Information Theoretical Estimators (ITE) Toolbox

By Zoltán Szabó. 2013

Subfields: Evaluation & Benchmarks

How researchers see this paper

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

In Evaluation & Benchmarks

1. Lars M. Mescheder
2. Sebastian Nowozin
3. Andreas Geiger
4. Raquel Urtasun
5. Sanja Fidler
6. Vladlen Koltun
7. Stefan Roth
8. Binh Yang

Citations in the corpus

• Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks (2017, methodology): “We evaluate all methods by estimating the KullbackLeibler-Divergence to the ground-truth data using the ITEpackage (Szabo, 2013) applied to 10000 samples from the ground-truth data and the respective approximation.”