550 citations · 718 across the 45 of their papers we have counts for
46 papers
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…
MadSGM: Multivariate Anomaly Detection with Score-based Generative Models
Haksoo Lim, Sewon Park, Minjung Kim +3
The time-series anomaly detection is one of the most fundamental tasks for time-series. Unlike the time-series forecasting and classification, the time-series anomaly detection typ…
Precursor-of-Anomaly Detection for Irregular Time Series
Sheo Yon Jhin, Jaehoon Lee, Noseong Park
Anomaly detection is an important field that aims to identify unexpected patterns or data points, and it is closely related to many real-world problems, particularly to application…
Hawkes Process Based on Controlled Differential Equations
Minju Jo, Seungji Kook, Noseong Park
Hawkes processes are a popular framework to model the occurrence of sequential events, i.e., occurrence dynamics, in several fields such as social diffusion. In real-world scenario…
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…
Graph Neural Rough Differential Equations for Traffic Forecasting
Jeongwhan Choi, Noseong Park
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…
Regular Time-series Generation using SGM
Haksoo Lim, Minjung Kim, Sewon Park +1
Score-based generative models (SGMs) are generative models that are in the spotlight these days. Time-series frequently occurs in our daily life, e.g., stock data, climate data, an…
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…
GREAD: Graph Neural Reaction-Diffusion Networks
Jeongwhan Choi, Seoyoung Hong, Noseong Park +1
Graph neural networks (GNNs) are one of the most popular research topics for deep learning. GNN methods typically have been designed on top of the graph signal processing theory. I…
Blurring-Sharpening Process Models for Collaborative Filtering
Jeongwhan Choi, Seoyoung Hong, Noseong Park +1
Collaborative filtering is one of the most fundamental topics for recommender systems. Various methods have been proposed for collaborative filtering, ranging from matrix factoriza…
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…
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…
Phishing URL Detection: A Network-based Approach Robust to Evasion
Taeri Kim, Noseong Park, Jiwon Hong +1
Many cyberattacks start with disseminating phishing URLs. When clicking these phishing URLs, the victim's private information is leaked to the attacker. There have been proposed se…
Prediction-based One-shot Dynamic Parking Pricing
Seoyoung Hong, Heejoo Shin, Jeongwhan Choi +1
Many U.S. metropolitan cities are notorious for their severe shortage of parking spots. To this end, we present a proactive prediction-driven optimization framework to dynamically…
An Empirical Study on the Membership Inference Attack against Tabular Data Synthesis Models
Jihyeon Hyeong, Jayoung Kim, Noseong Park +1
Tabular data typically contains private and important information; thus, precautions must be taken before they are shared with others. Although several methods (e.g., differential…
AdamNODEs: When Neural ODE Meets Adaptive Moment Estimation
Suneghyeon Cho, Sanghyun Hong, Kookjin Lee +1
Recent work by Xia et al. leveraged the continuous-limit of the classical momentum accelerated gradient descent and proposed heavy-ball neural ODEs. While this model offers computa…
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 (…
SPI-GAN: Denoising Diffusion GANs with Straight-Path Interpolations
Jinsung Jeon, Noseong Park
Score-based generative models (SGMs) show the state-of-the-art sampling quality and diversity. However, their training/sampling complexity is notoriously high due to the highly com…
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…
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…
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…