ADA Lovelace Center Publikationen

Hier finden Sie die im ADA Lovelace Center entstandenen Publikationen.

[117] Bärmann A., Gemander P. Hager L., Nöth F., Schneider O. (2022): EETTlib – Energy-Efficient Timetabling Library. In: Networks. 

[116] Bärmann A., Gemander P., Martin A., Merkert M.(2022): On Recognizing Staircase Compatibility. In: Journal of Optimization Theory and Applications.

[115] Bärmann A., Gemander P., Merkert M., Wiertz A., Javier Zaragoza-Martínez F. (2022): Algorithms for the Clique Problem with Multiple-Choice Constraints under a Series-Parallel Dependency Graph. In: Discrete Applied Mathematics.

[114] Bärmann A., Burlacu R., Hager L., Kleinert T. (2022). On Piecewise Linear Approximations of Bilinear Terms: Structural Comparison of Univariate and Bivariate Mixed-Integer Programming Formulations. In: Journal on Global Optimization.
 

[113] Brieger T., Raichur N., Jdidi D., Ott F., Feigl T., van der Merwe J., Rugamer A., Felber W. (2022). Multimodal Learning for Reliable Interference Classification in GNSS Signals. In: Proceedings Intl. Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+).

[112] Bruns V., Benz M., Geppert C. (2022): Tissue Cartography for Colorectal Cancer. In: Artificial Intelligence Applications in Human Pathology, 203-241, Ralf Huss, Michael Grunkin (Herausgeber), World Scientific Europe Ltd (Verlag), 978-1-80061-138-2 (ISBN). 

[111] Wilm F., Benz M., Bruns V., Baghdadlian S., Dexl J., Hartmann D., Kuritcyn P.; Weidenfeller M.; Wittenberg T., Merkel S., Merkel S.; Hartmann A.; Eckstein M.; Geppert C. (2022). Fast Whole-Slide Cartography in Colon Cancer Histology Using Superpixels and CNN Classification. In: J. Med. Imag. 9(2) 027501.
 

[110] Bruns V., Benz M., Kuritcyn P., Dexl J., Wittenberg T., Eckstein M., Hartmann A., Geppert C. (2022). Fast and Robust Deep Tissue Cartography for Colon Sections. In: 105. Jahrestagung der Deutschen Gesellschaft für Pathologie.
 

[109] Deng D., Karl F., Hutter F., Bischl B., Lindauer M. (2022). Efficient Automated Deep Learning for Time Series Forecasting. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases.
 

[108] Dexl J., Benz M., Kuritcyn P., Wittenberg T., Bruns V., Geppert C., Hartmann A., Bernd Bischl B., Goschenhofer J. (2022). Robust Colon Tissue Cartography with Semi-Supervision. In: Current Directions in Biomedical Engineering, vol. 8, no. 2, 2022, pp. 344-347.

[107] Goschenhofer J., Ragupathy P., Heumann C., Bischl B., Assenmacher M. (2022). CC-Top: Constrained Clustering for Dynamic Topic Discovery. In: Proceedings of the EMNLP Workshop on Ever Evolving NLP. 

[106] Jaina V., Wicht J., Wetzker U., Frotzscher A. (2022). Synchronizing Spectral and Protocol Analysis for Complementary Troubleshooting of Wireless Standards. In: International Conference on Embedded Wireless Systems and Networks 2022

[105] Jdidi D., Brieger T., Feigl T., Franco D., an der Merwe J., Rügamer A., Seitz J., Felber W. (2022). Unsupervised Disentanglement for Post-Identification of GNSS Interference in the Wild. In: Proceedings Intl. Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+). 

[104] Kram S., Kraus C., Stahlke M., Feigl T., Thielecke J., Mutschler C. (2022). Delay Estimation in Dense Multipath Environments using Time Series Segmentation. In: Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC).

[103] Landgraf D., Völz A., Kontes G., Graichen K., Mutschler C. (2022). Hierarchical Learning for Model Predictive Collision Avoidance. (2022). In: IFAC-PapersOnLine, Volume 55, Issue 20.  

