Search Results for author: Noseong Park

Found 60 papers, 31 papers with code

PAC-FNO: Parallel-Structured All-Component Fourier Neural Operators for Recognizing Low-Quality Images

no code implementations20 Feb 2024 Jinsung Jeon, Hyundong Jin, Jonghyun Choi, Sanghyun Hong, Dongeun Lee, Kookjin Lee, Noseong Park

Extensively evaluating methods with seven image recognition benchmarks, we show that the proposed PAC-FNO improves the performance of existing baseline models on images with various resolutions by up to 77. 1% and various types of natural variations in the images at inference.

Continuous-time Autoencoders for Regular and Irregular Time Series Imputation

no code implementations27 Dec 2023 Hyowon Wi, Yehjin Shin, Noseong Park

However, it has been overlooked for a long time to design an imputation method based on continuous-time recurrent neural networks (RNNs), i. e., neural controlled differential equations (NCDEs).

Imputation Irregular Time Series +1

RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation

no code implementations27 Dec 2023 Jeongwhan Choi, Hyowon Wi, Chaejeong Lee, Sung-Bae Cho, Dongha Lee, Noseong Park

Contrastive learning (CL) has emerged as a promising technique for improving recommender systems, addressing the challenge of data sparsity by leveraging self-supervised signals from raw data.

Contrastive Learning Data Integration +1

Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer

1 code implementation19 Dec 2023 Youn-Yeol Yu, Jeongwhan Choi, Woojin Cho, Kookjin Lee, Nayong Kim, Kiseok Chang, Chang-Seung Woo, Ilho Kim, Seok-Woo Lee, Joon-Young Yang, Sooyoung Yoon, Noseong Park

These methods are typically designed to i) reduce the computational cost in solving physical dynamics and/or ii) propose techniques to enhance the solution accuracy in fluid and rigid body dynamics.

Operator-learning-inspired Modeling of Neural Ordinary Differential Equations

no code implementations16 Dec 2023 Woojin Cho, Seunghyeon Cho, Hyundong Jin, Jinsung Jeon, Kookjin Lee, Sanghyun Hong, Dongeun Lee, Jonghyun Choi, Noseong Park

Neural ordinary differential equations (NODEs), one of the most influential works of the differential equation-based deep learning, are to continuously generalize residual networks and opened a new field.

Image Classification Image Generation +3

Polynomial-based Self-Attention for Table Representation learning

no code implementations12 Dec 2023 Jayoung Kim, Yehjin Shin, Jeongwhan Choi, Hyowon Wi, Noseong Park

Structured data, which constitutes a significant portion of existing data types, has been a long-standing research topic in the field of machine learning.

Representation Learning

Graph Convolutions Enrich the Self-Attention in Transformers!

no code implementations7 Dec 2023 Jeongwhan Choi, Hyowon Wi, Jayoung Kim, Yehjin Shin, Kookjin Lee, Nathaniel Trask, Noseong Park

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

Code Classification speech-recognition +2

Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations

no code implementations8 Nov 2023 Seonkyu Lim, Jaehyeon Park, Seojin Kim, Hyowon Wi, Haksoo Lim, Jinsung Jeon, Jeongwhan Choi, Noseong Park

Long-term time series forecasting (LTSF) is a challenging task that has been investigated in various domains such as finance investment, health care, traffic, and weather forecasting.

Time Series Time Series Forecasting +1

Precursor-of-Anomaly Detection for Irregular Time Series

1 code implementation27 Jun 2023 Sheo Yon Jhin, Jaehoon Lee, Noseong Park

Unlike conventional anomaly detection, which focuses on determining whether a given time series observation is an anomaly or not, PoA detection aims to detect future anomalies before they happen.

Anomaly Detection Irregular Time Series +2

Hawkes Process Based on Controlled Differential Equations

1 code implementation9 May 2023 Minju Jo, Seungji Kook, Noseong Park

However, existing neural network-based Hawkes process models not only i) fail to capture such complicated irregular dynamics, but also ii) resort to heuristics to calculate the log-likelihood of events since they are mostly based on neural networks designed for regular discrete inputs.

Irregular Time Series Time Series

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

1 code implementation25 Apr 2023 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.

