Xiao-Ming Wu

Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

Many interesting problems in machine learning are being revisited with new deep learning tools. For graph-based semi-supervised learning, a recent important development is graph convolutional networks (GCNs), which nicely integrate local vertex features and graph …

Benchmarking News Recommendation in the Era of Green AI

2024

Over recent years, news recommender systems have gained significant attention in both academia and industry, emphasizing the need for a standardized benchmark to evaluate and compare the performance of these systems. Concurrently, Green AI advocates …

AIMMerging: Adaptive Iterative Model Merging Using Training Trajectories for Language Model Continual Learning

2025 · arXiv (Cornell University)

Continual learning (CL) is essential for deploying large language models (LLMs) in dynamic real-world environments without the need for costly retraining. Recent model merging-based methods have attracted significant attention, but they still struggle to effectively …

Label Efficient Semi-Supervised Learning via Graph Filtering

2019

Graph-based methods have been demonstrated as one of the most effective approaches for semi-supervised learning, as they can exploit the connectivity patterns between labeled and unlabeled data samples to improve learning performance. However, existing graph-based …