21 citations · 45 across the 16 of their papers we have counts for
12 papers · 1 filter
Physics-Guided Geometric Diffusion for Macro Placement Generation
Jongho Yoon, Jinsung Jeon, Seokhyeong Kang
Macro placement is a pivotal stage in VLSI physical design, fundamentally determining the overall chip performance. Recent data-driven placement methods have demonstrated significa…
Possibility for Proactive Anomaly Detection
Jinsung Jeon, Jaehyeon Park, Sewon Park +3
Time-series anomaly detection, which detects errors and failures in a workflow, is one of the most important topics in real-world applications. The purpose of time-series anomaly d…
Operator-learning-inspired Modeling of Neural Ordinary Differential Equations
Woojin Cho, Seunghyeon Cho, Hyundong Jin +6
Neural ordinary differential equations (NODEs), one of the most influential works of the differential equation-based deep learning, are to continuously generalize residual networks…
Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations
Seonkyu Lim, Jaehyeon Park, Seojin Kim +5
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 forecasti…
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…
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…
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…
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…
LightMove: A Lightweight Next-POI Recommendation for Taxicab Rooftop Advertising
Jinsung Jeon, Soyoung Kang, Minju Jo +4
Mobile digital billboards are an effective way to augment brand-awareness. Among various such mobile billboards, taxicab rooftop devices are emerging in the market as a brand new m…
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…