Contrastive Learning Vocal Bursts Type Prediction

Graph Neural Rough Differential Equations for Traffic Forecasting

2 code implementations20 Mar 2023 Jeongwhan Choi, Noseong Park

A prevalent approach in the field is to combine graph convolutional networks and recurrent neural networks for the spatio-temporal processing.

Time Series Traffic Prediction

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

no code implementations23 Jan 2023 Deokki Hong, Kanghyun Choi, Hye Yoon Lee, Joonsang Yu, Noseong Park, Youngsok Kim, Jinho Lee

Co-exploration of an optimal neural architecture and its hardware accelerator is an approach of rising interest which addresses the computational cost problem, especially in low-profile systems.

Neural Architecture Search

Regular Time-series Generation using SGM

no code implementations20 Jan 2023 Haksoo Lim, Minjung Kim, Sewon Park, Noseong Park

We propose a conditional score network for the time-series generation domain.

Denoising Time Series +2

Learnable Path in Neural Controlled Differential Equations

no code implementations11 Jan 2023 Sheo Yon Jhin, Minju Jo, Seungji Kook, Noseong Park, Sungpil Woo, Sunhwan Lim

Neural controlled differential equations (NCDEs), which are continuous analogues to recurrent neural networks (RNNs), are a specialized model in (irregular) time-series processing.

Irregular Time Series Time Series +2

GREAD: Graph Neural Reaction-Diffusion Networks

1 code implementation25 Nov 2022 Jeongwhan Choi, Seoyoung Hong, Noseong Park, Sung-Bae Cho

In particular, diffusion equations have been widely used for designing the core processing layer of GNNs, and therefore they are inevitably vulnerable to the notorious oversmoothing problem.

Node Classification

Time Series Forecasting with Hypernetworks Generating Parameters in Advance

no code implementations22 Nov 2022 Jaehoon Lee, Chan Kim, Gyumin Lee, Haksoo Lim, Jeongwhan Choi, Kookjin Lee, Dongeun Lee, Sanghyun Hong, Noseong Park

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).

Time Series Time Series Forecasting

Blurring-Sharpening Process Models for Collaborative Filtering

1 code implementation17 Nov 2022 Jeongwhan Choi, Seoyoung Hong, Noseong Park, Sung-Bae Cho

Various methods have been proposed for collaborative filtering, ranging from matrix factorization to graph convolutional methods.

Collaborative Filtering Recommendation Systems

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

no code implementations8 Nov 2022 Seoyoung Hong, Minju Jo, Seungji Kook, Jaeeun Jung, Hyowon Wi, Noseong Park, Sung-Bae Cho

We present a time-series forecasting-based upgrade kit (TimeKit), which works in the following way: it i) first decides a base collaborative filtering algorithm, ii) extracts user/item embedding vectors with the base algorithm from user-item interaction logs incrementally, e. g., every month, iii) trains our time-series forecasting model with the extracted time- series of embedding vectors, and then iv) forecasts the future embedding vectors and recommend with their dot-product scores owing to a recent breakthrough in processing complicated time- series data, i. e., neural controlled differential equations (NCDEs).

Collaborative Filtering Recommendation Systems +2

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

no code implementations10 Oct 2022 Fan Wu, Sanghyun Hong, Donsub Rim, Noseong Park, Kookjin Lee

However, parameterization of dynamics using a neural network makes it difficult for humans to identify causal structures in the data.

Time Series Time Series Analysis

STaSy: Score-based Tabular data Synthesis

1 code implementation8 Oct 2022 Jayoung Kim, Chaejeong Lee, Noseong Park

Our proposed training strategy includes a self-paced learning technique and a fine-tuning strategy, which further increases the sampling quality and diversity by stabilizing the denoising score matching training.

Denoising

Prediction-based One-shot Dynamic Parking Pricing

2 code implementations30 Aug 2022 Seoyoung Hong, Heejoo Shin, Jeongwhan Choi, Noseong Park

Owing to the continuous and bijective characteristics of NODEs, in addition, we design a one-shot price optimization method given a pre-trained prediction model, which requires only one iteration to find the optimal solution.

