Subjective Logic

By Audun Jøsang. 2016

302 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Probabilistic & Bayesian Methods, Information Theory

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. Federico Cerutti
2. Lance M. Kaplan
3. Murat Sensoy
4. Melih Kandemir
5. Alun David Preece
6. Longo Luca
7. Giulia Vilone

In Probabilistic & Bayesian Methods

1. Federico Cerutti
2. Lance M. Kaplan
3. Murat Sensoy
4. Melih Kandemir
5. Alun David Preece

In Information Theory

1. Melih Kandemir
2. Federico Cerutti
3. Murat Sensoy
4. Lance M. Kaplan

Citations in the corpus

• Evidential Deep Learning to Quantify Classification Uncertainty (2018, methodology): “such as automated model selection and enhanced immunity to overfitting. In this paper, we put our full focus on the uncertainty estimation problem and approach it from a Theory of Evidence perspective [7, 14]. We interpret softmax, the standard output of a classification network, as the parameter set ”
• Uncertainty Aware AI ML: Why and How (2018, background): “Evidential Deep Learning (EDL) aims to extend classical deep learning with the ideas from evidential reasoning and subjective logic (Dempster 2008; Jøsang 2016) to quantify uncertainty in classification tasks (Sensoy, Kandemir, and Kaplan 2018).”