15 citations · 35 across the 5 of their papers we have counts for
5 papers · 1 filter
ActUp: Analyzing and Consolidating tSNE and UMAP
Andrew Draganov, Jakob Rødsgaard Jørgensen, Katrine Scheel Nellemann +4
tSNE and UMAP are popular dimensionality reduction algorithms due to their speed and interpretable low-dimensional embeddings. Despite their popularity, however, little work has be…
On Quantitative Evaluations of Counterfactuals
Frederik Hvilshøj, Alexandros Iosifidis, Ira Assent
As counterfactual examples become increasingly popular for explaining decisions of deep learning models, it is essential to understand what properties quantitative evaluation metri…
Learning by Design: Structuring and Documenting the Human Choices in Machine Learning Development
Simon Enni, Ira Assent
The influence of machine learning (ML) is quickly spreading, and a number of recent technological innovations have applied ML as a central technology. However, ML development still…
ECINN: Efficient Counterfactuals from Invertible Neural Networks
Frederik Hvilshøj, Alexandros Iosifidis, Ira Assent
Counterfactual examples identify how inputs can be altered to change the predicted class of a classifier, thus opening up the black-box nature of, e.g., deep neural networks. We pr…
Active Learning of SVDD Hyperparameter Values
Holger Trittenbach, Klemens Böhm, Ira Assent
Support Vector Data Description is a popular method for outlier detection. However, its usefulness largely depends on selecting good hyperparameter values -- a difficult problem th…