Spatio-Temporal Forecasting

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

1 code implementation17 Aug 2022 Jihyeon Hyeong, Jayoung Kim, Noseong Park, Sushil Jajodia

Tabular data typically contains private and important information; thus, precautions must be taken before they are shared with others.

Inference Attack Membership Inference Attack

AdamNODEs: When Neural ODE Meets Adaptive Moment Estimation

1 code implementation13 Jul 2022 Suneghyeon Cho, Sanghyun Hong, Kookjin Lee, Noseong Park

In this work, we propose adaptive momentum estimation neural ODEs (AdamNODEs) that adaptively control the acceleration of the classical momentum-based approach.

Computational Efficiency

SPI-GAN: Denoising Diffusion GANs with Straight-Path Interpolations

no code implementations29 Jun 2022 Jinsung Jeon, Noseong Park

Score-based generative models (SGMs) show the state-of-the-art sampling quality and diversity.

Denoising

SOS: Score-based Oversampling for Tabular Data

1 code implementation17 Jun 2022 Jayoung Kim, Chaejeong Lee, Yehjin Shin, Sewon Park, Minjung Kim, Noseong Park, Jihoon Cho

To our knowledge, we are the first presenting a score-based tabular data oversampling method.

Style Transfer

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

no code implementations8 Feb 2022 Jaehoon Lee, Jihyeon Hyeong, Jinsung Jeon, Noseong Park, Jihoon Cho

First, we can further improve the synthesis quality, by decreasing the negative log-density of real records in the process of adversarial training.

Graph Neural Controlled Differential Equations for Traffic Forecasting

1 code implementation7 Dec 2021 Jeongwhan Choi, Hwangyong Choi, Jeehyun Hwang, Noseong Park

A prevalent approach in the field is to combine graph convolutional networks and recurrent neural networks for the spatio-temporal processing.

Spatio-Temporal Forecasting Time Series Forecasting +1

Invertible Tabular GANs: Killing Two Birds with One Stone for Tabular Data Synthesis

1 code implementation NeurIPS 2021 Jaehoon Lee, Jihyeon Hyeong, Jinsung Jeon, Noseong Park, Jihoon Cho

First, we can further improve the synthesis quality, by decreasing the negative log-density of real records in the process of adversarial training.

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

2 code implementations14 Nov 2021 Taeyong Kong, Taeri Kim, Jinsung Jeon, Jeongwhan Choi, Yeon-Chang Lee, Noseong Park, Sang-Wook Kim

To our knowledge, we are the first who design a hybrid method and report the correlation between the graph centrality and the linearity/non-linearity of nodes.

Collaborative Filtering Recommendation Systems

Climate Modeling with Neural Diffusion Equations

2 code implementations11 Nov 2021 Jeehyun Hwang, Jeongwhan Choi, Hwangyong Choi, Kookjin Lee, Dongeun Lee, Noseong Park

On the other hand, neural ordinary differential equations (NODEs) are to learn a latent governing equation of ODE from data.

Weather Forecasting

ConCoDE: Hard-constrained Differentiable Co-Exploration Method for Neural Architectures and Hardware Accelerators

no code implementations29 Sep 2021 Deokki Hong, Kanghyun Choi, Hey Yoon Lee, Joonsang Yu, Youngsok Kim, Noseong Park, Jinho Lee

To handle the hard constraint problem of differentiable co-exploration, we propose ConCoDE, which searches for hard-constrained solutions without compromising the global design objectives.

Neural Architecture Search

Regularizing Image Classification Neural Networks with Partial Differential Equations

no code implementations29 Sep 2021 Jungeun Kim, Seunghyun Hwang, Jeehyun Hwang, Kookjin Lee, Dongeun Lee, Noseong Park

In other words, the knowledge contained by the learned governing equation can be injected into the neural network which approximates the PDE solution function.

Classification Image Classification

IIT-GAN: Irregular and Intermittent Time-series Synthesis with Generative Adversarial Networks

no code implementations29 Sep 2021 Jinsung Jeon, Jeonghak Kim, Haryong Song, Noseong Park

In this paper, we solve the problem of synthesizing irregular and intermittent time-series where values can be missing and may not have specific frequencies, which is far more challenging than existing settings.

