2 research outputs found
Sampling-free Uncertainty Estimation in Gated Recurrent Units with Exponential Families
There has recently been a concerted effort to derive mechanisms in vision and
machine learning systems to offer uncertainty estimates of the predictions they
make. Clearly, there are enormous benefits to a system that is not only
accurate but also has a sense for when it is not sure. Existing proposals
center around Bayesian interpretations of modern deep architectures -- these
are effective but can often be computationally demanding. We show how classical
ideas in the literature on exponential families on probabilistic networks
provide an excellent starting point to derive uncertainty estimates in Gated
Recurrent Units (GRU). Our proposal directly quantifies uncertainty
deterministically, without the need for costly sampling-based estimation. We
demonstrate how our model can be used to quantitatively and qualitatively
measure uncertainty in unsupervised image sequence prediction. To our
knowledge, this is the first result describing sampling-free uncertainty
estimation for powerful sequential models such as GRUs.Comment: Version
Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation
We present a sampling-free approach for computing the epistemic uncertainty
of a neural network. Epistemic uncertainty is an important quantity for the
deployment of deep neural networks in safety-critical applications, since it
represents how much one can trust predictions on new data. Recently promising
works were proposed using noise injection combined with Monte-Carlo sampling at
inference time to estimate this quantity (e.g. Monte-Carlo dropout). Our main
contribution is an approximation of the epistemic uncertainty estimated by
these methods that does not require sampling, thus notably reducing the
computational overhead. We apply our approach to large-scale visual tasks
(i.e., semantic segmentation and depth regression) to demonstrate the
advantages of our method compared to sampling-based approaches in terms of
quality of the uncertainty estimates as well as of computational overhead.Comment: International Conference on Computer Vision 2019 (oral