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Noise-Contrastive Variational Information Bottleneck Networks
While deep neural networks for classification have shown impressive predictive performance, e. g. in image classification, they generally tend to be overconfident.
Sampling-free Variational Inference for Neural Networks with Multiplicative Activation Noise
Variational inference methods for BNNs approximate the intractable weight posterior with a tractable distribution, yet mostly rely on sampling from the variational distribution during training and inference.
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