1 citations · 3 across the 19 of their papers we have counts for
19 papers
EvidenceLens: A Claim-Evidence Matrix for Auditing Financial Question Answering
Fengchen Gu, Xiaotian Ren, Zhengyong Jiang +6
Large language models are increasingly used to answer questions over annual reports, earnings decks, and analyst notes, yet their outputs remain difficult to verify in high-stakes…
Addressing Market Regime Changes and Heavy-Tailed Returns in Portfolio Optimization via Bayesian VAR and Elliptical Black-Litterman
Daniil Mikriukov, Ruoyu Sun, Angelos Stefanidis +2
Deep reinforcement learning (DRL) frameworks for portfolio optimization have shown promise for their ability to learn allocation rules dynamically from market data. However, these…
MERIT: Multilingual Expert-Reward Informed Tuning for Chinese-Centric Low-Resource Machine Translation
Zhixiang Lu, Chong Zhang, Chenyu Xue +4
Neural machine translation (NMT) from Chinese to low-resource Southeast Asian languages remains severely constrained by the extreme scarcity of clean parallel corpora and the perva…
SAGE: Sustainable Agent-Guided Expert-tuning for Culturally Attuned Translation in Low-Resource Southeast Asia
Zhixiang Lu, Chong Zhang, Yulong Li +5
The vision of an inclusive World Wide Web is impeded by a severe linguistic divide, particularly for communities in low-resource regions of Southeast Asia. While large language mod…
SAMP-HDRL: Segmented Allocation with Momentum-Adjusted Utility for Multi-agent Portfolio Management via Hierarchical Deep Reinforcement Learning
Xiaotian Ren, Nuerxiati Abudurexiti, Zhengyong Jiang +3
Portfolio optimization in non-stationary markets is challenging due to regime shifts, dynamic correlations, and the limited interpretability of deep reinforcement learning (DRL) po…
Silhouette-to-Contour Registration: Aligning Intraoral Scan Models with Cephalometric Radiographs
Yiyi Miao, Taoyu Wu, Ji Jiang +6
Reliable 3D-2D alignment between intraoral scan (IOS) models and lateral cephalometric radiographs is critical for orthodontic diagnosis, yet conventional intensity-driven registra…
Dental3R: Geometry-Aware Pairing for Intraoral 3D Reconstruction from Sparse-View Photographs
Yiyi Miao, Taoyu Wu, Tong Chen +6
Intraoral 3D reconstruction is fundamental to digital orthodontics, yet conventional methods like intraoral scanning are inaccessible for remote tele-orthodontics, which typically…
MedFact: A Large-scale Chinese Dataset for Evidence-based Medical Fact-checking of LLM Responses
Tong Chen, Zimu Wang, Yiyi Miao +6
Medical fact-checking has become increasingly critical as more individuals seek medical information online. However, existing datasets predominantly focus on human-generated conten…
DeepGB-TB: A Risk-Balanced Cross-Attention Gradient-Boosted Convolutional Network for Rapid, Interpretable Tuberculosis Screening
Zhixiang Lu, Yulong Li, Feilong Tang +5
Large-scale tuberculosis (TB) screening is limited by the high cost and operational complexity of traditional diagnostics, creating a need for artificial-intelligence solutions. We…
Factor-MCLS: Multi-agent learning system with reward factor matrix and multi-critic framework for dynamic portfolio optimization
Ruoyu Sun, Angelos Stefanidis, Zhengyong Jiang +1
Typical deep reinforcement learning (DRL) agents for dynamic portfolio optimization learn the factors influencing portfolio return and risk by analyzing the output values of the re…
MTS: A Deep Reinforcement Learning Portfolio Management Framework with Time-Awareness and Short-Selling
Fengchen Gu, Zhengyong Jiang, Ángel F. García-Fernández +3
Portfolio management remains a crucial challenge in finance, with traditional methods often falling short in complex and volatile market environments. While deep reinforcement appr…
MIGT: Memory Instance Gated Transformer Framework for Financial Portfolio Management
Fengchen Gu, Angelos Stefanidis, Ángel García-Fernández +2
Deep reinforcement learning (DRL) has been applied in financial portfolio management to improve returns in changing market conditions. However, unlike most fields where DRL is wide…
KD-MSLRT: Lightweight Sign Language Recognition Model Based on Mediapipe and 3D to 1D Knowledge Distillation
Yulong Li, Bolin Ren, Ke Hu +4
Artificial intelligence has achieved notable results in sign language recognition and translation. However, relatively few efforts have been made to significantly improve the quali…
A novel multi-agent dynamic portfolio optimization learning system based on hierarchical deep reinforcement learning
Ruoyu Sun, Yue Xi, Angelos Stefanidis +2
Deep Reinforcement Learning (DRL) has been extensively used to address portfolio optimization problems. The DRL agents acquire knowledge and make decisions through unsupervised int…
Combining Transformer based Deep Reinforcement Learning with Black-Litterman Model for Portfolio Optimization
Ruoyu Sun, Angelos Stefanidis, Zhengyong Jiang +1
As a model-free algorithm, deep reinforcement learning (DRL) agent learns and makes decisions by interacting with the environment in an unsupervised way. In recent years, DRL algor…
CAD: Clustering And Deep Reinforcement Learning Based Multi-Period Portfolio Management Strategy
Zhengyong Jiang, Jeyan Thiayagalingam, Jionglong Su +1
In this paper, we present a novel trading strategy that integrates reinforcement learning methods with clustering techniques for portfolio management in multi-period trading. Speci…
An Empirical Study of Challenges in Converting Deep Learning Models
Moses Openja, Amin Nikanjam, Ahmed Haj Yahmed +3
There is an increase in deploying Deep Learning (DL)-based software systems in real-world applications. Usually DL models are developed and trained using DL frameworks that have th…
A Deep Deterministic Policy Gradient-based Strategy for Stocks Portfolio Management
Huanming Zhang, Zhengyong Jiang, Jionglong Su
With the improvement of computer performance and the development of GPU-accelerated technology, trading with machine learning algorithms has attracted the attention of many researc…
Application of Deep Q-Network in Portfolio Management
Ziming Gao, Yuan Gao, Yi Hu +2
Machine Learning algorithms and Neural Networks are widely applied to many different areas such as stock market prediction, face recognition and population analysis. This paper wil…