Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks
with Sparse Gaussian Processes
- URL: http://arxiv.org/abs/2109.09690v2
- Date: Tue, 21 Sep 2021 15:27:37 GMT
- Title: Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks
with Sparse Gaussian Processes
- Authors: Jongseok Lee, Jianxiang Feng, Matthias Humt, Marcus G. M\"uller,
Rudolph Triebel
- Abstract summary: This paper presents a probabilistic framework to obtain both reliable and fast uncertainty estimates for predictions with Deep Neural Networks (DNNs)
We show the effectiveness of our approach in terms of predictive uncertainty, improved scalability, and run-time efficiency on a Jetson TX2.
- Score: 29.986386912503992
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper presents a probabilistic framework to obtain both reliable and
fast uncertainty estimates for predictions with Deep Neural Networks (DNNs).
Our main contribution is a practical and principled combination of DNNs with
sparse Gaussian Processes (GPs). We prove theoretically that DNNs can be seen
as a special case of sparse GPs, namely mixtures of GP experts (MoE-GP), and we
devise a learning algorithm that brings the derived theory into practice. In
experiments from two different robotic tasks -- inverse dynamics of a
manipulator and object detection on a micro-aerial vehicle (MAV) -- we show the
effectiveness of our approach in terms of predictive uncertainty, improved
scalability, and run-time efficiency on a Jetson TX2. We thus argue that our
approach can pave the way towards reliable and fast robot learning systems with
uncertainty awareness.
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