515 citations · 547 across the 5 of their papers we have counts for
8 papers
Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs
Seongjun Yun, Minbyul Jeong, Sungdong Yoo +5
Graph Neural Networks (GNNs) have been widely applied to various fields due to their powerful representations of graph-structured data. Despite the success of GNNs, most existing G…
MAPS: Multi-agent Reinforcement Learning-based Portfolio Management System
Jinho Lee, Raehyun Kim, Seok-Won Yi +1
Generating an investment strategy using advanced deep learning methods in stock markets has recently been a topic of interest. Most existing deep learning methods focus on proposin…
Graph Transformer Networks
Seongjun Yun, Minbyul Jeong, Raehyun Kim +2
Graph neural networks (GNNs) have been widely used in representation learning on graphs and achieved state-of-the-art performance in tasks such as node classification and link pred…
WATTNet: Learning to Trade FX via Hierarchical Spatio-Temporal Representation of Highly Multivariate Time Series
Michael Poli, Jinkyoo Park, Ilija Ilievski
Finance is a particularly challenging application area for deep learning models due to low noise-to-signal ratio, non-stationarity, and partial observability. Non-deliverable-forwa…
HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction
Raehyun Kim, Chan Ho So, Minbyul Jeong +3
Many researchers both in academia and industry have long been interested in the stock market. Numerous approaches were developed to accurately predict future trends in stock prices…
SAIN: Self-Attentive Integration Network for Recommendation
Seoungjun Yun, Raehyun Kim, Miyoung Ko +1
With the growing importance of personalized recommendation, numerous recommendation models have been proposed recently. Among them, Matrix Factorization (MF) based models are the m…