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A framework for benchmarking uncertainty in deep regression
Franko Schmähling, Jörg Martin, Clemens Elster
We propose a framework for the assessment of uncertainty quantification in deep regression. The framework is based on regression problems where the regression function is a linear…
Uncertainty Quantification by Ensemble Learning for Computational Optical Form Measurements
Lara Hoffmann, Ines Fortmeier, Clemens Elster
Uncertainty quantification by ensemble learning is explored in terms of an application from computational optical form measurements. The application requires to solve a large-scale…
Detecting unusual input to neural networks
Jörg Martin, Clemens Elster
Evaluating a neural network on an input that differs markedly from the training data might cause erratic and flawed predictions. We study a method that judges the unusualness of an…
Inspecting adversarial examples using the Fisher information
Jörg Martin, Clemens Elster
Adversarial examples are slight perturbations that are designed to fool artificial neural networks when fed as an input. In this work the usability of the Fisher information for th…