4 citations · 6 across the 6 of their papers we have counts for
6 papers
Metadata-Free Meta-Reweighted Direct Preference Optimization under Noisy Preference Labels
Hua Qu, Yifan Li, Xiaodong Yuan
Direct Preference Optimization (DPO) has become an important method for aligning large language models (LLMs) with human preferences because it removes the need for explicit reward…
Think-then-Act: A Dual-Angle Evaluated Retrieval-Augmented Generation
Yige Shen, Hao Jiang, Hua Qu +1
Despite their impressive capabilities, large language models (LLMs) often face challenges such as temporal misalignment and generating hallucinatory content. Enhancing LLMs with re…
Scene-Specific Pedestrian Detection Based on Parallel Vision
Wenwen Zhang, Kunfeng Wang, Hua Qu +2
As a special type of object detection, pedestrian detection in generic scenes has made a significant progress trained with large amounts of labeled training data manually. While th…
Diffusion Maximum Correntropy Criterion Algorithms for Robust Distributed Estimation
Wentao Ma, Badong Chen, Jiandong Duan +1
Robust diffusion adaptive estimation algorithms based on the maximum correntropy criterion (MCC), including adaptation to combination MCC and combination to adaptation MCC, are dev…
Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-Gaussian environments
Wentao Ma, Hua Qua, Guan Gui +3
Sparse adaptive channel estimation problem is one of the most important topics in broadband wireless communications systems due to its simplicity and robustness. So far many sparsi…
Sparsity Aware Normalized Least Mean p-power Algorithms with Correntropy Induced Metric Penalty
Wentao Ma, Hua Qu, Jihong Zhao +2
For identifying the non-Gaussian impulsive noise systems, normalized LMP (NLMP) has been proposed to combat impulsive-inducing instability. However, the standard algorithm is witho…