A factor-graph message-passing method for Bayesian neural networks that handles CNNs, avoids double-counting, and shows competitive accuracy with improved calibration on CIFAR-10.
Benchmarking uncertainty estimation methods for deep learning with safety-related metrics
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Approximate Message Passing for Bayesian Neural Networks
A factor-graph message-passing method for Bayesian neural networks that handles CNNs, avoids double-counting, and shows competitive accuracy with improved calibration on CIFAR-10.