanism, have achieved state-of-the-art performance across various tasks in natural language processing, computer vision, time-ser…

cs.LG2023★ 4 cited

CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular Synthesis

Chaejeong Lee, Jayoung Kim, Noseong Park

With growing attention to tabular data these days, the attempt to apply a synthetic table to various tasks has been expanded toward various scenarios. Owing to the recent advances…

cs.LG2022★ 9 cited

STaSy: Score-based Tabular data Synthesis

Jayoung Kim, Chaejeong Lee, Noseong Park

Tabular data synthesis is a long-standing research topic in machine learning. Many different methods have been proposed over the past decades, ranging from statistical methods to d…

cs.LG2022★ 2 cited

SOS: Score-based Oversampling for Tabular Data

Jayoung Kim, Chaejeong Lee, Yehjin Shin +4

Score-based generative models (SGMs) are a recent breakthrough in generating fake images. SGMs are known to surpass other generative models, e.g., generative adversarial networks (…

cs.LG2022★ 1 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.LG2022

EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting

Sheo Yon Jhin, Jaehoon Lee, Minju Jo +5

Deep learning inspired by differential equations is a recent research trend and has marked the state of the art performance for many machine learning tasks. Among them, time-series…

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…