65 citations · 75 across the 4 of their papers we have counts for
4 papers
A Comparative Study of Label-free Representation Quality Metrics in Deep Learning
Daniel Richards Arputharaj, Daniel Jönsson, Gabriel Eilertsen
We present a comparative study of label-free metrics for assessing the quality of representations in deep neural networks to understand their reliability under a wide variety of co…
Neural Activation Patterns (NAPs): Visual Explainability of Learned Concepts
Alex Bäuerle, Daniel Jönsson, Timo Ropinski
A key to deciphering the inner workings of neural networks is understanding what a model has learned. Promising methods for discovering learned features are based on analyzing acti…
Classifying the classifier: dissecting the weight space of neural networks
Gabriel Eilertsen, Daniel Jönsson, Timo Ropinski +2
This paper presents an empirical study on the weights of neural networks, where we interpret each model as a point in a high-dimensional space -- the neural weight space. To explor…
Inviwo -- A Visualization System with Usage Abstraction Levels
Daniel Jönsson, Peter Steneteg, Erik Sundén +6
The complexity of today's visualization applications demands specific visualization systems tailored for the development of these applications. Frequently, such systems utilize lev…