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ORCIDUte Schmid
Person information
- affiliation: University of Bamberg, Faculty Information Systems and Applied Computer Sciences, Germany
- affiliation: University of Osnabrück, Institute of Computer Science, Germany
- affiliation (PhD 1994): TU Berlin, Department of Computer Science, Germany
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2020 – today
- 2025
[j52]Daniel Losch, Steffen Jaschke, Tilman Michaeli, Simone Opel, Ute Schmid, Stefan Seegerer, Peer Stechert:
Was alle über Künstliche Intelligenz wissen sollen und wie KI-bezogene Kompetenzen in der Schule entwickelt werden können. Inform. Spektrum 48(1): 38-46 (2025)- [j51]Lukas Bahr
, Christoph Wehner, Judith Wewerka, José Bittencourt, Ute Schmid, Rüdiger Daub:
Knowledge graph enhanced retrieval-augmented generation for failure mode and effects analysis. J. Ind. Inf. Integr. 45: 100807 (2025) - [i35]Daniel Hallmann, Kerstin Jacob, Gerald Lüttgen, Ute Schmid, Rüdiger von der Weth:
USeR: A Web-based User Story eReviewer for Assisted Quality Optimizations. CoRR abs/2503.02049 (2025) - [i34]Yuliia Kaidashova, Bettina Finzel, Ute Schmid:
Toward Simple and Robust Contrastive Explanations for Image Classification by Leveraging Instance Similarity and Concept Relevance. CoRR abs/2506.23975 (2025) - 2024
- [j50]Martin Atzmueller, Johannes Fürnkranz, Tomás Kliegr, Ute Schmid:
Explainable and interpretable machine learning and data mining. Data Min. Knowl. Discov. 38(5): 2571-2595 (2024) - [j49]Jonas Troles, Ute Schmid, Wen Fan, Jiaojiao Tian:
BAMFORESTS: Bamberg Benchmark Forest Dataset of Individual Tree Crowns in Very-High-Resolution UAV Images. Remote. Sens. 16(11): 1935 (2024) - [c86]Daniel Gramelt, Timon Höfer, Ute Schmid:
Interactive Explainable Anomaly Detection for Industrial Settings. ECCV Workshops (4) 2024: 133-147 - [c85]Andreas Gilson, Mareike Weule, Jonathan Hansen, Lukas Meyer, Fabian Keil, Oliver Scholz, Annika Killer, Patrick Noack, Marc Stamminger, Ute Schmid:
CherryGraph: Encoding digital twins of cherry trees into a knowledge graph based on topology. GIL 2024: 71-82 - [c84]Bettina Finzel, Judith Knoblach, Anna Magdalena Thaler, Ute Schmid:
Near Hit and Near Miss Example Explanations for Model Revision in Binary Image Classification. IDEAL (2) 2024: 260-271 - [c83]Julio Wissing, Teresa Scholz, Stefan Saloman, Lidia Fargueta, Stephan Junger, Alessio Stefani, Wladimir Tschekalinskij, Stephan Scheele, Ute Schmid:
SPECTRE: A Dataset for Spectral Reconstruction on Chip-Size Spectrometers with a Physics-Informed Augmentation Method. IEEE SENSORS 2024: 1-4 - [c82]Emanuel Slany, Stephan Scheele, Ute Schmid:
Hybrid Explanatory Interactive Machine Learning for Medical Diagnosis. AIAI (1) 2024: 105-116 - [c81]Lukas Meyer, Andreas Gilson, Ute Schmid, Marc Stamminger:
FruitNeRF: A Unified Neural Radiance Field based Fruit Counting Framework. IROS 2024: 1-8 - [c80]Emanuel Slany, Stephan Scheele, Ute Schmid:
Explanatory Interactive Machine Learning with Counterexamples from Constrained Large Language Models. KI 2024: 324-331 - [c79]Jonas Amling, Stephan Scheele, Emanuel Slany, Moritz Lang, Ute Schmid:
Explainable AI for Mixed Data Clustering. xAI (2) 2024: 42-62 - [c78]Franz Motzkus, Georgii Mikriukov, Christian Hellert, Ute Schmid:
Locally Testing Model Detections for Semantic Global Concepts. xAI (1) 2024: 137-159 - [p10]Ulrich Furbach, Ute Schmid:
Einführung. Künstliche Intelligenz für Lehrkräfte 2024: 1-5 - [p9]Ute Schmid:
Suche im Problemraum. Künstliche Intelligenz für Lehrkräfte 2024: 9-23 - [p8]Ute Schmid:
Erklärbarkeit. Künstliche Intelligenz für Lehrkräfte 2024: 117-124 - [p7]Johannes Langer, Ute Schmid:
Generative KI. Künstliche Intelligenz für Lehrkräfte 2024: 125-136 - [p6]Ute Schmid:
Natürliche und Künstliche Intelligenz. Künstliche Intelligenz für Lehrkräfte 2024: 197-204 - [e11]Ulrich Furbach, Emanuel Kitzelmann, Tilman Michaeli, Ute Schmid:
Künstliche Intelligenz für Lehrkräfte: Eine fachliche Einführung mit didaktischen Hinweisen. ars digitalis, Springer Fachmedien Wiesbaden 2024, ISBN 978-3-658-44248-4 [contents] - [e10]Slawomir Nowaczyk, Przemyslaw Biecek, Neo Christopher Chung, Mauro Vallati, Pawel Skruch, Joanna Jaworek-Korjakowska, Simon Parkinson, Alexandros Nikitas, Martin Atzmüller, Tomás Kliegr, Ute Schmid, Szymon Bobek, Nada Lavrac, Marieke Peeters, Roland van Dierendonck, Saskia Robben, Eunika Mercier-Laurent, Gülgün Kayakutlu, Mieczyslaw Lech Owoc, Karl Mason, Abdul Wahid, Pierangela Bruno, Francesco Calimeri, Francesco Cauteruccio, Giorgio Terracina, Diedrich Wolter, Jochen L. Leidner, Michael Kohlhase, Vania Dimitrova:
