Deep Uncertainty

What 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 ...

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....

Representing inferential uncertainty in deep neural networks through sampling

As deep neural networks (DNNs) are applied to increasingly challenging problems, they will need to be able to represent their own uncertainty. Modeling uncertainty is one of the key features of Bayesian methods. Scalable Bayesian DNNs that use dropout-based variational distributions have recently been proposed. Here we evaluate the ability of Bayesian DNNs trained with Bernoulli or Gaussian dis...

Propagating Uncertainty in Multi-Stage Bayesian Convolutional Neural Networks with Application to Pulmonary Nodule Detection

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...

Bayesian Deep Learning | Uncertainty in Deep Learning

Spatial Uncertainty Sampling for End to End Control

End-to-end trained neural networks (NNs) are a compelling approach to autonomous vehicle control because of their ability to learn complex tasks without manual engineering of rule-based decisions. However, challenging road conditions, ambiguous navigation situations, and safety considerations require reliable uncertainty estimation for the eventual adoption of full-scale autonomous vehicles. Ba...