Uncertainty propagation through deep neural networks

By Ahmed Hussen Abdelaziz, Shinji Watanabe, John R. Hershey, Emmanuel Vincent, Dorothea Kolossa. 2015

57 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Marco Ancona
2. Markus H. Gross
3. Cengiz Oztireli
4. Stefan Roth
5. Jochen Gast
6. K. Müller
7. Wojciech Samek
8. Su-In Lee

In Signal Processing

1. Stefan Roth

Citations in the corpus

• Lightweight Probabilistic Deep Networks (2018, background): “[1] apply sampling to propagate uncertainties in speech enhancement networks.”
• Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation (2019, background): “This problem has been previously studied in the scope of uncertainty propagation in DNNs (Abdelaziz et al., 2015; Gast & Roth, 2018; Thiagarajan et al., 2018), where the goal is to analyze how the uncertainty of the input data or of the network parameters propagates through the linear and non-linear”