ProMoS introduces the first unsupervised generalist graph anomaly detection method via prototype-based distillation from a self-supervised GNN teacher to a mixture-of-students model for zero-shot cross-graph transfer.
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MoS applies theme-aware routing to extract multi-scale theme-specific subsequences from noisy long user sequences, achieving state-of-the-art recommendation performance with fewer FLOPs than comparable MoE models.
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Generalist Graph Anomaly Detection via Prototype-Based Distillation
ProMoS introduces the first unsupervised generalist graph anomaly detection method via prototype-based distillation from a self-supervised GNN teacher to a mixture-of-students model for zero-shot cross-graph transfer.
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Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation
MoS applies theme-aware routing to extract multi-scale theme-specific subsequences from noisy long user sequences, achieving state-of-the-art recommendation performance with fewer FLOPs than comparable MoE models.