[102] Löffler C., Fallah K., Fenu S., Zanca D., Eskofier B., J. Rozell C., Mutschler C. (2022). Active Learning of Ordinal Embeddings: A User Study on Football Data. (2022). In: arXiv preprint.

[101] Löffler C., Lai W., Eskofier B., Zanca D., Schmidt L. & Mutschler C. (2022). Don’t Get Me Wrong: How to apply Deep Visual Interpretations to Time Series. In: arXiv preprint. 

[100] Löffler C., Mutschler C. (2022). IALE: Imitating Active Learner Ensembles. In: Journal of Machine Learning Research, 23(107), 1–29. 

[99] Mahesh B., Weber D., Garbas J., Foltyn A., Oppelt M., Becker L., Rohleder N., Lang N. (2022). Setup for Multimodal Human Stress Dataset Collection. In: Volume 2 of the Proceedings of the Joint 12th International Conference on Methods and Techniques in Behavioral Research and 6th Seminar on Behavioral Methods, May 18-20, 2022.

[98] Marzilger R., Hirn F., Alvarez R., Witt N. (2022). Sports Scene Searching, Rating & Solving using AI. In: Proceedings of the 14th Symposium of the Section Sport Informatics and Engineering of the German Society of Sport Science (dvs), Chemnitz September 29-30, 2022 (spinfortec2022).

[97] Oppelt M., Foltyn A., Deuschel J., Lang N., Holzer N., Eskofier B., Yang S. (2022). ADABase: A Multimodal Dataset for Cognitive Load Estimation. In: Sensors 2023, 23(1), 340

[96] Ott F., Raichur N., Rügamer D., Feigl T., eumann H., Bischl B., Mutschler C. (2022). Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression. In: arXiv preprint.

[95] Ott F., Rügamer D., Heublein L., Bischl B., Mutschler C. (2022). Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition. In: arXiv preprint.

[94] Ott F., Rügamer D., Heublein L., Bischl B., Mutschler C. (2022). Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift. In: Proceedings of the ACM Intl. Conf. on Multimedia (AC-MMM), pages 5934-5943, Lisboa, Portugal, October 2022

[93] Ott F., Rügamer D., Heublein L., Bischl B., Mutschler C. (2022). Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition. In: IAPR Intl. Workshop on Multimodal Pattern Recognition of Social Signals in Human Computer Interaction (MPRSS).

[92] Ott F., Rügamer D., Heublein L., Bischl B., Mutschler C. (2022). Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens. In: International Journal on Document Analysis and Recognition (IJDAR), September 2022.

[91] Qiu J., Oppelt M., Nissen M., Anneken L., Breininger K., Eskofier B. (2022). Improving Deep Learning-based Cardiac Abnormality Detection in 12-Lead ECG with Data Augmentation. In: 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2022, pp. 945-949

[90] Raichur N., Brieger T., Jdidi D., Feigl T., van der Merwe J., Ghimire B., Ott F., Rügamer A., Wolfgang Felber W. (2022). Machine Learning-assisted GNSS Interference Monitoring through Crowd-sourcing. In: Proceedings Intl. Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+).

[89] Reeb L. (2022). Searching for Soccer Scenes using Siamese Neural Networks. In: Data Science Blog.

[88] Richter A., Ijaradar J., Wetzker U., Jain V., Frotzscher A. (2022). Multivariate Time Series Imputation: A Survey on available Methods with a Focus on hybrid GANs. In: IEEE Access.

[87] Rietsch S., Huang S., Kontes G., Plinge A., Mutschler C. (2022). Driver Dojo: A Benchmark for Generalizable Reinforcement Learning for Autonomous Driving. In: GitHub & ArXiv.

[86]  Rüger S., Goschenhofer J., Nath A., Firsching M., Ennen A., Bernd Bischl B. (2022). Deep-Learning-based Aluminum Sorting on Dual Energy X-Ray Transmission Data. In: 9th Sensor-Based Sorting & Control 2022. ISBN: 978-3-8440-8516-7

[85] Schellenberger M., Lorentz V., Eckardt B. (2022). Cognitive Power Electronics – An Enabler for Smart Systems. In: PCIM Europe 2022: International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management, Nuremberg, Germany.

