ADA Lovelace Center Publikationen

Hier finden Sie die im ADA Lovelace Center entstandenen Publikationen.

[170] Alle J., Gruber R., Wörlein N., Uhlmann N., Claußen J., Witten­berg T., Gerth S. (2023): 3D Segmentation of Plant Root Systems using Spatial Pyramid Pooling and Locally Adap­tive Field-Of-View Inference. In: Front. Plant Sci.

[169] Aßenmacher, M., Rauch, L., Goschenhofer, J., Stephan, A., Bischl, B., Roth, B., Sick, B. (2023): Towards Enhancing Deep Active Learning with Weak Supervision and Constrained Clustering. In: Proceedings of the 7th Workshop on Interactive Adaptive Learning (Co-Located with ECML-PKDD 2023)

[168] Bärmann A., Burlacu R., Hager L., Kutzer K. (2023): An Approximation Algorithm for Optimal Piecewise Linear Interpolations of Bounded Variable Products. In: Journal of Optimization Theory and Applications

[167] Bärmann, A., Martin, A., Schneider, O. (2023): The Bipartite Boolean Quadric Polytope with Multiple-Choice Constraints. In: SIAM Journal on Optimization.

[166] Bärmann A., Gemander P., Hager L., Nöth F., Schneider O. (2023): EETTlib - Energy-efficient train timetabling library. In: Networks

[165] