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Found 2 papers, 2 papers with code
Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability
Bayesian inference allows expressing the uncertainty of posterior belief under a probabilistic model given prior information and the likelihood of the evidence.
FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings
Experimental results on several downstream tasks, over seven real-world data sets, show that FILDNE is able to reduce memory and computational time costs while providing competitive quality measure gains with respect to the contemporary methods for representation learning on dynamic graphs.
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