How does node-based Bayesian neural network alignment compare to weight-based BNNs in reducing accuracy degrad

Abstract

Abstract: Bayesian neural networks (BNNs) promise improved generalization under covariate shift by providing principled probabilistic representations of epistemic uncertainty. However, weight-based BNNs often struggle with high computational complexity of large-scale architectures and datasets. Node-based BNNs have recently been introduced as scalable alternatives, which induce epistemic uncertainty by multiplying each hidden node with latent random variables, while learning a point-estimate of the weights. In this paper, we interpret these latent noise variables as implicit representations of simple an

Research Question

How does node-based Bayesian neural network alignment compare to weight-based BNNs in reducing accuracy degradation on out-of-distribution image classification benchmarks like CIFAR-10-C and ImageNet-C?

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