5 citations · 9 across the 8 of their papers we have counts for
8 papers
CF-JEPA: Mask-free forward prediction with asymmetric encoder utilization for time-series representation learning
Jaehoon Lee, Sunghyun Sim
Self-supervised learning (SSL) for time-series representation learning is dominated by two paradigms: contrastive methods, which face challenges in constructing positive or negativ…
FEATHer: Fourier-Efficient Adaptive Temporal Hierarchy Forecaster for Time-Series Forecasting
Jaehoon Lee, Seungwoo Lee, Younghwi Kim +2
Time-series forecasting is fundamental in industrial domains like manufacturing and smart factories. As systems evolve toward automation, models must operate on edge devices (e.g.,…
Rewarding Structural Conformance of Reasoning using Process Mining
Yongjae Lee, Taekhyun Park, Sunghyun Sim +1
Recent advances in sparse reward policy gradient methods have enabled effective reinforcement learning (RL)-based language model post-training. However, for reasoning tasks such as…
Artificial Intelligence-based Smart Port Logistics Metaverse for Enhancing Productivity, Environment, and Safety in Port Logistics: A Case Study of Busan Port
Sunghyun Sim, Dohee Kim, Kikun Park +1
The increase in global trade, the impact of COVID-19, and the tightening of environmental and safety regulations have brought significant changes to the maritime transportation mar…
Legacy Learning Strategy Based on Few-Shot Font Generation Models for Automatic Text Design in Metaverse Content
Younghwi Kim, Dohee Kim, Seok Chan Jeong +1
The metaverse consists of hardware, software, and content, among which text design plays a critical role in enhancing user immersion and usability as a content element. However, in…
Distributed Lag Transformer based on Time-Variable-Aware Learning for Explainable Multivariate Time Series Forecasting
Younghwi Kim, Dohee Kim, Joongrock Kim +1
Time series data is a key element of big data analytics, commonly found in domains such as finance, healthcare, climate forecasting, and transportation. In large scale real world s…
Automatic Discovery of Multi-perspective Process Model using Reinforcement Learning
Sunghyun Sim, Ling Liu, Hyerim Bae
Process mining is a methodology for the derivation and analysis of process models based on the event log. When process mining is employed to analyze business processes, the process…
Correlation recurrent units: A novel neural architecture for improving the predictive performance of time-series data
Sunghyun Sim, Dohee Kim, Hyerim Bae
The time-series forecasting (TSF) problem is a traditional problem in the field of artificial intelligence. Models such as Recurrent Neural Network (RNN), Long Short Term Memory (L…