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Found 2 papers, 1 papers with code
Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty
In this paper, we propose a novel framework called Hyper-Evidential Neural Network (HENN) that explicitly models predictive uncertainty due to composite class labels in training data in the context of the belief theory called Subjective Logic (SL).
FedMS: Federated Learning with Mixture of Sparsely Activated Foundations Models
In this paper, we propose a novel two-stage federated learning algorithm called FedMS.
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