FedTGNN-SS delivers strong AUROC on GDM and related datasets even at 80% label missingness per site by combining local k-NN graphs, adaptive GNNs, prototype pseudo-labeling, and privacy-safe centroid sharing.
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SNNs deployed on Loihi 2 achieve real-time object detection with the lowest dynamic energy per inference and recover 87-100% of ANN accuracy via distillation-aware training.
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Federated Semi-Supervised Graph Neural Networks with Prototype-Guided Pseudo-Labeling for Privacy-Preserving Gestational Diabetes Mellitus Prediction
FedTGNN-SS delivers strong AUROC on GDM and related datasets even at 80% label missingness per site by combining local k-NN graphs, adaptive GNNs, prototype pseudo-labeling, and privacy-safe centroid sharing.
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Real-Time Frame- and Event-based Object Detection with Spiking Neural Networks on Edge Neuromorphic Hardware: Design, Deployment and Benchmark
SNNs deployed on Loihi 2 achieve real-time object detection with the lowest dynamic energy per inference and recover 87-100% of ANN accuracy via distillation-aware training.