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Jun-Nan Li

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#graph neural networks Open access Oct 2026

Adaptive Modality Routing for Temporal Knowledge Graph Forecasting with Heterogeneous Mixture-of-Experts

Experiments indicate that the proposed H-MoE framework can improve forecasting performance under the evaluation setting and support heterogeneous expert specialization as a useful design direction, while the scope of the conclusions remains limited to the tested ICEWS benchmarks and implementation.

Fei Chen, Bing Guo, Yan Shen et al. · 0 citations
Jul 2026

A unified framework for failure and anomaly detection built on mask-level semantic segmentation

This work investigated intrinsic failures and anomalies in semantic segmentation and proposed a unified framework that manages these errors specifically for autonomous vehicles, and developed an anomaly fusion module that integrates data from both inlier and void masks, alongside pyramid features from the backbone, ult...

Jun-Nan Li, Wendian Luo, Zheng Luo et al. · 0 citations

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