Inductive programming meets the real world S Gulwani, J Hernández-Orallo, E Kitzelmann, SH Muggleton, U Schmid, ... Communications of the ACM 58 (11), 90-99, 2015 | 219 | 2015 |
Inductive synthesis of functional programs: An explanation based generalization approach. E Kitzelmann, U Schmid, R Olsson, LP Kaelbling Journal of Machine Learning Research 7 (2), 2006 | 129 | 2006 |
Ultra-strong machine learning: comprehensibility of programs learned with ILP SH Muggleton, U Schmid, C Zeller, A Tamaddoni-Nezhad, T Besold Machine Learning 107, 1119-1140, 2018 | 127 | 2018 |
Metaphors and heuristic-driven theory projection (HDTP) H Gust, KU Kühnberger, U Schmid Theoretical Computer Science 354 (1), 98-117, 2006 | 105 | 2006 |
Computer models solving intelligence test problems: Progress and implications J Hernández-Orallo, F Martínez-Plumed, U Schmid, M Siebers, DL Dowe Artificial Intelligence 230, 74-107, 2016 | 100 | 2016 |
Automatic detection of pain from facial expressions: a survey T Hassan, D Seuß, J Wollenberg, K Weitz, M Kunz, S Lautenbacher, ... IEEE transactions on pattern analysis and machine intelligence 43 (6), 1815-1831, 2019 | 87 | 2019 |
Learning recursive control programs from problem solving. P Langley, D Choi, R Olsson, U Schmid Journal of Machine Learning Research 7 (3), 2006 | 84 | 2006 |
Inductive rule learning on the knowledge level U Schmid, E Kitzelmann Cognitive Systems Research 12 (3-4), 237-248, 2011 | 73 | 2011 |
Particle swarm optimization T Zeugmann, P Poupart, J Kennedy, X Jin, J Han, L Saitta, M Sebag, ... Encyclopedia of machine learning 1 (1), 760-766, 2011 | 72 | 2011 |
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 | 68 | 2020 |
The next generation of medical decision support: A roadmap toward transparent expert companions S Bruckert, B Finzel, U Schmid Frontiers in artificial intelligence 3, 507973, 2020 | 62 | 2020 |
Inductive synthesis of functional programs: universal planning, folding of finite programs, and schema abstraction by analogical reasoning U Schmid Springer Science & Business Media, 2003 | 61 | 2003 |
Deep-learned faces of pain and emotions: Elucidating the differences of facial expressions with the help of explainable AI methods K Weitz, T Hassan, U Schmid, JU Garbas tm-Technisches Messen 86 (7-8), 404-412, 2019 | 59 | 2019 |
The challenge of complexity for cognitive systems U Schmid, M Ragni, C Gonzalez, J Funke Cognitive Systems Research 12 (3-4), 211-218, 2011 | 59 | 2011 |
An introduction to inductive programming P Flener, U Schmid Artificial Intelligence Review 29, 45-62, 2008 | 59 | 2008 |
Induction of recursive program schemes U Schmid, F Wysotzki Machine Learning: ECML-98: 10th European Conference on Machine Learning …, 1998 | 53 | 1998 |
How does predicate invention affect human comprehensibility? U Schmid, C Zeller, T Besold, A Tamaddoni-Nezhad, S Muggleton Inductive Logic Programming: 26th International Conference, ILP 2016, London …, 2017 | 52 | 2017 |
Simulation-based planning of optimal conditions for industrial computed tomography S Reisinger, S Kasperl, M Franz, J Hiller, U Schmid International Symposium on Digital Industrial Radiology and Computed Tomography, 2011 | 49 | 2011 |
An algebraic framework for solving proportional and predictive analogies U Schmid, H Gust, KU Kühnberger, J Burghardt Proceedings of Eurocogsci 03, 295-300, 2019 | 43 | 2019 |
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 | 43 | 2018 |