16 citations · 36 across the 6 of their papers we have counts for
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cs.LG2020★ 5 cited
In-Distribution Interpretability for Challenging Modalities
Cosmas Heiß, Ron Levie, Cinjon Resnick +2
It is widely recognized that the predictions of deep neural networks are difficult to parse relative to simpler approaches. However, the development of methods to investigate the m…
cs.LG2020★ 10 cited
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…