b'Noseong Park'

research

∙ 08/29/2023

MadSGM: Multivariate Anomaly Detection with Score-based Generative Models

The time-series anomaly detection is one of the most fundamental tasks f...

0 Haksoo Lim, et al. ∙

research

∙ 06/27/2023

Precursor-of-Anomaly Detection for Irregular Time Series

Anomaly detection is an important field that aims to identify unexpected...

0 Sheo Yon Jhin, et al. ∙

research

∙ 05/09/2023

Hawkes Process based on Controlled Differential Equations

Hawkes processes are a popular framework to model the occurrence of sequ...

0 Minju Jo, et al. ∙

research

∙ 04/25/2023

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

With growing attention to tabular data these days, the attempt to apply ...

0 Chaejeong Lee, et al. ∙

research

∙ 03/20/2023

Graph Neural Rough Differential Equations for Traffic Forecasting

Traffic forecasting is one of the most popular spatio-temporal tasks in ...

0 Jeongwhan Choi, et al. ∙

research

∙ 01/23/2023

Enabling Hard Constraints in Differentiable Neural Network and Accelerator Co-Exploration

Co-exploration of an optimal neural architecture and its hardware accele...

0 Deokki Hong, et al. ∙

research

∙ 01/20/2023

Regular Time-series Generation using SGM

Score-based generative models (SGMs) are generative models that are in t...

0 Haksoo Lim, et al. ∙

research

∙ 01/11/2023

Learnable Path in Neural Controlled Differential Equations

Neural controlled differential equations (NCDEs), which are continuous a...

0 Sheo Yon Jhin, et al. ∙

research

∙ 11/25/2022

GREAD: Graph Neural Reaction-Diffusion Equations

Graph neural networks (GNNs) are one of the most popular research topics...

1 Jeongwhan Choi, et al. ∙

research

∙ 11/22/2022

Time Series Forecasting with Hypernetworks Generating Parameters in Advance

Forecasting future outcomes from recent time series data is not easy, es...

0 Jaehoon Lee, et al. ∙

research

∙ 11/17/2022

Perturbation-Recovery Method for Recommendation

Collaborative filtering is one of the most influential recommender syste...

1 Jeongwhan Choi, et al. ∙

research

∙ 11/08/2022

TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering

Recommender systems are a long-standing research problem in data mining ...

0 Seoyoung Hong, et al. ∙

research

∙ 10/10/2022

Mining Causality from Continuous-time Dynamics Models: An Application to Tsunami Forecasting

Continuous-time dynamics models, such as neural ordinary differential eq...

0 Fan Wu, et al. ∙

research

∙ 10/08/2022

STaSy: Score-based Tabular data Synthesis

Tabular data synthesis is a long-standing research topic in machine lear...

0 Jayoung Kim, et al. ∙

research

∙ 10/05/2022

GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks

Time series synthesis is an important research topic in the field of dee...

0 Jinsung Jeon, et al. ∙

research

∙ 09/03/2022

Phishing URL Detection: A Network-based Approach Robust to Evasion

Many cyberattacks start with disseminating phishing URLs. When clicking ...

0 Taeri Kim, et al. ∙

research

∙ 08/30/2022

Prediction-based One-shot Dynamic Parking Pricing

Many U.S. metropolitan cities are notorious for their severe shortage of...

15 Seoyoung Hong, et al. ∙

research

∙ 08/17/2022

An Empirical Study on the Membership Inference Attack against Tabular Data Synthesis Models

Tabular data typically contains private and important information; thus,...

0 Jihyeon Hyeong, et al. ∙

research

∙ 07/13/2022

AdamNODEs: When Neural ODE Meets Adaptive Moment Estimation

Recent work by Xia et al. leveraged the continuous-limit of the classica...

0 Suneghyeon Cho, et al. ∙

research

∙ 06/29/2022

SPI-GAN: Distilling Score-based Generative Models with Straight-Path Interpolations

Score-based generative models (SGMs) are a recently proposed paradigm fo...

0 Jinsung Jeon, et al. ∙

research

∙ 06/17/2022

SOS: Score-based Oversampling for Tabular Data

Score-based generative models (SGMs) are a recent breakthrough in genera...

0 Jayoung Kim, et al. ∙

research

∙ 04/19/2022

LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations

The problem of processing very long time-series data (e.g., a length of ...

0 Jaehoon Lee, et al. ∙

research

∙ 04/19/2022

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

Deep learning inspired by differential equations is a recent research tr...