[84] Schmidt L.; Brosig L., Kontes G.; Plinge A.; Eskofier B., Mutschler C. (2022). An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility. In: IEEE International Conference on Intelligent Transport Systems, Macau, China.

[83] Schmidt L.; Rietsch S.; Plinge A.; Eskofier B., Mutschler C. (2022).  How to Learn from Risk: Explicit Risk-Utility Reinforcement Learning for Efficient and Safe Driving Strategies. In: IEEE International Conference on Intelligent Transport Systems, Macau, China.

[82] Schwanninger R., Wunder B. (2022). Impedances in DC-Microgrids – From Offline to Online Measurements. In: 11th Power Analysis & Design Symposium 2022 (VIRTUAL).

[81] Wicht J., Wetzker U., Jain V. (2022). Spectrogram Data Set for Deep Learning Based RF-Frame Detection.

[80] Wilm F., Benz M., Bruns V., Baghdadlian S., Dexl J., Hartmann D., Kuritcyn P.; Weidenfeller M.; Wittenberg T., Merkel S., Merkel S.; Hartmann A.; Eckstein M.; Geppert C. (2022). Fast Whole-Slide Cartography in Colon Cancer Histology Using Superpixels and CNN Classification. In: J. Med. Imag. 9(2) 027501.

[79] Altstidl T.; Kram S.; Herrmann O.; Stahlke M.; Feigl T.; Mutschler C. (2021). Accuracy-Aware Compression of Channel Impulse Responses Using Deep Learning. In: Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN 2021).

[78] Baermann A.; Gemander P.; Martin A.; Merkert M.; Nöth F. (2021). Energy-Efficient Timetabling in a German Underground System. In: Success Stories of Industrial Mathematics in Germany (eingeladenes Buchkapitel, Editoren: Küfer et al.).

[77] Bärmann A.; Martin A.; Schneider O. (2021). Efficient Formulations and Decomposition Approach es for Power Peak Reduction in Railway Traffic via Timetabling. In: Transportation Science, Vol. 55.

[76] Bärmann A.; Mehringer J.; Menden C.; Neumann U.; Schemm J.; Schneider O.; Sonnleitner B.; Weissenbäck M. (2021). Data Analytics in der Supply Chain (Whitepaper).

[75] Bärmann A.; Schneider O. (2021). Set Characterizations and Convex Extensions for Geometric Convex-Hull Proofs. In: Mathematical Programming.

[74] Bruns V.; Huss R.; Kuritcyn P.; Benz M.; Ebigbo A.; Probst A.; Palm C.; Wittenberg T.; Geppert C.; Hartmann A.; Messmann H.; Märkl B. (2021). Adapting an Adenocarcinoma-Trained Tissue Cartography AI Model to Early Lesions and Adenoma. Virtuelle Pathologietage der Deutschen Gesellschaft für Pathologie.

[73] Deuschel J.; Foltyn A. (2021). Benchmarking Robustness to Natural Distribution Shifts for Facial Analysis. NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and Applications.

[72] Deuschel J.; Firmbach D.; Geppert C.; Eckstein M.; Hartmann A.; Bruns V.; Kuritcyn P.; Dexl J.; Hartmann D.; Perrin D.; Wittenberg T.; Benz M. (2021). Multi-Prototype Few-Shot Learning in Histopathology. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops.

[71] Deuschel J.; Finzel B.; Rieger I. (2021). Uncovering the Bias in Facial Expressions.

[70] Dexl J.; Benz M.; Bruns V.; Kuritcyn P.; Wittenberg T. (2021). MitoDet: Simple and Robust Mitosis Detection. In: Proceedings of the MICCAI 2021. LNCS 13166.

[69] Feigl T.; Eberlein E.; Kram S.; Mutschler C. (2021). Robust ToA-Estimation using Convolutional Neural Networks on Randomized Channel Models. In: Proceedings of the International Conference on Indoor Positioning and Indoor Navigation. (IPIN 2021).