Artificial Intelligence. ECAI 2023 International Workshops - XAI³, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30 - October 4, 2023, Proceedings, Part I. Communications in Computer and Information Science 1947, Springer 2024, ISBN 978-3-031-50395-5 [contents] - [e9]Slawomir Nowaczyk, Przemyslaw Biecek, Neo Christopher Chung, Mauro Vallati, Pawel Skruch, Joanna Jaworek-Korjakowska, Simon Parkinson, Alexandros Nikitas, Martin Atzmüller, Tomás Kliegr, Ute Schmid, Szymon Bobek, Nada Lavrac, Marieke Peeters, Roland van Dierendonck, Saskia Robben, Eunika Mercier-Laurent, Gülgün Kayakutlu, Mieczyslaw Lech Owoc, Karl Mason, Abdul Wahid, Pierangela Bruno, Francesco Calimeri, Francesco Cauteruccio, Giorgio Terracina, Diedrich Wolter, Jochen L. Leidner, Michael Kohlhase, Vania Dimitrova:
Artificial Intelligence. ECAI 2023 International Workshops - XAI³, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30 - October 4, 2023, Proceedings, Part II. Communications in Computer and Information Science 1948, Springer 2024, ISBN 978-3-031-50484-6 [contents] - [d2]Lukas Meyer, Andreas Gilson, Ute Schmid, Marc Stamminger:
FruitNeRF: A Unified Neural Radiance Field based Fruit Counting Framework. Zenodo, 2024
- [d1]Julio Wissing, Teresa Scholz, Stefan Saloman, Lidia Fargueta, Stephan Junger, Alessio Stefani, Wladimir Tschekalinskij, Stephan Scheele, Ute Schmid, Fraunhofer Institute for Integrated Circuits:
SPECTRE Dataset. Zenodo, 2024 - [i33]Simon Schramm, Christoph Wehner, Ute Schmid:
Comprehensible Artificial Intelligence on Knowledge Graphs: A survey. CoRR abs/2404.03499 (2024) - [i32]Céline Hocquette, Johannes Langer, Andrew Cropper, Ute Schmid:
Can humans teach machines to code? CoRR abs/2404.19397 (2024) - [i31]Bettina Finzel, Patrick Hilme, Johannes Rabold, Ute Schmid:
When a Relation Tells More Than a Concept: Exploring and Evaluating Classifier Decisions with CoReX. CoRR abs/2405.01661 (2024) - [i30]Franz Motzkus, Georgii Mikriukov, Christian Hellert, Ute Schmid:
Locally Testing Model Detections for Semantic Global Concepts. CoRR abs/2405.17523 (2024) - [i29]Franz Motzkus, Christian Hellert, Ute Schmid:
CoLa-DCE - Concept-guided Latent Diffusion Counterfactual Explanations. CoRR abs/2406.01649 (2024) - [i28]Lukas Bahr, Christoph Wehner, Judith Wewerka, José Bittencourt, Ute Schmid, Rüdiger Daub:
Knowledge Graph Enhanced Retrieval-Augmented Generation for Failure Mode and Effects Analysis. CoRR abs/2406.18114 (2024) - [i27]Lukas Meyer, Andreas Gilson, Ute Schmid, Marc Stamminger:
FruitNeRF: A Unified Neural Radiance Field based Fruit Counting Framework. CoRR abs/2408.06190 (2024) - [i26]Daniel Gramelt, Timon Höfer, Ute Schmid:
Interactive Explainable Anomaly Detection for Industrial Settings. CoRR abs/2410.12817 (2024) - [i25]Filip Ilievski, Barbara Hammer, Frank van Harmelen, Benjamin Paassen, Sascha Saralajew, Ute Schmid, Michael Biehl, Marianna Bolognesi, Xin Luna Dong, Kiril Gashteovski, Pascal Hitzler, Giuseppe Marra, Pasquale Minervini, Martin Mundt, Axel-Cyrille Ngonga Ngomo, Alessandro Oltramari, Gabriella Pasi, Zeynep G. Saribatur, Luciano Serafini, John Shawe-Taylor, Vered Shwartz, Gabriella Skitalinskaya, Clemens Stachl, Gido M. van de Ven, Thomas Villmann:
Aligning Generalisation Between Humans and Machines. CoRR abs/2411.15626 (2024) - 2023
- [j48]Louisa Heidrich, Emanuel Slany, Stephan Scheele, Ute Schmid:
FairCaipi: A Combination of Explanatory Interactive and Fair Machine Learning for Human and Machine Bias Reduction. Mach. Learn. Knowl. Extr. 5(4): 1519-1538 (2023) - [j47]Lun Ai, Johannes Langer, Stephen H. Muggleton, Ute Schmid:
Explanatory machine learning for sequential human teaching. Mach. Learn. 112(10): 3591-3632 (2023) - [j46]Andreas Holzinger, Anna Saranti, Alessa Angerschmid, Bettina Finzel, Ute Schmid, Heimo Müller:
Toward human-level concept learning: Pattern benchmarking for AI algorithms. Patterns 4(8): 100788 (2023) - [j45]Joscha Eirich, Dominik Jäckle, Michael Sedlmair, Christoph Wehner, Ute Schmid, Jürgen Bernard, Tobias Schreck:
ManuKnowVis: How to Support Different User Groups in Contextualizing and Leveraging Knowledge Repositories. IEEE Trans. Vis. Comput. Graph. 29(8): 3441-3457 (2023) - [j44]Simon Schramm, Christoph Wehner, Ute Schmid:
Comprehensible Artificial Intelligence on Knowledge Graphs: A survey. J. Web Semant. 79: 100806 (2023) - [c77]Bettina Finzel, Ines Rieger, Simon Kuhn, Ute Schmid:
Domain-Specific Evaluation of Visual Explanations for Application-Grounded Facial Expression Recognition. CD-MAKE 2023: 31-44 - [c76]Emanuel Slany, Stephan Scheele, Ute Schmid:
Bayesian CAIPI: A Probabilistic Approach to Explanatory and Interactive Machine Learning. ECAI Workshops (1) 2023: 285-301 - [c75]Simon Schramm, Ute Schmid:
