ROAM routes region tokens to MoE experts via entropic optimal transport with per-slide capacity marginals and graph regularization, achieving competitive performance and external AUC 0.845 on NSCLC WSI benchmarks.
arXiv preprint arXiv:2505.00792 (2025) 10 X
2 Pith papers cite this work. Polarity classification is still indexing.
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MATE is a multi-modal MoE trajectory policy using a cosine router and stochastic noise to improve expert balance, reporting 4.75% higher average success rate than prior methods on LIBERO under data scarcity.
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Region-Graph Optimal Transport Routing for Mixture-of-Experts Whole-Slide Image Classification
ROAM routes region tokens to MoE experts via entropic optimal transport with per-slide capacity marginals and graph regularization, achieving competitive performance and external AUC 0.845 on NSCLC WSI benchmarks.
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Learning Multi-Modal Trajectory Policies for Data-Efficient Robotic Manipulation
MATE is a multi-modal MoE trajectory policy using a cosine router and stochastic noise to improve expert balance, reporting 4.75% higher average success rate than prior methods on LIBERO under data scarcity.