Time Series Time Series Analysis

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

1 code implementation11 Aug 2021 Jinsung Jeon, Soyoung Kang, Minju Jo, Seunghyeon Cho, Noseong Park, Seonghoon Kim, Chiyoung Song

Among various such mobile billboards, taxicab rooftop devices are emerging in the market as a brand new media.

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

2 code implementations8 Aug 2021 Jeongwhan Choi, Jinsung Jeon, Noseong Park

In this work, we extend them based on neural ordinary differential equations (NODEs), because the linear GCN concept can be interpreted as a differential equation, and present the method of Learnable-Time ODE-based Collaborative Filtering (LT-OCF).

Collaborative Filtering Recommendation Systems

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

1 code implementation31 May 2021 Sheo Yon Jhin, Minju Jo, Taeyong Kong, Jinsung Jeon, Noseong Park

Neural ordinary differential equations (NODEs) presented a new paradigm to construct (continuous-time) neural networks.

OCT-GAN: Neural ODE-based Conditional Tabular GANs

1 code implementation31 May 2021 Jayoung Kim, Jinsung Jeon, Jaehoon Lee, Jihyeon Hyeong, Noseong Park

Synthesizing tabular data is attracting much attention these days for various purposes.

Clustering Fraud Detection

Large-Scale Data-Driven Airline Market Influence Maximization

no code implementations31 May 2021 Duanshun Li, Jing Liu, Jinsung Jeon, Seoyoung Hong, Thai Le, Dongwon Lee, Noseong Park

On top of the prediction models, we define a budget-constrained flight frequency optimization problem to maximize the market influence over 2, 262 routes.

DISE: Dynamic Integrator Selection to Minimize Forward Pass Time in Neural ODEs

no code implementations1 Jan 2021 Soyoung Kang, Ganghyeon Park, Kwang-Sung Jun, Noseong Park

Because it is not the case that every input requires the advanced integrator, we design an auxiliary neural network to choose an appropriate integrator given input to decrease the overall inference time without significantly sacrificing accuracy.

Neural Partial Differential Equations

no code implementations1 Jan 2021 Jungeun Kim, Seunghyun Hwang, Jihyun Hwang, Kookjin Lee, Dongeun Lee, Noseong Park

Neural ordinary differential equations (neural ODEs) introduced an approach to approximate a neural network as a system of ODEs after considering its layer as a continuous variable and discretizing its hidden dimension.

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

1 code implementation4 Dec 2020 Jungeun Kim, Kookjin Lee, Dongeun Lee, Sheo Yon Jin, Noseong Park

We present a method for learning dynamics of complex physical processes described by time-dependent nonlinear partial differential equations (PDEs).

SHIELD: Defending Textual Neural Networks against Multiple Black-Box Adversarial Attacks with Stochastic Multi-Expert Patcher

1 code implementation ACL 2022 Thai Le, Noseong Park, Dongwon Lee

Even though several methods have proposed to defend textual neural network (NN) models against black-box adversarial attacks, they often defend against a specific text perturbation strategy and/or require re-training the models from scratch.

Adversarial Robustness

Two-stage Training of Graph Neural Networks for Graph Classification

1 code implementation10 Nov 2020 Manh Tuan Do, Noseong Park, Kijung Shin

By adapting five GNN models to our method, we demonstrate the consistent improvement in accuracy and utilization of each GNN's allocated capacity over the original training method of each model up to 5. 4\% points in 12 datasets.

General Classification Graph Classification +3

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

1 code implementation24 Jan 2020 Jihoon Ko, Kyuhan Lee, Kijung Shin, Noseong Park

In this work, we present an inductive machine learning method, called Monte Carlo Simulator (MONSTOR), for estimating the influence of given seed nodes in social networks unseen during training.

Data Synthesis based on Generative Adversarial Networks

no code implementations9 Jun 2018 Noseong Park, Mahmoud Mohammadi, Kshitij Gorde, Sushil Jajodia, Hongkyu Park, Youngmin Kim

We call this property model compatibility.

Databases Cryptography and Security H.3.4; I.2; K.6.5

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

2 code implementations5 Dec 2016 Ankesh Anand, Tanmoy Chakraborty, Noseong Park

Online content publishers often use catchy headlines for their articles in order to attract users to their websites.

Clickbait Detection Feature Engineering

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