0 Sheo Yon Jhin, et al. ∙

research

∙ 03/31/2022

It's All In the Teacher: Zero-Shot Quantization Brought Closer to the Teacher

Model quantization is considered as a promising method to greatly reduce...

0 Kanghyun Choi, et al. ∙

research

∙ 02/08/2022

Invertible Tabular GANs: Killing Two Birds with OneStone for Tabular Data Synthesis

Tabular data synthesis has received wide attention in the literature. Th...

0 Jaehoon Lee, et al. ∙

research

∙ 12/07/2021

Graph Neural Controlled Differential Equations for Traffic Forecasting

Traffic forecasting is one of the most popular spatio-temporal tasks in ...

0 Jeongwhan Choi, et al. ∙

research

∙ 11/14/2021

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

There were fierce debates on whether the non-linear embedding propagatio...

0 Taeyong Kong, et al. ∙

research

∙ 11/11/2021

Climate Modeling with Neural Diffusion Equations

Owing to the remarkable development of deep learning technology, there h...

0 Jeehyun Hwang, et al. ∙

research

∙ 11/04/2021

Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples

Model quantization is known as a promising method to compress deep neura...

0 Kanghyun Choi, et al. ∙

research

∙ 09/04/2021

Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting

Neural networks inspired by differential equations have proliferated for...

0 Sheo Yon Jhin, et al. ∙

research

∙ 08/11/2021

LightMove: A Lightweight Next-POI Recommendation for Taxicab Rooftop Advertising

Mobile digital billboards are an effective way to augment brand-awarenes...

0 Jinsung Jeon, et al. ∙

research

∙ 08/08/2021

LT-OCF: Learnable-Time ODE-based Collaborative Filtering

Collaborative filtering (CF) is a long-standing problem of recommender s...

0 Jeongwhan Choi, et al. ∙

research

∙ 05/31/2021

Large-Scale Data-Driven Airline Market Influence Maximization

We present a prediction-driven optimization framework to maximize the ma...

0 Duanshun Li, et al. ∙

research

∙ 05/31/2021

OCT-GAN: Neural ODE-based Conditional Tabular GANs

Synthesizing tabular data is attracting much attention these days for va...

0 Jayoung Kim, et al. ∙

research

∙ 05/31/2021

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

Neural ordinary differential equations (NODEs) presented a new paradigm ...

0 Sheo Yon Jhin, et al. ∙

research

∙ 12/04/2020

DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation

We present a method for learning dynamics of complex physical processes ...

0 Jungeun Kim, et al. ∙

research

∙ 11/20/2020

Detecting Universal Trigger's Adversarial Attack with Honeypot

The Universal Trigger (UniTrigger) is a recently-proposed powerful adver...

0 Thai Le, et al. ∙

research

∙ 11/17/2020

SIENA: Stochastic Multi-Expert Neural Patcher

Neural network (NN) models that are solely trained to maximize the likel...

0 Thai Le, et al. ∙

research

∙ 11/10/2020

Two-stage Training of Graph Neural Networks for Graph Classification

Graph Neural Networks (GNNs) have received massive attention in the fiel...

0 Manh Tuan Do, et al. ∙

research

∙ 06/28/2020

Human Mobility during COVID-19 in the Context of Mild Social Distancing: Implications for Technological Interventions

The COVID-19 pandemic has brought both tangible and intangible damage to...

0 Myeong Lee, et al. ∙

research

∙ 01/24/2020

MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Social Networks

Influence maximization (IM) is one of the most important problems in soc...

0 Jihoon Ko, et al. ∙

research

∙ 06/11/2019

Solving Large-Scale 0-1 Knapsack Problems and its Application to Point Cloud Resampling

0-1 knapsack is of fundamental importance in computer science, business,...

0 Duanshun Li, et al. ∙

research

∙ 10/03/2018

GI-OHMS: Graphical Inference to Detect Overlapping Communities

Discovery of communities in complex networks is a topic of considerable ...

0 Nasheen Nur, et al. ∙

research

∙ 06/09/2018

Data Synthesis based on Generative Adversarial Networks

Privacy is an important concern for our society where sharing data with ...

0 Noseong Park, et al. ∙

research

∙ 05/07/2018

MEGAN: Mixture of Experts of Generative Adversarial Networks for Multimodal Image Generation

Recently, generative adversarial networks (GANs) have shown promising pe...

0 David Keetae Park, et al. ∙

research

∙ 12/05/2016

We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!

Online content publishers often use catchy headlines for their articles ...

0 Ankesh Anand, et al. ∙

Noseong Park

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