Bayesian Deep Learning | Uncertainty in Deep Learning
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Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Deep learning has gained tremendous attention in applied machine learning. However such tools for regression and classification do not capture model uncertainty. Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost. We show that dropout in neural networks (NNs) can be cast as a Bayesian approximation....
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Even though active learning forms an important pillar of machine learning, deep learning tools are not prevalent within it. Deep learning poses several difficulties when used in an active learning setting. First, we have to handle small amounts of data. Recent advances in deep learning, on the other hand, are notorious for their dependence on large amounts of data. Second, many acquisition func...
متن کاملWhat Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
There are two major types of uncertainty one can model. Aleatoric uncertainty captures noise inherent in the observations. On the other hand, epistemic uncertainty accounts for uncertainty in the model – uncertainty which can be explained away given enough data. Traditionally it has been difficult to model epistemic uncertainty in computer vision, but with new Bayesian deep learning tools this ...
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Motivated by the problem of computer-aided detection (CAD) of pulmonary nodules, we introduce methods to propagate and fuse uncertainty information in a multi-stage Bayesian convolutional neural network (CNN) architecture. The question we seek to answer is “can we take advantage of the model uncertainty provided by one deep learning model to improve the performance of the subsequent deep learni...
متن کاملUncertainty in Deep Learning
Deep learning has attracted tremendous attention from researchers in various fields of information engineering such as AI, computer vision, and language processing [Kalchbrenner and Blunsom, 2013; Krizhevsky et al., 2012; Mnih et al., 2013], but also from more traditional sciences such as physics, biology, and manufacturing [Anjos et al., 2015; Baldi et al., 2014; Bergmann et al., 2014]. Neural...
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