59 citations · 204 across the 12 of their papers we have counts for
4 papers · 1 filter
ParaDime: A Framework for Parametric Dimensionality Reduction
Andreas Hinterreiter, Christina Humer, Bernhard Kainz +1
ParaDime is a framework for parametric dimensionality reduction (DR). In parametric DR, neural networks are trained to embed high-dimensional data items in a low-dimensional space…
InstanceFlow: Visualizing the Evolution of Classifier Confusion on the Instance Level
Michael Pühringer, Andreas Hinterreiter, Marc Streit
Classification is one of the most important supervised machine learning tasks. During the training of a classification model, the training instances are fed to the model multiple t…
Projective Latent Interventions for Understanding and Fine-tuning Classifiers
Andreas Hinterreiter, Marc Streit, Bernhard Kainz
High-dimensional latent representations learned by neural network classifiers are notoriously hard to interpret. Especially in medical applications, model developers and domain exp…
ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion
Andreas Hinterreiter, Peter Ruch, Holger Stitz +4
Classifiers are among the most widely used supervised machine learning algorithms. Many classification models exist, and choosing the right one for a given task is difficult. Durin…