[68] Foltyn A.; Deuschel J. (2021). Towards Reliable Multimodal Stress Detection under Distribution Shift. In: Companion Publication of the 2021 International Conference on Multimodal Interaction (ICMI ’21 Companion).

[67] Henne M.; Gansloser J.; Schwaiger A.; Weiss G. (2021). Machine-Learning Methods for Enhanced Reliable Perception of Autonomous Systems.

[66] Gansloser J. (2021). Methoden zur Absicherung von Machine Learning Verfahren in sicherheitskritischen Systemen. (2021). Forum Safety & Security 2021.

[65] Gerostathopoulos I.; Plasil F.; Prehofer C.; Thomas J.; Bischl B. (2021). Automated Online Experiment-Driven Adaptation – Mechanics and Cost Aspects. In: IEEE Access Journal.

[64] Goschenhofer J.; Hvingelby R.; Ruegamer D.; Thomas J.; Wagner M.; Bischl B. (2021). Deep Semi-Supervised Learning for Time Series Classification. 20th IEEE International Conference on Machine Learning and Applications (ICMLA).

[63] Kaminwar S.; Goschenhofer J.; Thomas J.; Thon I.; Bischl B. (2021). Structured Verification of Machine Learning Models in Industrial Settings. In: Big Data.

[62] Kuritcyn P.; Benz M.; Dexl J.; Bruns V.; Hartmann A.; Geppert C. (2021). Comparison of CNN Models on a Multi-Scanner Database in Colon Cancer Histology. Medical Imaging with Deep Learning (MIDL 2021).

[61] Kuritcyn P.; Geppert C.; Eckstein M.; Hartmann A.; Bruns V.; Dexl J.; Baghdadlian S.; Hartmann D.; Perrin D.; Wittenberg T.; Benz M. (2021). Robust Slide Cartography in Colon Cancer Histology – Evaluation on a Multi-Scanner Database. In: Bildverarbeitung für die Medizin 2021, Informatik aktuell.

[60] Löffler C.; Reeb L.; Dzibela D.; Marzilger R.; Witt N.; Esko-fier B.; Mutschler C. (2021). Deep Siamese Metric Learning: A Highly Scalable Approach to Searching Unordered Sets of Trajectories. In: ACM Transaction on Intelligent Systems.

[59] Neuhaus P.; Henninger M.; Frotzscher A.; Wetzker U. (2021). Autoencoder-Based Characterisation of Passive IEEE 802.11 Link Level Measurements. 2021 Joint European Conference on Networks and Communications & 6G Summit (EUCNC).

[58] Plinge A.; Kontes G.; Schmidt L. (2021). How to Teach a Machine to Drive in Difficult Situations and Be Able to Rely on It. Forum Künstliche Intelligenz.

[57] Roeder G.; Ott L.; Meier A.; Wunder B.; Wienzek P.; Baermann A.; Liers F.; Schellenberger M. (2021). Analysis and Improvement of LVDC-Grid Stability using Circuit Simulation and Machine Learning – A Case Study. In: Proceedings of the NEIS 2021 – Conference on Sustainable Energy Supply and Energy Storage Systems (IEEE).

[56] Saha B; Becker L.; Garbas J.; Oppelt M.; Foltyn M.; Hettenkofer S.; Lang N.; Struck M.; Rohleder N.; Mahesh B. (2021). Investigation of Relation between Physiological Responses and Personality during Stress Recovery. IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops).

[55] Sczogiel S.; Neumann U.; Schmitt-Rüth S. (2021). Detecting Social and Ethical Implications of Artificial Intelligence: A Structured Workshop Method from a Social Sciences Perspective. 31st RESER International Conference.

[54] Schmidt L.; Kontes G.; Plinge A.; Mutschler C. (2021). Can You Trust Your Autonomous Car? Interpretable and Verifiably Safe Reinforcement Learning. IEEE Intelligent Vehicles Symposium (Best Paper Award).

[53] Stahlke M.; Kram S.; Ott F.; Feigl T.; Mutschler C. (2021). Estimating TOA Reliability with Variational Autoencoders. In: IEEE Sensors Journal.

