1k citations · 1.7k across the 88 of their papers we have counts for
5 papers · 1 filter
A Robust Unsupervised Ensemble of Feature-Based Explanations using Restricted Boltzmann Machines
Vadim Borisov, Johannes Meier, Johan van den Heuvel +2
Understanding the results of deep neural networks is an essential step towards wider acceptance of deep learning algorithms. Many approaches address the issue of interpreting artif…
Deep Neural Networks and Tabular Data: A Survey
Vadim Borisov, Tobias Leemann, Kathrin Seßler +3
Heterogeneous tabular data are the most commonly used form of data and are essential for numerous critical and computationally demanding applications. On homogeneous data sets, dee…
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
Martin Pawelczyk, Sascha Bielawski, Johannes van den Heuvel +2
Counterfactual explanations provide means for prescriptive model explanations by suggesting actionable feature changes (e.g., increase income) that allow individuals to achieve fav…
TEyeD: Over 20 million real-world eye images with Pupil, Eyelid, and Iris 2D and 3D Segmentations, 2D and 3D Landmarks, 3D Eyeball, Gaze Vector, and Eye Movement Types
Wolfgang Fuhl, Gjergji Kasneci, Enkelejda Kasneci
We present TEyeD, the world's largest unified public data set of eye images taken with head-mounted devices. TEyeD was acquired with seven different head-mounted eye trackers. Amon…
On Baselines for Local Feature Attributions
Johannes Haug, Stefan Zürn, Peter El-Jiz +1
High-performing predictive models, such as neural nets, usually operate as black boxes, which raises serious concerns about their interpretability. Local feature attribution method…