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cs.AR2026
ESR-HGNN: Eliminating Semantic Redundancy for Efficient Mini-batch HGNN Inference
Dengke Han, Mingyu Yan, Duo Wang +3
Heterogeneous graph neural networks (HGNNs) are highly effective in processing heterogeneous graph data and have been widely adopted in critical domains. As real-world graph data c…
cs.LG2026
DraftExpert: Expansion-Aware Self-Speculative Decoding for End-Device MoE Inference
Dengke Han
Large Mixture-of-Experts (MoE) language models are attractive for end-device deployment because only a small subset of experts is active per token, but their routed expert weights…