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cs.LG2026
When Model Merging Breaks Routing: Training-Free Calibration for MoE
Canbin Huang, Tianyuan Shi, Xiaojun Quan +3
Model merging has emerged as a cost-effective approach for consolidating the capabilities of multiple LLMs without retraining. However, existing merging techniques, largely based o…
cs.LG2022
Deep Partial Multiplex Network Embedding
Qifan Wang, Yi Fang, Anirudh Ravula +5
Network embedding is an effective technique to learn the low-dimensional representations of nodes in networks. Real-world networks are usually with multiplex or having multi-view r…