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Deep neural networks lack interpretability and tend to be overconfident, which poses a serious problem in safety-critical applications like autonomous driving, medical imaging, or machine vision tasks with high demands on reliability.
A Stochastic Approximation Method
Herbert Robbins and Sutton Monro · 1951
Earlier work this paper cites.
A Practical Bayesian Framework for Backpropagation Networks
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Earlier work this paper cites.
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