515 citations · 791 across the 8 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…
Predicting Multiple Demographic Attributes with Task Specific Embedding Transformation and Attention Network
Raehyun Kim, Hyunjae Kim, Janghyuk Lee +1
Most companies utilize demographic information to develop their strategy in a market. However, such information is not available to most retail companies. Several studies have been…
Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network
Jinho Lee, Raehyun Kim, Yookyung Koh +1
We applied Deep Q-Network with a Convolutional Neural Network function approximator, which takes stock chart images as input, for making global stock market predictions. Our model…