83 citations · 195 across the 8 of their papers we have counts for
8 papers
Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models
Boyu Zhang, Hongyang Yang, Tianyu Zhou +2
Financial sentiment analysis is critical for valuation and investment decision-making. Traditional NLP models, however, are limited by their parameter size and the scope of their t…
Explainable Deep Reinforcement Learning for Portfolio Management: An Empirical Approach
Mao Guan, Xiao-Yang Liu
Deep reinforcement learning (DRL) has been widely studied in the portfolio management task. However, it is challenging to understand a DRL-based trading strategy because of the bla…
FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance
Xiao-Yang Liu, Hongyang Yang, Jiechao Gao +1
Deep reinforcement learning (DRL) has been envisioned to have a competitive edge in quantitative finance. However, there is a steep development curve for quantitative traders to ob…
FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance
Zechu Li, Xiao-Yang Liu, Jiahao Zheng +3
Machine learning techniques are playing more and more important roles in finance market investment. However, finance quantitative modeling with conventional supervised learning app…
Towards Extremely Compact RNNs for Video Recognition with Fully Decomposed Hierarchical Tucker Structure
Miao Yin, Siyu Liao, Xiao-Yang Liu +2
Recurrent Neural Networks (RNNs) have been widely used in sequence analysis and modeling. However, when processing high-dimensional data, RNNs typically require very large model si…
Deep Learning Based 3D Segmentation: A Survey
Yong He, Hongshan Yu, Xiaoyan Liu +4
3D segmentation is a fundamental and challenging problem in computer vision with applications in autonomous driving and robotics. It has received significant attention from the com…
Differentially Private Synthetic Data: Applied Evaluations and Enhancements
Lucas Rosenblatt, Xiaoyan Liu, Samira Pouyanfar +3
Machine learning practitioners frequently seek to leverage the most informative available data, without violating the data owner's privacy, when building predictive models. Differe…
FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance
Xiao-Yang Liu, Hongyang Yang, Qian Chen +4
As deep reinforcement learning (DRL) has been recognized as an effective approach in quantitative finance, getting hands-on experiences is attractive to beginners. However, to trai…