| Enriching visual with verbal explanations for relational concepts–combining LIME with Aleph J Rabold, H Deininger, M Siebers, U Schmid Machine Learning and Knowledge Discovery in Databases: International …, 2020 | 63 | 2020 |
| Explaining black-box classifiers with ILP–empowering LIME with Aleph to approximate non-linear decisions with relational rules J Rabold, M Siebers, U Schmid Inductive Logic Programming: 28th International Conference, ILP 2018 …, 2018 | 41 | 2018 |
| Effect of superpixel aggregation on explanations in lime–a case study with biological data L Schallner, J Rabold, O Scholz, U Schmid Machine Learning and Knowledge Discovery in Databases: International …, 2020 | 31 | 2020 |
| Expressive explanations of DNNs by combining concept analysis with ILP J Rabold, G Schwalbe, U Schmid KI 2020: Advances in Artificial Intelligence: 43rd German Conference on AI …, 2020 | 21 | 2020 |
| Generating contrastive explanations for inductive logic programming based on a near miss approach J Rabold, M Siebers, U Schmid Machine Learning 111 (5), 1799-1820, 2022 | 16 | 2022 |
| A neural-symbolic approach for explanation generation based on sub-concept detection: an application of metric learning for low-time-budget labeling J Rabold KI-Künstliche Intelligenz 36 (3), 225-235, 2022 | 1 | 2022 |
| Enriching LIME with Inductive Logic Programming: Explaining Deep Learning Classifiers with Logic Rules in a Companion System Framework J Rabold Otto-Friedrich-Universität, 2022 | | 2022 |
| Angewandte Informatik Seminar KI: gestern, heute, morgen DE künstlicher neuronaler Netze | | 2016 |
| 5 Discussion groups 5.1 Neuro-symbolic integration R Evans, E Bingham, EL Mencia, H Ruess, J Rabold, K Ellis, U Schmid Approaches and Applications of Inductive Programming 107 (7), 83, 2016 | | 2016 |