[52] Wicht J.; Wetzker U.; Frotzscher A. (2021). Deep Learning Based Real-Time Spectrum Analysis for Wireless Networks. European Wireless 2021.

[51] Wilm F., Benz M.; Bruns V.; Baghdadlian S.; Dexl J.; Hartmann D.; Kuritcyn P.; Weidenfeller M.; Wittenberg T.; Merkel S.; Hartmann A.; Eckstein M.; Geppert C. (2021). Fast Whole-Slide Cartography in Colon Cancer Histology using Superpixels and CNN Classification.

[50] Wittenberg T.; Benz M.; Foltyn A.; Hackner R.; Hetzel J.; Wiesmann V.; Eixelberger T. (2021). Acquisition of Semantics for AI-based Applications in Medical Technologies – An Overview. In: Current Directions in Biomedical Engineering.

[49] Baermann, A.; Gemander P.; Merkert M. (2020). The Clique Problem with Multiple-Choice Constraints under a Cycle-Free Dependency Graph. In: Discrete Applied Mathematics, Vol. 283.
 

[48] Binder M.; Moosbauer J.; Thomas J.; Bischl B. (2020). Multi-Objective Hyperparameter Tuning and Feature Selection using Filter Ensembles. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference (GECCO).
 

[47] Butyrev L.; Edelhäußer T.; Mutschler C. (2020). Deep Reinforcement Learning for Motion Planning of Mobile Robots.
 

[46] Feigl T.; Grunder L.; Mutschler C.; Roth D. (2020). Real-Time Gait Reconstruction for Virtual Reality Using a Single Sensor. In: Proceedings International Symposium on Mixed and Augmented Reality (ISMAR) pp: 1-8.
 

[45] Feigl T.; Kram S.; Woller P.; Siddiqui, R.; Philippsen M. (2020). RNN-Aided Human Velocity Estimation from a Single IMU. In: J. Sensors Vol. 20 (13), Nr. 3656, pp. 1-43.
 

[44] Feigl T.; Porada A.; Steiner S.; Löffler C.; Mutschler C.; Philippsen M. (2020). Localization Limitations of ARCore, ARKit, and Hololens in Dynamic Large-Scale Industry Environments. In: Proceedings International Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (GRAPP), pp. 1-13.
 

[43] Gemander P.; Chen W.; Weninger D.; Gottwald L.; Gleixner A.; Martin A. (2020). Two-row and two-column mixed-integer presolve using hashing-based pairing methods. In: EURO Journal on Computational Optimization, Vol. 8.
 

[42] Gjoreski M.; Gams M.; Luštrek M.; Genc P.; Garbas J.; Hassan T. (2020). Machine learning and end-to-end deep learning for monitoring driver distractions from physiological and visual signals.
 

[41] Gruber R.; Gerth S.; Claußen J.; Wörlein N.; Uhlmann N.; Wittenberg T. (2020). Exploring Flood Filling Networks for Instance Segmentation of XXL-Volumetric and Bulk Material CT Data. In: Journal of Nondestructive Evaluation, Volume 40.
 

[40] Henne M.; Schwaiger A.; Roscher K.; Weiss G. (2020). Benchmarking Uncertainty Estimation Methods for Deep Learning with Safety-Related Metrics. Workshop on Artificial Intelligence Safety. SafeAI-2020.
 

[39] Koch R.; Pfeiffer N.; Lang N.; Eskofier B.; Amft O.; Struck M.; Wittenberg T. (2020). Evaluation of HRV extraction algorithms from PPG data using neural networks. In: Current Directions in Biomedical Engineering. 6(3).
 

[38] Kontes G.; Scherer D.; Nisselbeck T.; Fischer J.; Mutschler C. (2020). High-Speed Collision Avoidance using Deep Reinforcement Learning and Domain Randomization for Autonomous Vehicles. In: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), pp. 1-8.
 

[37] Kram S.; Stahlke M.; Mutschler C. (2020). IPIN Offline Competition: Channel Impulse Responses. In: Proceedings International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1-10.
 

[36] Löffler C.; Mutschler C. (2020). IALE: Imitating active learner ensembles. International Conference on Learning Representations.
 

