Toward Robust Uncertainty Estimation with Random Activation Functions

In this paper, we focus on ensemble UQ techniques, either Bayesian Recent advances in deep neural networks have demonstrated or non-Bayesian, as this group is less explored compared to remarkable performance in a wide variety of applications, the solely Bayesian techniques. An ensemble model aggregates ranging from recommendation systems and improving user the predictions of multiple individual base-learners (or experience to natural language processing and speech recognition ensemble members), which in our case are neural networks (Abiodun et al. 2018). Nevertheless, blindly relying (NNs), and the empirical variance of their predictions gives on the outcome of these models can have harmful effects, an approximate measure of uncertainty. The idea behind this especially in high-stake domains such as healthcare heuristic is highly intuitive: the more the base-learners disagree and autonomous driving, as models can provide inaccurate on the outcome, the more uncertain they are. Therefore, predictions when queried in out-of-distribution data the goal of ensemble members is to have a great level points (Amodei et al. 2016). Consequently, correctly quantifying of disagreement (variability) in the areas where little or no the uncertainty of models' predictions is an admissible data is available, and to have a high level of agreement in mechanism to distinguish where a model can or cannot regions with abundance of data (Pearce et al. 2018).