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Found 9 papers, 1 papers with code
Estimating Model Uncertainty of Neural Network in Sparse Information Form
As a result, we show that the information form of MND can be scalably applied to represent model uncertainty in MND.
Combining Shape Completion and Grasp Prediction for Fast and Versatile Grasping with a Multi-Fingered Hand
We present a novel, fast, and high fidelity deep learning pipeline consisting of a shape completion module that is based on a single depth image, and followed by a grasp predictor that is based on the predicted object shape.
Shape Completion with Prediction of Uncertain Regions
We train on this dataset and test each method in shape completion and prediction of uncertain regions for known and novel object instances and on synthetic and real data.
Virtual Reality via Object Pose Estimation and Active Learning: Realizing Telepresence Robots with Aerial Manipulation Capabilities
This article presents a novel telepresence system for advancing aerial manipulation in dynamic and unstructured environments.
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes
This paper presents a probabilistic framework to obtain both reliable and fast uncertainty estimates for predictions with Deep Neural Networks (DNNs).
A Survey of Uncertainty in Deep Neural Networks
Different examples from the wide spectrum of challenges in different fields give an idea of the needs and challenges regarding uncertainties in practical applications.
Bayesian Optimization Meets Laplace Approximation for Robotic Introspection
In robotics, deep learning (DL) methods are used more and more widely, but their general inability to provide reliable confidence estimates will ultimately lead to fragile and unreliable systems.
Learning Multiplicative Interactions with Bayesian Neural Networks for Visual-Inertial Odometry
This paper presents an end-to-end multi-modal learning approach for monocular Visual-Inertial Odometry (VIO), which is specifically designed to exploit sensor complementarity in the light of sensor degradation scenarios.
Estimating Model Uncertainty of Neural Networks in Sparse Information Form
We present a sparse representation of model uncertainty for Deep Neural Networks (DNNs) where the parameter posterior is approximated with an inverse formulation of the Multivariate Normal Distribution (MND), also known as the information form.