[35] Löffler C.; Nickel C.; Sobel C.; Dzibela D.; Braat J.; Gruhler B.; Woller P.; Witt N.; Mutschler C. (2020). Automated Quality Assurance for Hand-held Tools via Embedded Classification and AutoML. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp. 1-9, ISBN: 978-3-030-67670-4.
 

[34] Löffler C.; Redzepagic A.; Feigl T.; Mutschler C. (2020). A Sense of Quality for Augmented Reality Assisted Process Guidance. International Symposium on Mixed and Augmented Reality.
 

[33] Melnychuk V.; Faerman E.; Manakov I.; Seidl T. (2020). Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels. MICCAI KDAH-CIKM Workshop 2020 - 3rd Workshop on Knowledge-driven Analytics and Systems Impacting Human Quality of Life.
 

[32] Mishra A.; Löffler C.; Plinge A. (2020). Recipies for Post-Training Quantization of Deep Neural Networks. In: Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2), pp. 1-12.
 

[31] Oppelt M.; Riehl M.; Kemeth F.; Steffan J. (2020). Combining Scatter Transform and Deep Neural Networks for Multilabel Electrocardiogram Signal Classification. In: Computing in Cardiology, pp. 1-12.
 

[30] Ott F.; Feigl T; Löffler C.; Mutschler C. (2020). ViPR: Visual-Odometry-aided Pose Regression for 6DoF Camera Localization. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 1-10.
 

[29] Redzepagic A.; Löffler C.; Feigl T.; Mutschler C. (2020). A Sense of Quality for Augmented Reality Assisted Process Guidance. In: Proceedings International Symposium on Mixed and Augmented Reality (ISMAR), pp. 1-8.
 

[28] Reeb L.; Dzibela D.; Marzilger R.; Witt N. (2020): Effiziente Suche und Bewertung von Szenen in Spielsportarten. In: 13. Symposium der DVS-Sektion »Sportinformatik und Sporttechnologie«, pp. 1-10.
 

[27] Sauer A. (2020). Die Gleichstromfabrik: Energieeffizient. Robust. Zukunftsweisend. Buch von Carl Hanser Verlag GmbH Co KG. ISBN: 9783446466128.
 

[26] Schwaiger A.; Sinhamahapatra p.; Gansloser J.; Roscher K. (2020). Is Uncertainty Quantification in Deep Learning Sufficient for Out-of-Distribution Detection? Workshop on Artificial Intelligence Safety (AISafety 2020).
 

[25] Stahlke M.; Kram S.; Mutschler C. (2020). NLOS Detection using UWB Channel Impulse Responses and Convolutional Neural Networks. In: Proceedings International Conference on Localization and GNSS (ICL-GNSS).
 

[24] Thomas J.; Goschenhofer J.; Wagner M.; Thon I. (2020). Limitations and Potentials of AutoML within the Machine Learning Lifecycle. Whitepaper in Industriekooperation.
 

[23] Thomas J.; Karl F.; Gross R. (2020). State-of-the-art and Limitations of Automated Machine Learning. Whitepaper in Industriekooperation.

[22] Baermann, A.; Büsing, C.; Liers, F. (2019). Globalized Robust Optimization with Gamma-Robustness. In: INFORMS Journal on Computing.
 

[21] Binder, M.; Moosbauer J.; Thomas J.; Bischl B. (2019). Model-Agnostic Approaches to Multi-Objective Simultaneous Hyperparameter Tuning and Feature Selection.
 

[20] Butyrev L.; Kontes G.; Löffler C.; Mutschler C. (2019). Overcoming Catastrophic Forgetting via Hessian-free Curvature Estimates.
 

[19] Chen W.; Gemander P.; Gleixner A.; Gottwald L.; Martin A.; Weninger D. (2019). Two-Row and Two-Column Mixed-Integer Presolve using Hash-Based Pairing Methods. In: European Journal on Computational Optimization.
 

[18] Feigl T.; Kram S.; Woller P.; Siddiqui R.; Philippsen M.; Mutschler C. (2019). A Bidirectional LSTM for Estimating Dynamic Human Velocities from a Single IMU. International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1-8.
 