Inductive Logic Programming for Explainable Graph Clustering. ICKG 2023: 235-242 - [c74]Jonas Troles, Richard Nieding, Sonia Simons, Ute Schmid:
Task Planning Support for Arborists and Foresters: Comparing Deep Learning Approaches for Tree Inventory and Tree Vitality Assessment Based on UAV-Data. I4CS 2023: 103-122 - [c73]Simon Schramm, Ulrich Niklas, Ute Schmid:
Cluster Robust Inference for Embedding-Based Knowledge Graph Completion. KSEM (1) 2023: 284-299 - [c72]Alisa Véronique Münsterberg, Reinhard Budde, Ute Schmid, Thorsten Leimbach:
Perzeptrons programmieren und explorieren im Rahmen der Open Roberta Lernumgebung. INFOS 2023: 435-436 - [i24]Durgesh Nandini, Ute Schmid:
Explaining Hate Speech Classification with Model Agnostic Methods. CoRR abs/2306.00021 (2023) - [i23]Jonas-Dario Troles, Richard Nieding, Sonia Simons, Ute Schmid:
Task Planning Support for Arborists and Foresters: Comparing Deep Learning Approaches for Tree Inventory and Tree Vitality Assessment Based on UAV-Data. CoRR abs/2307.01651 (2023) - [i22]Bettina Finzel, Simon P. Kuhn, David E. Tafler, Ute Schmid:
Explaining with Attribute-based and Relational Near Misses: An Interpretable Approach to Distinguishing Facial Expressions of Pain and Disgust. CoRR abs/2308.14163 (2023) - [i21]Luc De Raedt, Ute Schmid, Johannes Langer:
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 23442). Dagstuhl Reports 13(10): 182-211 (2023) - 2022
- [j43]Kyra Gobel, Cornelia Niessen, Sebastian Seufert, Ute Schmid:
Explanatory machine learning for justified trust in human-AI collaboration: Experiments on file deletion recommendations. Frontiers Artif. Intell. 5 (2022) - [j42]Ute Schmid, Britta Wrede:
Explainable AI. Künstliche Intell. 36(3): 207-210 (2022) - [j41]Ute Schmid, Britta Wrede:
What is Missing in XAI So Far? Künstliche Intell. 36(3): 303-315 (2022) - [j40]Ute Schmid:
Constructing Explainability - Interdisciplinary Framework to Actively Shape Explanations in XAI. Künstliche Intell. 36(3): 327-331 (2022) - [j39]Sebastian Kiefer, Mareike Hoffmann, Ute Schmid:
Semantic Interactive Learning for Text Classification: A Constructive Approach for Contextual Interactions. Mach. Learn. Knowl. Extr. 4(4): 994-1010 (2022) - [j38]Johannes Rabold, Michael Siebers, Ute Schmid:
Generating contrastive explanations for inductive logic programming based on a near miss approach. Mach. Learn. 111(5): 1799-1820 (2022) - [j37]Joscha Eirich, Jakob Bonart, Dominik Jäckle, Michael Sedlmair, Ute Schmid, Kai Fischbach, Tobias Schreck, Jürgen Bernard:
IRVINE: A Design Study on Analyzing Correlation Patterns of Electrical Engines. IEEE Trans. Vis. Comput. Graph. 28(1): 11-21 (2022) - [c71]Dennis Müller, Michael März, Stephan Scheele, Ute Schmid:
An Interactive Explanatory AI System for Industrial Quality Control. AAAI 2022: 12580-12586 - [c70]Kolja Kühnlenz, Ute Schmid, Barbara Kühnlenz:
A Video-based Study on Perceived Intelligence, Subjective Performance and Trust under Variation of Prior Information given to Users in Autonomous Driving. ARSO 2022: 1-4 - [c69]Julio Wissing, Stephan Scheele, Aliya Mohammed, Dorothea Kolossa, Ute Schmid:
HiMLEdge - Energy-Aware Optimization for Hierarchical Machine Learning. ARTIIS (2) 2022: 15-29 - [c68]Jaspar Pahl, Ines Rieger, Anna Möller, Thomas Wittenberg, Ute Schmid:
Female, white, 27? Bias Evaluation on Data and Algorithms for Affect Recognition in Faces. FAccT 2022: 973-987 - [c67]Ines Rieger, Jaspar Pahl, Bettina Finzel, Ute Schmid:
CorrLoss: Integrating Co-Occurrence Domain Knowledge for Affect Recognition. ICPR 2022: 798-804 - [c66]Marvin Herchenbach, Dennis Müller, Stephan Scheele, Ute Schmid:
Explaining Image Classifications with Near Misses, Near Hits and Prototypes - Supporting Domain Experts in Understanding Decision Boundaries. ICPRAI (2) 2022: 419-430 - [c65]Christoph Wehner, Francis Powlesland, Bashar Altakrouri, Ute Schmid:
Explainable Online Lane Change Predictions on a Digital Twin with a Layer Normalized LSTM and Layer-wise Relevance Propagation. IEA/AIE 2022: 621-632 - [c64]Emanuel Slany, Yannik Ott, Stephan Scheele, Jan Paulus, Ute Schmid:
CAIPI in Practice: Towards Explainable Interactive Medical Image Classification. AIAI (Workshops) 2022: 389-400 - [c63]Bettina Finzel, Simon P. Kuhn, David E. Tafler, Ute Schmid:
Explaining with Attribute-Based and Relational Near Misses: An Interpretable Approach to Distinguishing Facial Expressions of Pain and Disgust. ILP 2022: 40-51 - [c62]Durgesh Nandini, Ute Schmid:
Explaining Hate Speech Classification with Model-Agnostic Methods. KI (Workshops) 2022 - [p5]Ute Schmid:
Interactive Learning with Mutual Explanations in Relational Domains. Human-Like Machine Intelligence 2022: 338-354 - [i20]Gesina Schwalbe, Christian Wirth, Ute Schmid:
