4 papers
RiPPLE: Cross-Space Performance Prediction from Early Training for Neural Architecture Search
Yifan Yang, Zhaoyan Wang, Zheng Gao +2
Neural architecture search (NAS) evaluates candidate networks, but fully training enough architectures to rank an entire space is expensive. Zero-cost proxies score architectures a…
Are LLMs Better GNN Helpers? Rethinking Robust Graph Learning under Deficiencies with Iterative Refinement
Zhaoyan Wang, Zheng Gao, Arogya Kharel +1
Graph Neural Networks (GNNs) are widely adopted in Web-related applications, serving as a core technique for learning from graph-structured data, such as text-attributed graphs. Ye…
Beyond Interactions: Node-Level Graph Generation for Knowledge-Free Augmentation in Recommender Systems
Zhaoyan Wang, Hyunjun Ahn, In-Young Ko
Recent advances in recommender systems rely on external resources such as knowledge graphs or large language models to enhance recommendations, which limit applicability in real-wo…
Forecasting at Full Spectrum: Holistic Multi-Granular Traffic Modeling under High-Throughput Inference Regimes
Zhaoyan Wang, Xiangchi Song, In-Young Ko
Notably, current intelligent transportation systems rely heavily on accurate traffic forecasting and swift inference provision to make timely decisions. While Graph Convolutional N…