550 citations · 718 across the 45 of their papers we have counts for
28 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
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…
DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation
Jungeun Kim, Kookjin Lee, Dongeun Lee +2
We present a method for learning dynamics of complex physical processes described by time-dependent nonlinear partial differential equations (PDEs). Our particular interest lies in…