15 citations · 17 across the 3 of their papers we have counts for
5 papers
Interval Neural Networks as Instability Detectors for Image Reconstructions
Jan Macdonald, Maximilian März, Luis Oala +1
This work investigates the detection of instabilities that may occur when utilizing deep learning models for image reconstruction tasks. Although neural networks often empirically…
Interval Neural Networks: Uncertainty Scores
Luis Oala, Cosmas Heiß, Jan Macdonald +3
We propose a fast, non-Bayesian method for producing uncertainty scores in the output of pre-trained deep neural networks (DNNs) using a data-driven interval propagating network. T…
The Computational Complexity of Understanding Network Decisions
Stephan Wäldchen, Jan Macdonald, Sascha Hauch +1
For a Boolean function and an assignment to its variables we consider the problem of finding the subsets of the var…
A Rate-Distortion Framework for Explaining Neural Network Decisions
Jan Macdonald, Stephan Wäldchen, Sascha Hauch +1
We formalise the widespread idea of interpreting neural network decisions as an explicit optimisation problem in a rate-distortion framework. A set of input features is deemed rele…
The Oracle of DLphi
Dominik Alfke, Weston Baines, Jan Blechschmidt +24
We present a novel technique based on deep learning and set theory which yields exceptional classification and prediction results. Having access to a sufficiently large amount of l…