Ute Schmid
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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
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
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
Metaphors and heuristic-driven theory projection (HDTP)
H Gust, KU Kühnberger, U Schmid
Theoretical Computer Science 354 (1), 98-117, 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
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
Learning recursive control programs from problem solving.
P Langley, D Choi, R Olsson, U Schmid
Journal of Machine Learning Research 7 (3), 2006
Inductive rule learning on the knowledge level
U Schmid, E Kitzelmann
Cognitive Systems Research 12 (3-4), 237-248, 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
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
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
Inductive synthesis of functional programs: universal planning, folding of finite programs, and schema abstraction by analogical reasoning
U Schmid
Springer Science & Business Media, 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
The challenge of complexity for cognitive systems
U Schmid, M Ragni, C Gonzalez, J Funke
Cognitive Systems Research 12 (3-4), 211-218, 2011
An introduction to inductive programming
P Flener, U Schmid
Artificial Intelligence Review 29, 45-62, 2008
Induction of recursive program schemes
U Schmid, F Wysotzki
Machine Learning: ECML-98: 10th European Conference on Machine Learning …, 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
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
An algebraic framework for solving proportional and predictive analogies
U Schmid, H Gust, KU Kühnberger, J Burghardt
Proceedings of Eurocogsci 03, 295-300, 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
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