Concept Embeddings for Fuzzy Logic Verification of Deep Neural Networks in Perception Tasks. CoRR abs/2201.00572 (2022) - [i19]Dennis Müller, Michael März, Stephan Scheele, Ute Schmid:
An Interactive Explanatory AI System for Industrial Quality Control. CoRR abs/2203.09181 (2022) - [i18]Christoph Wehner, Francis Powlesland, Bashar Altakrouri, Ute Schmid:
Explainable Online Lane Change Predictions on a Digital Twin with a Layer Normalized LSTM and Layer-wise Relevance Propagation. CoRR abs/2204.01292 (2022) - [i17]Emanuel Slany, Yannik Ott, Stephan Scheele, Jan Paulus, Ute Schmid:
CAIPI in Practice: Towards Explainable Interactive Medical Image Classification. CoRR abs/2204.02661 (2022) - [i16]Lun Ai, Johannes Langer, Stephen H. Muggleton, Ute Schmid:
Explanatory machine learning for sequential human teaching. CoRR abs/2205.10250 (2022) - [i15]Ines Rieger, Jaspar Pahl, Bettina Finzel, Ute Schmid:
CorrLoss: Integrating Co-Occurrence Domain Knowledge for Affect Recognition. CoRR abs/2210.17233 (2022) - 2021
- [j36]Lun Ai, Stephen H. Muggleton, Céline Hocquette, Mark Gromowski, Ute Schmid:
Beneficial and harmful explanatory machine learning. Mach. Learn. 110(4): 695-721 (2021) - [j35]Teena Hassan, Dominik Seuß, Johannes Wollenberg, Katharina Weitz, Miriam Kunz, Stefan Lautenbacher, Jens-Uwe Garbas, Ute Schmid:
Automatic Detection of Pain from Facial Expressions: A Survey. IEEE Trans. Pattern Anal. Mach. Intell. 43(6): 1815-1831 (2021) - [c61]Bettina Finzel, David Elias Tafler, Anna Magdalena Thaler, Ute Schmid:
Multimodal Explanations for User-centric Medical Decision Support Systems. HUMAN@AAAI Fall Symposium 2021 - [c60]Anna Magdalena Thaler, Ute Schmid:
Explaining Machine Learned Relational Concepts in Visual Domains - Effects of Perceived Accuracy on Joint Performance and Trust. CogSci 2021 - [c59]Deniz Neufeld, Ute Schmid:
Anomaly Detection for Hydraulic Systems under Test. ETFA 2021: 1-8 - [c58]Ute Schmid, Anja Gärtig-Daugs, Linda Müller, Alexander Werner:
Grundkonzepte des Maschinellen Lernens für die Grundschule - Algorithmen, Biases, Generalisierungsfehler. GI-Jahrestagung 2021: 1611-1623 - [c57]Sascha Lang, Valentin Plenk, Ute Schmid:
A Case-Based Reasoning Approach for a Decision Support System in Manufacturing. IEA/AIE (2) 2021: 265-271 - [c56]Bettina Finzel, David E. Tafler, Stephan Scheele, Ute Schmid:
Explanation as a Process: User-Centric Construction of Multi-level and Multi-modal Explanations. KI 2021: 80-94 - [c55]Bettina Finzel, René Kollmann, Ines Rieger, Jaspar Pahl, Ute Schmid:
Deriving Temporal Prototypes from Saliency Map Clusters for the Analysis of Deep-Learning-based Facial Action Unit Classification. LWDA 2021: 86-97 - [c54]Jonas-Dario Troles, Ute Schmid:
Extending Challenge Sets to Uncover Gender Bias in Machine Translation: Impact of Stereotypical Verbs and Adjectives. WMT@EMNLP 2021: 531-541 - [e8]Martin Atzmüller, Tomás Kliegr, Ute Schmid:
Proceedings of the First International Workshop on Explainable and Interpretable Machine Learning (XI-ML 2020) co-located with the 43rd German Conference on Artificial Intelligence (KI 2020), Bamberg, Germany, September 21, 2020 (Virtual Workshop). CEUR Workshop Proceedings 2796, CEUR-WS.org 2021 [contents] - [i14]Johannes Rabold, Gesina Schwalbe, Ute Schmid:
Expressive Explanations of DNNs by Combining Concept Analysis with ILP. CoRR abs/2105.07371 (2021) - [i13]Johannes Rabold, Michael Siebers, Ute Schmid:
Generating Contrastive Explanations for Inductive Logic Programming Based on a Near Miss Approach. CoRR abs/2106.08064 (2021) - [i12]Jonas-Dario Troles, Ute Schmid:
Extending Challenge Sets to Uncover Gender Bias in Machine Translation: Impact of Stereotypical Verbs and Adjectives. CoRR abs/2107.11584 (2021) - [i11]Bettina Finzel, David E. Tafler, Stephan Scheele, Ute Schmid:
Explanation as a process: user-centric construction of multi-level and multi-modal explanations. CoRR abs/2110.03759 (2021) - [i10]Andrew Cropper, Luc De Raedt, Richard Evans, Ute Schmid:
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 21192). Dagstuhl Reports 11(4): 20-33 (2021) - 2020
- [j34]Mark Gromowski, Michael Siebers, Ute Schmid:
A process framework for inducing and explaining Datalog theories. Adv. Data Anal. Classif. 14(4): 821-835 (2020) - [j33]Sebastian Bruckert, Bettina Finzel, Ute Schmid:
The Next Generation of Medical Decision Support: A Roadmap Toward Transparent Expert Companions. Frontiers Artif. Intell. 3: 507973 (2020) - [j32]Ute Schmid, Bettina Finzel:
Mutual Explanations for Cooperative Decision Making in Medicine. Künstliche Intell. 34(2): 227-233 (2020) - [c53]Ines Rieger, Rene Kollmann, Bettina Finzel, Dominik Seuss, Ute Schmid:
