3 papers
cs.LG2020
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…
cs.LG2019
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…
math.ST2019
The variation of the posterior variance and Bayesian sample size determination
Jörg Martin, Clemens Elster
We consider Bayesian sample size determination using a criterion that utilizes the first two moments of the expected posterior variance. We study the resulting sample size in depen…