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FMM-Head: Enhancing Autoencoder-based ECG anomaly detection with prior knowledge
Detecting anomalies in electrocardiogram data is crucial to identifying deviations from normal heartbeat patterns and providing timely intervention to at-risk patients.
Fast Server Learning Rate Tuning for Coded Federated Dropout
In cross-device Federated Learning (FL), clients with low computational power train a common\linebreak[4] machine model by exchanging parameters via updates instead of potentially private data.
DeepGANTT: A Scalable Deep Learning Scheduler for Backscatter Networks
Without the need to retrain, DeepGANTT generalizes to networks 6x larger in the number of nodes and 10x larger in the number of tags than those used for training, breaking the scalability limitations of the optimal scheduler and reducing carrier utilization by up to 50% compared to the state-of-the-art heuristic.
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