Verifying Deep Learning-based Decisions for Facial Expression Recognition. ESANN 2020: 139-144 - [c52]Ute Schmid, Volker Tresp, Matthias Bethge, Kristian Kersting, Rainer Stiefelhagen:
Künstliche Intelligenz - Die dritte Welle. GI-Jahrestagung 2020: 91-95 - [c51]Johannes Rabold, Gesina Schwalbe, Ute Schmid:
Expressive Explanations of DNNs by Combining Concept Analysis with ILP. KI 2020: 148-162 - [c50]Ute Schmid:
AI goes to school: learning about and learning with artificial intelligence. WiPSCE 2020: 2:1 - [p4]Günther Görz, Tanya Braun, Ute Schmid:
Einleitung. Handbuch der Künstlichen Intelligenz 2020: 1-26 - [e7]Günther Görz, Ute Schmid, Tanya Braun:
Handbuch der Künstlichen Intelligenz, 6. Auflage. De Gruyter 2020, ISBN 9783110659948 [contents] - [e6]Ute Schmid, Franziska Klügl, Diedrich Wolter:
KI 2020: Advances in Artificial Intelligence - 43rd German Conference on AI, Bamberg, Germany, September 21-25, 2020, Proceedings. Lecture Notes in Computer Science 12325, Springer 2020, ISBN 978-3-030-58284-5 [contents] - [i9]Ines Rieger, Rene Kollmann, Bettina Finzel, Dominik Seuss, Ute Schmid:
Verifying Deep Learning-based Decisions for Facial Expression Recognition. CoRR abs/2003.00828 (2020) - [i8]Lun Ai, Stephen H. Muggleton, Céline Hocquette, Mark Gromowski, Ute Schmid:
Beneficial and Harmful Explanatory Machine Learning. CoRR abs/2009.06410 (2020)
2010 – 2019
- 2019
- [j31]Michael Siebers, Ute Schmid:
Please delete that! Why should I? - Explaining learned irrelevance classifications of digital objects. Künstliche Intell. 33(1): 35-44 (2019) - [c49]Ludwig Schallner, Johannes Rabold, Oliver Scholz, Ute Schmid:
Effect of Superpixel Aggregation on Explanations in LIME - A Case Study with Biological Data. PKDD/ECML Workshops (1) 2019: 147-158 - [c48]Johannes Rabold, Hannah Deininger, Michael Siebers, Ute Schmid:
Enriching Visual with Verbal Explanations for Relational Concepts - Combining LIME with Aleph. PKDD/ECML Workshops (1) 2019: 180-192 - [c47]Anja Gärtig-Daugs, Alexander Werner, Ute Schmid:
"Wie funktioniert das?" - Informatische Konzepte in der Vor- und Grundschule spielerisch begreifen und anwenden. INFOS 2019: 377 - [i7]Johannes Rabold, Hannah Deininger, Michael Siebers, Ute Schmid:
Enriching Visual with Verbal Explanations for Relational Concepts - Combining LIME with Aleph. CoRR abs/1910.01837 (2019) - [i6]Ludwig Schallner, Johannes Rabold, Oliver Scholz, Ute Schmid:
Effect of Superpixel Aggregation on Explanations in LIME - A Case Study with Biological Data. CoRR abs/1910.07856 (2019) - [i5]Luc De Raedt, Richard Evans, Stephen H. Muggleton, Ute Schmid:
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 19202). Dagstuhl Reports 9(5): 58-88 (2019) - 2018
- [j30]Ute Schmid, Katharina Weitz, Anja Gärtig-Daugs:
Informatik in der Grundschule - Eine informatisch-pädagogische Perspektive auf informatikdidaktische Konzepte. Inform. Spektrum 41(3): 200-207 (2018) - [j29]Stephen H. Muggleton, Ute Schmid, Christina Zeller, Alireza Tamaddoni-Nezhad, Tarek R. Besold:
Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP. Mach. Learn. 107(7): 1119-1140 (2018) - [c46]Bettina Finzel, Hannah Deininger, Ute Schmid:
From beliefs to intention: mentoring as an approach to motivate female high school students to enrol in computer science studies. GenderIT 2018: 251-260 - [c45]Johannes Rabold, Michael Siebers, Ute Schmid:
Explaining Black-Box Classifiers with ILP - Empowering LIME with Aleph to Approximate Non-linear Decisions with Relational Rules. ILP 2018: 105-117 - [c44]Michael Siebers, Ute Schmid:
Was the Year 2000 a Leap Year? Step-Wise Narrowing Theories with Metagol. ILP 2018: 141-156 - [c43]Ute Schmid:
Inductive Programming as Approach to Comprehensible Machine Learning. DKB/KIK@KI 2018: 4-12 - [c42]Ingo J. Timm, Steffen Staab, Michael Siebers, Claudia Schon, Ute Schmid, Kai Sauerwald, Lukas Reuter, Marco Ragni, Claudia Niederée, Heiko Maus, Gabriele Kern-Isberner, Christian Jilek, Paulina Friemann, Thomas Eiter, Andreas Dengel, Hannah Dames, Tanja Bock, Jan Ole Berndt, Christoph Beierle:
Intentional Forgetting in Artificial Intelligence Systems: Perspectives and Challenges. KI 2018: 357-365 - 2017
- [j28]Aboubakr Benabbas, Golnaz Elmamooz, Brent Lagesse, Daniela Nicklas, Ute Schmid:
Living Lab Bamberg: an infrastructure to explore smart city research challenges in the wild. Künstliche Intell. 31(3): 265-271 (2017) - [c41]Frederick Birnbaum, Christian Moewes, Daniela Nicklas, Ute Schmid:
Data Mining von multidimensionalen Qualitätsdaten aus einer computerintegrierten industriellen Fertigung zur visuellen Analyse von komplexen Wirkzusammenhängen. BTW (Workshops) 2017: 139-142 - [c40]Christina Zeller, Ute Schmid:
