7 citations · 21 across the 17 of their papers we have counts for
5 papers · 1 filter
Extended target tracking utilizing machine-learning software -- with applications to animal classification
Magnus Malmström, Anton Kullberg, Isaac Skog +2
This paper considers the problem of detecting and tracking objects in a sequence of images. The problem is formulated in a filtering framework, using the output of object-detection…
Fusion framework and multimodality for the Laplacian approximation of Bayesian neural networks
Magnus Malmström, Isaac Skog, Daniel Axehill +1
This paper considers the problem of sequential fusion of predictions from neural networks (NN) and fusion of predictions from multiple NN. This fusion strategy increases the robust…
On the validity of using the delta method for calculating the uncertainty of the predictions from an overparameterized model
Magnus Malmström, Isaac Skog, Daniel Axehill +1
The uncertainty in the prediction calculated using the delta method for an overparameterized (parametric) black-box model is shown to be larger or equal to the uncertainty in the p…
Exact Worst-Case Execution-Time Analysis for Implicit Model Predictive Control
Daniel Arnström, David Broman, Daniel Axehill
We propose the first method that determines the exact worst-case execution time (WCET) for implicit linear model predictive control (MPC). Such WCET bounds are imperative when MPC…
Uncertainty quantification in neural network classifiers -- a local linear approach
Magnus Malmström, Isaac Skog, Daniel Axehill +1
Classifiers based on neural networks (NN) often lack a measure of uncertainty in the predicted class. We propose a method to estimate the probability mass function (PMF) of the dif…