[17] Hassan T.; Seuß D.; Wollenberg J.; Weitz K.; Kunz M.; Lautenbacher S.; Garbas J.; Schmid U. (2019). Automatic detection of pain from facial expressions: a survey. IEEE Transactions on Pattern Analysis and Machine Learning.
 

[16] Henne M.; Schwainger A.; Weiss G. (2019). Managing Uncertainty of AIbased Perception for Autonomous Systems. Workshop on Artificial Intelligence Safety. Online resource: Co-located with the 28th International Joint Conference on Artificial Intelligence, pp 57-60.
 

[15] Kram S.; Stahlke M.; Feigl T.; Seitz J.; Thielecke J. (2019). UWB Channel Impulse Responses for Positioning in Complex Environments: A Detailed Feature Analysis. In: Sensors, Vol. 19, Nr. 24 (5547).
 

[14] Menden C.; Mehringer J.; Martin A.; Amberg M. (2019). Vorhersage von Ersatzteilbedarfen mit Hilfe von Clusteringverfahren. In: HMD Praxis der Wirtschaftsinformatik. (DOI), Vol. 56, pp 1000-1016.
 

[13] Mutschler C.; Pokutta S. (2019). Self-Supervised Policy Adaptation.
 

[12] Niitsoo A.; Edelhäußer T.; Eberlein E.; Hadaschik N.; Mutschler C. (2019). A Deep Learning Approach to Position Estimation from Channel Impulse Responses. In: Sensors Vol. 19 Nr. 5 (1064).
 

[11] Ott F.; Feigl T.; Löffler C.; Mutschler C. (2019). ViPR: Visual-Odometry-aided Pose Regression for 6DoF Camera Localization.
 

[10] Rabold J.; Deininger H.; Siebers M.; Schmid U. (2019). Enriching Visual with Verbal Explanations for Relational Concepts-Combining LIME with Aleph.
 

[9] De Raedt L.; Evans R.; Muggleton, S.; Schmid U. (2019). Approaches and Applications of Inductive Programming. In Dagstuhl Reports, Vol. 9, Nr. 5.
 

[8] Rieger I.; Finzel B.; Seuß D.; Wittenberg T.; Schmid U. (2019). Make Pain Estimation Transparent: A Roadmap to Fuse Bayesian Deep Learning and Inductive Logic Programming (Poster). 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society.
 

[7] Rüger S.; Firsching M.; Lucic J.; Ennen A.; Uhlmann N.; Wittenberg T. (2019). Automated detection of bone splinters in DEXA phantoms using deep neural networks. In Current Directions in Biomedical Engineering, Vol. 5, Nr. 1, pp: 281-283.
 

[6] Schallner L.; Rabold J.; Scholz O.; Schmid U. (2019). Effect of Superpixel Aggregation on Explanations in LIME - A Case Study with Biological Data.
 

[5] Schmid U.; Finzel B.; Mutual Explanations for Cooperative Decision Making in Medicine. In Künstliche Intelligenz, Vol. 34, Nr. 2. 2020, pp: 227-233.
 

[4] Seitzer M.; Foltyn A.; Kemeth F. (2019). Improved Disentanglement through Learned Aggregation of Convolutional Feature Maps. Disentanglement Challenge@NeuIPS 2019 (2. Platz).
 

[3] Siebers M.; Schmid U. (2019). Please delete that! Why should I? - Explaining learned irrelevance classifications of digital objects. (2019) In Künstliche Intelligenz, Vol. 33, Nr. 1, pp: 35-44.
 

[2] Weitz K.; Hassan T.; Schmid U.; Garbas J. (2019). Deep-learned faces of pain and emotions: Elucidating the differences of facial expressions with the help of explainable AI methods. In tm-Technisches Messen Vol. 86, Nr. 7-8, pp: 404-412.
 

[1] Zobel P.; Rathke M.; Mühldorfer S.; Wittenberg T. (2019). Computer Aided Detection of Polyps in White Light-Colonoscopy Images using Deep Neural Networks. In Current Directions in Biomedical Engineering, Vol. 5, Nr. 1, pp: 231-234.