The Impact of Presentation Order on Category Learning Strategies: Behavioral Data and Self-Reports. CogSci 2017 - [c39]Michael Siebers, Kyra Gobel, Cornelia Niessen, Ute Schmid:
Requirements for a companion system to support identifying irrelevancy. ICCT 2017: 1-2 - [c38]José Hernández-Orallo, Fernando Martínez-Plumed, Ute Schmid, Michael Siebers, David L. Dowe:
Computer Models Solving Intelligence Test Problems: Progress and Implications (Extended Abstract). IJCAI 2017: 5005-5009 - [c37]Christina Zeller, Ute Schmid:
A Human Like Incremental Decision Tree Algorithm: Combining Rule Learning, Pattern Induction, and Storing Examples. LWDA 2017: 64 - [c36]Anja Gärtig-Daugs, Katharina Weitz, Ute Schmid:
Kindliche Modelle der digitalen Welt. INFOS 2017: 419-420 - [c35]Katharina Weitz, Anja Gärtig-Daugs, Daniel Knauf, Ute Schmid:
Computer Science in Early Childhood Education: Pedagogical Beliefs and Perceived Self-Confidence in Preschool Teachers. WiPSCE 2017: 117-118 - [c34]Maike Wolking, Ute Schmid:
Mental Models, Career Aspirations, and the Acquirement of Basic Concepts of Computer Science in Elementary Education: Empirical Evaluation of the Computer Science Experimenter's Kit. WiPSCE 2017: 119-120 - [r6]Pierre Flener, Ute Schmid:
Inductive Programming. Encyclopedia of Machine Learning and Data Mining 2017: 658-666 - [r5]Pierre Flener, Ute Schmid:
Programming by Demonstration. Encyclopedia of Machine Learning and Data Mining 2017: 1017-1018 - [r4]Pierre Flener, Ute Schmid:
Trace-Based Programming. Encyclopedia of Machine Learning and Data Mining 2017: 1281-1282 - [i4]Ute Schmid, Stephen H. Muggleton, Rishabh Singh:
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 17382). Dagstuhl Reports 7(9): 86-108 (2017) - 2016
- [j27]José Hernández-Orallo, Fernando Martínez-Plumed, Ute Schmid, Michael Siebers, David L. Dowe:
Computer models solving intelligence test problems: Progress and implications. Artif. Intell. 230: 74-107 (2016) - [j26]Michael Siebers, Ute Schmid, Dominik Seuß, Miriam Kunz, Stefan Lautenbacher:
Characterizing facial expressions by grammars of action unit sequences - A first investigation using ABL. Inf. Sci. 329: 866-875 (2016) - [c33]Teena Hassan, Dominik Seuss, Johannes Wollenberg, Jens-Uwe Garbas, Ute Schmid:
A Practical Approach to Fuse Shape and Appearance Information in a Gaussian Facial Action Estimation Framework. ECAI 2016: 1812-1817 - [c32]Daniel Hallmann, Ute Schmid, Rüdiger von der Weth:
Gemeinsame mentale Modelle in der agilen Softwareentwicklung: Ein Ansatz zur Erstellung von Gestaltungsempfehlungen für "gute" erfahrungsspezifische User Stories. GI-Jahrestagung 2016: 1969-1974 - [c31]Michael Siebers, Franz Uhrmann, Oliver Scholz, Christoph Stocker, Ute Schmid:
Automatische Detektion von Trockenstress bei Tabakpflanzen mittels Machine-Learning-Verfahren. GIL Jahrestagung 2016: 197-200 - [c30]Christina Zeller, Ute Schmid:
Automatic Generation of Analogous Problems to Help Resolving Misconceptions in an Intelligent Tutor System for Written Subtraction. ICCBR Workshops 2016: 108-117 - [c29]Ute Schmid, Christina Zeller, Tarek R. Besold, Alireza Tamaddoni-Nezhad, Stephen H. Muggleton:
How Does Predicate Invention Affect Human Comprehensibility? ILP 2016: 52-67 - [c28]Anja Gärtig-Daugs, Katharina Weitz, Maike Wolking, Ute Schmid:
Computer science experimenter's kit for use in preschool and primary school. WiPSCE 2016: 66-71 - 2015
- [j25]Sumit Gulwani, José Hernández-Orallo, Emanuel Kitzelmann, Stephen H. Muggleton, Ute Schmid, Benjamin G. Zorn:
Inductive programming meets the real world. Commun. ACM 58(11): 90-99 (2015) - [j24]Ute Schmid, Anja Gärtig-Daugs, Silvia Förtsch:
Introvertierte Studenten, fleißige Studentinnen? - Geschlechtsspezifische Unterschiede in Motivation, Zufriedenheit und Wahrnehmungsmustern bei Informatikstudierenden - Ergebnisse aus Erstsemesterbefragungen an der Fakultät Wirtschaftsinformatik und Angewandte Informatik der Otto-Friedrich-Universität Bamberg differenziert nach Geschlecht. Inform. Spektrum 38(5): 379-395 (2015) - [j23]Ute Schmid:
You Need the AI Community - and the AI Community Needs You! Künstliche Intell. 29(3): 239-241 (2015) - [j22]Tarek R. Besold, José Hernández-Orallo, Ute Schmid:
Can Machine Intelligence be Measured in the Same Way as Human intelligence? Künstliche Intell. 29(3): 291-297 (2015) - [c27]Ute Schmid, Marco Ragni:
Comparing Computer Models Solving Number Series Problems. AGI 2015: 352-361 - [c26]Ute Schmid:
Cognitive Systems: Goals, Approaches, Applications. GI-Jahrestagung 2015: 1251-1252 - [i3]José Hernández-Orallo, Stephen H. Muggleton, Ute Schmid, Benjamin G. Zorn:
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 15442). Dagstuhl Reports 5(10): 89-111 (2015) - 2014
- [j21]Ute Schmid:
Does AI Need a New Debate on Ethics? Künstliche Intell. 28(1): 1-3 (2014) - [c25]Jacqueline Hofmann, Emanuel Kitzelmann, Ute Schmid:
Applying Inductive Program Synthesis to Induction of Number Series A Case Study with IGOR2. KI 2014: 25-36 - [p3]Ute Schmid, Lukas Berle, Michael Munz, Klaus Stein, Martin Sticht:
How Similar is What I Get to What I Want: Matchmaking for Mobility Support. Computational Approaches to Analogical Reasoning 2014: 263-287 - [i2]Mark Wernsdorfer, Ute Schmid:
Grounding Hierarchical Reinforcement Learning Models for Knowledge Transfer. CoRR abs/1412.6451 (2014) - 2013
- [j20]Ute Schmid, Michael Siebers, Johannes Folger, Simone Schineller, Dominik Seuß, Marius Raab, Claus-Christian Carbon, Stella J. Faerber:
A cognitive model for predicting esthetical judgements as similarity to dynamic prototypes. Cogn. Syst. Res. 24: 72-79 (2013) - [j19]Christoph Schlieder, Ute Schmid, Michael Munz, Klaus Stein:
Assistive Technology to Support the Mobility of Senior Citizens - Overcoming Mobility Barriers and Establishing Mobility Chains by Social Collaboration. Künstliche Intell. 27(3): 247-253 (2013) - [c24]Christoph Stocker, Michael Siebers, Ute Schmid:
Erkennung von Sequenzen mimischer Schmerzausdrücke durch genetische Programmierung. LWA 2013: 117-120 - [c23]Christoph Stocker, Franz Uhrmann, Oliver Scholz, Michael Siebers, Ute Schmid:
A machine learning approach to drought stress level classification of tobacco plants. LWA 2013: 163-167 - [p2]Mark Wernsdorfer, Ute Schmid:
From Streams of Observations to Knowledge-Level Productive Predictions. Human Behavior Recognition Technologies 2013: 268-281 - [p1]Günther Görz, Ute Schmid, Ipke Wachsmuth:
Einleitung. Handbuch der Künstlichen Intelligenz 2013: 1-18 - [e5]Günther Görz, Josef Schneeberger, Ute Schmid:
Handbuch der Künstlichen Intelligenz, 5. Auflage. Oldenbourg Wissenschaftsverlag 2013, ISBN 9783486719796 [contents] - [i1]Sumit Gulwani, Emanuel Kitzelmann, Ute Schmid:
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 13502). Dagstuhl Reports 3(12): 43-66 (2013) - 2012
- [j18]Ute Schmid:
KI und Informatik. Künstliche Intell. 26(1): 1-4 (2012) - [c22]Linn Gralla, Thora Tenbrink, Michael Siebers, Ute Schmid:
Analogical Problem Solving: Insights from Verbal Reports. CogSci 2012 - [c21]Michael Siebers, Ute Schmid:
Semi-analytic Natural Number Series Induction. KI 2012: 249-252 - [c20]Ute Schmid, Michael Siebers, Dominik Seuß, Miriam Kunz, Stefan Lautenbacher:
Applying Grammar Inference To Identify Generalized Patterns of Facial Expressions of Pain. ICGI 2012: 183-188 - 2011
- [j17]Ute Schmid, Marco Ragni, Cleotilde Gonzalez, Joachim Funke:
The challenge of complexity for cognitive systems. Cogn. Syst. Res. 12(3-4): 211-218 (2011) - [j16]Ute Schmid, Emanuel Kitzelmann:
Inductive rule learning on the knowledge level. Cogn. Syst. Res. 12(3-4): 237-248 (2011) - [c19]Marius Raab, Mark Wernsdorfer, Emanuel Kitzelmann, Ute Schmid:
From Sensorimotor Graphs to Rules: An Agent Learns from a Stream of Experience. AGI 2011: 333-339 - [e4]Emanuel Kitzelmann, Ute Schmid:
Proceedings of AAIP 2011 - 4th International Workshop on Approaches and Applications of Inductive Programming, Odense, Denmark, July 19, 2011. 2011 [contents] - 2010
- [c18]Christine Barthold, Anton Papst, Thomas Wittenberg, Christian Küblbeck, Stefan Lautenbacher, Ute Schmid, Sven Friedl:
Tracking von Gesichtsmimik mit Hilfe von Gitterstrukturen zur Klassifikation von schmerzrelevanten Action Units. Bildverarbeitung für die Medizin 2010: 455-459 - [c17]Martin Hofmann, Ute Schmid:
Data-Driven Detection of Recursive Program Schemes. ECAI 2010: 1063-1064 - [c16]Michael Siebers, Ute Schmid:
Interleaving Forward Backward Feature Selection. KDIR 2010: 454-457 - [c15]Ute Schmid, Martin Hofmann, Florian Bader, Tilmann Häberle, Thomas Schneider:
Incident Mining Using Structural Prototypes. IEA/AIE (2) 2010: 327-336 - [e3]Ute Schmid, Emanuel Kitzelmann, Rinus Plasmeijer:
Approaches and Applications of Inductive Programming, Third International Workshop, AAIP 2009, Edinburgh, UK, September 4, 2009. Revised Papers. Lecture Notes in Computer Science 5812, Springer 2010, ISBN 978-3-642-11930-9 [contents] - [r3]Pierre Flener, Ute Schmid:
Inductive Programming. Encyclopedia of Machine Learning 2010: 537-544 - [r2]Pierre Flener, Ute Schmid:
Programming by Demonstration. Encyclopedia of Machine Learning 2010: 805 - [r1]Pierre Flener, Ute Schmid:
Trace-Based Programming. Encyclopedia of Machine Learning 2010: 989
2000 – 2009
- 2009
- [j15]Ute Schmid, Martin Hofmann, Emanuel Kitzelmann:
Inductive Programming. Künstliche Intell. 23(2): 38-41 (2009) - [j14]Ute Schmid:
Editorial 3-2009. Künstliche Intell. 23(3): 1 (2009) - [c14]Martin Hofmann, Emanuel Kitzelmann, Ute Schmid:
Porting IgorII from Maude to Haskell. AAIP 2009: 140-158 - [c13]Neil Crossley, Emanuel Kitzelmann, Martin Hofmann, Ute Schmid:
Evolutionary Programming Guided by Analytically Generated Seeds. IJCCI 2009: 198-203 - 2008
- [b3]Jörg Mennicke, Christian Münzenmayer, Ute Schmid:
Classifier Learning for Imbalanced Data - a Comparison of kNN, SVM, and Decision Tree Learning. VDM 2008, ISBN 978-3-8364-9223-2, pp. I-IX, 1-164 - [j13]Pierre Flener, Ute Schmid:
An introduction to inductive programming. Artif. Intell. Rev. 29(1): 45-62 (2008) - [j12]Ute Schmid:
Gasteditorial KI und Kognition. Künstliche Intell. 22(1): 4 (2008) - [j11]Ute Schmid:
Cognition and AI. Künstliche Intell. 22(1): 5-7 (2008) - [j10]Ute Schmid:
Interview with Ken Forbus. Künstliche Intell. 22(1): 37-38 (2008) - [j9]Ute Schmid:
KI und Kognition - Service. Künstliche Intell. 22(1): 45 (2008) - [j8]Ute Schmid:
Editorial. Künstliche Intell. 22(2): 1 (2008) - [j7]Ute Schmid:
KogWis 2008. Künstliche Intell. 22(2): 78 (2008) - [c12]Martin Hofmann, Emanuel Kitzelmann, Ute Schmid:
Analysis and Evaluation of Inductive Programming Systems in a Higher-Order Framework. KI 2008: 78-86 - 2007
- [j6]Sven-Eric Schelhorn, Jacqueline Griego, Ute Schmid:
Transformational and derivational strategies in analogical problem solving. Cogn. Process. 8(1): 45-55 (2007) - [c11]Martin Hofmann, Andreas Hirschberger, Emanuel Kitzelmann, Ute Schmid:
Inductive Synthesis of Recursive Functional Programs. KI 2007: 468-472 - [e2]Emanuel Kitzelmann, Ute Schmid:
Proceedings of the Workshop on Approaches and Applications of Inductive Programming, AAIP'07, September 17, 2007, Warsaw, Poland. 2007 [contents] - 2006
- [j5]Emanuel Kitzelmann, Ute Schmid:
Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach. J. Mach. Learn. Res. 7: 429-454 (2006) - [j4]Helmar Gust, Kai-Uwe Kühnberger, Ute Schmid:
Metaphors and heuristic-driven theory projection (HDTP). Theor. Comput. Sci. 354(1): 98-117 (2006) - [c10]Stephan Weller, Ute Schmid:
Solving Proportional Analogies by E -Generalization. KI 2006: 64-75 - [c9]Emanuel Kitzelmann, Ute Schmid:
Inducing Constructor Systems from Example-Terms by Detecting Syntactical Regularities. RULE@FLoC 2006: 49-63 - 2005
- [c8]Emanuel Kitzelmann, Ute Schmid:
An Explanation Based Generalization Approach to Inductive Synthesis of Functional Programs. AAIP 2005: 15-26 - [e1]Emanuel Kitzelmann, Roland Olsson, Ute Schmid:
Workshop on Approaches and Applications of Inductive Programming, AAIP 2005, to be held in conjunction with the 22nd International Conference on Machine Learning (ICML 2005), Bonn, Germany, August 7, 2005. 2005 [contents] - 2004
- [c7]Helmar Gust, Kai-Uwe Kühnberger, Ute Schmid:
Ontological Aspects of Computing Analogies. ICCM 2004: 350-351 - 2003
- [b2]Ute Schmid:
Inductive Synthesis of Functional Programs, Universal Planning, Folding of Finite Programs, and Schema Abstraction by Analogical Reasoning. Lecture Notes in Computer Science 2654, Springer 2003, ISBN 3-540-40174-1 - [j3]Ute Schmid:
Inductive Synthesis of Functional Programs. Künstliche Intell. 17(3): 75- (2003) - 2002
- [j2]Ute Schmid:
Mitteilungen der Gesellschaft fuer Kognitionswissenschaft e.V. Kognitionswissenschaft 9(4): 197 (2002) - [c6]Emanuel Kitzelmann, Ute Schmid, Martin Mühlpfordt, Fritz Wysotzki:
Inductive Synthesis of Functional Programs. AISC 2002: 26-37 - [c5]Ute Schmid, Marina Müller, Fritz Wysotzki:
Integrating Function Application in State-Based Planning. KI 2002: 144-162 - 2000
- [c4]Ute Schmid, Fritz Wysotzki:
Applying Inductive Program Synthesis to Macro Learning. AIPS 2000: 371-378 - [c3]Sylvia Wiebrock, Lars Wittenburg, Ute Schmid, Fritz Wysotzki:
Inference and Visualization of Spatial Relations. Spatial Cognition 2000: 212-224
1990 – 1999
- 1998
- [c2]Ute Schmid, Fritz Wysotzki:
Induction of Recursive Program Schemes. ECML 1998: 214-225 - [c1]Berry Claus, Klaus Eyferth, Carsten Gips, Robin Hörnig, Ute Schmid, Sylvia Wiebrock, Fritz Wysotzki:
Reference Frames for Spatial Inference in Text Understanding. Spatial Cognition 1998: 241-266 - 1997
- [j1]Ute Schmid:
Programmieren durch analoges Schließen . Kognitionswissenschaft 6(3): 127-134 (1997) - 1994
- [b1]Ute Schmid:
Erwerb rekursiver Programmiertechniken als Induktion von Konzepten und Regeln - ein kognitionswissenschaftlicher Zugang zum Erwerb kognitiver Fertigkeiten. Technical University of Berlin, Germany, DISKI 70, Infix 1994, ISBN 978-3-929037-70-8, pp. 1-165
Coauthor Index
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