Publications (7)
Quality-Aware Robust Multi-View Clustering for Heterogeneous Observation Noise
Peihan Wu, Guanjie Cheng, Yufei Tong +2
The paper introduces QARMVC, a quality‑aware robust multi‑view clustering framework that estimates fine‑grained noise levels via reconstruction errors and uses instance‑level quali…
AST: Adaptive, Seamless, and Training-Free Precise Speech Editing
Sihan Lv, Yechen Jin, Zhen Li +5
Text-based speech editing aims to modify specific segments while preserving speaker identity and acoustic context. Current approaches generally involve either expensive task-specif…
E2PL: Effective and Efficient Prompt Learning for Incomplete Multi-view Multi-Label Class Incremental Learning
Jiajun Chen, Yue Wu, Kai Huang +6
Multi-view multi-label classification (MvMLC) is indispensable for modern web applications aggregating information from diverse sources. However, real-world web-scale settings are…
RIPRAG: Hack a Black-box Retrieval-Augmented Generation Question-Answering System with Reinforcement Learning
Meng Xi, Sihan Lv, Yechen Jin +4
Retrieval-Augmented Generation (RAG) systems based on Large Language Models (LLMs) have become a core technology for tasks such as question-answering (QA) and content generation. R…
TriSPrompt: A Hierarchical Soft Prompt Model for Multimodal Rumor Detection with Incomplete Modalities
Jiajun Chen, Yangyang Wu, Xiaoye Miao +2
The widespread presence of incomplete modalities in multimodal data poses a significant challenge to achieving accurate rumor detection. Existing multimodal rumor detection methods…
A Latent Feelings-aware RNN Model for User Churn Prediction with Behavioral Data
Meng Xi, Zhiling Luo, Naibo Wang +1
Predicting user churn and taking personalized measures to retain users is a set of common and effective practices for online game operators. However, different from the traditional…
Taming the Implicit: Dual-Channel Risk-Aware Reinforcement Fine-Tuning for Continual Multimodal Post-Training
Yibei Liu, Jiajun Chen, Qianle Zhang +4
Reinforcement fine-tuning (RFT) is widely believed to inherently resist catastrophic forgetting in continual post-training of multimodal large language models. Under pronounced tas…