33 citations · 38 across the 12 of their papers we have counts for
12 papers
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…
DentalSplat: Dental Occlusion Novel View Synthesis from Sparse Intra-Oral Photographs
Yiyi Miao, Taoyu Wu, Tong Chen +6
In orthodontic treatment, particularly within telemedicine contexts, observing patients' dental occlusion from multiple viewpoints facilitates timely clinical decision-making. Rece…
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…
MSWAL: 3D Multi-class Segmentation of Whole Abdominal Lesions Dataset
Zhaodong Wu, Qiaochu Zhao, Ming Hu +13
With the significantly increasing incidence and prevalence of abdominal diseases, there is a need to embrace greater use of new innovations and technology for the diagnosis and tre…
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…
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…
Kolmogorov-Arnold Networks: A Critical Assessment of Claims, Performance, and Practical Viability
Yuntian Hou, Tianrui Ji, Di Zhang +1
Kolmogorov-Arnold Networks (KANs) have gained significant attention as an alternative to traditional multilayer perceptrons, with proponents claiming superior interpretability and…
Contrastive Learning Via Equivariant Representation
Sifan Song, Jinfeng Wang, Qiaochu Zhao +6
Invariant Contrastive Learning (ICL) methods have achieved impressive performance across various domains. However, the absence of latent space representation for distortion (augmen…
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…
Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning
Yiman Liu, Qiming Huang, Xiaoxiang Han +9
Purpose: Congenital heart defect (CHD) is the most common birth defect. Thoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemodynamics…