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Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representations
BPFL aims to quantify the uncertainty and heterogeneity within and across clients towards uncertainty representations by addressing the statistical heterogeneity of client data.
A Probabilistic Generative Model for Tracking Multi-Knowledge Concept Mastery Probability
To better model students' exercise responses, we proposed a logarithmic linear model with three interactive strategies, which models students' exercise responses by considering the relationship among students' knowledge status, knowledge concept, and problems.
Learning Rate Perturbation: A Generic Plugin of Learning Rate Schedule towards Flatter Local Minima
Learning rate is one of the most important hyper-parameters that has a significant influence on neural network training.
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