most citedLeveraging Prior Experience: An Expandable Auxiliary Knowledge Base for Text-to-SQL

1 citations · 1 across the 6 of their papers we have counts for

collaborators

10 papers

cs.LG2025

Fairness-Aware Graph Representation Learning with Limited Demographic Information

Zichong Wang, Zhipeng Yin, Liping Yang +4

Ensuring fairness in Graph Neural Networks is fundamental to promoting trustworthy and socially responsible machine learning systems. In response, numerous fair graph learning meth…

cs.CY2025

AI Fairness Beyond Complete Demographics: Current Achievements and Future Directions

Zichong Wang, Zhipeng Yin, Roland H. C. Yap +1

Fairness in artificial intelligence (AI) has become a growing concern due to discriminatory outcomes in AI-based decision-making systems. While various methods have been proposed t…

cs.CV2025

Generative AI in Depth: A Survey of Recent Advances, Model Variants, and Real-World Applications

Shamim Yazdani, Akansha Singh, Nripsuta Saxena +6

In recent years, deep learning based generative models, particularly Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models (DMs), have been…

cs.CL2025

Towards Transparent AI: A Survey on Explainable Language Models

Avash Palikhe, Zichong Wang, Zhipeng Yin +4

Language Models (LMs) have significantly advanced natural language processing and enabled remarkable progress across diverse domains, yet their black-box nature raises critical con…

cs.LG2025

AMCR: A Framework for Assessing and Mitigating Copyright Risks in Generative Models

Zhipeng Yin, Zichong Wang, Avash Palikhe +3

Generative models have achieved impressive results in text to image tasks, significantly advancing visual content creation. However, this progress comes at a cost, as such models r…

cs.LG2025

Bridging Neural Networks and Dynamic Time Warping for Adaptive Time Series Classification

Jintao Qu, Zichong Wang, Chenhao Wu +1

Neural networks have achieved remarkable success in time series classification, but their reliance on large amounts of labeled data for training limits their applicability in cold-…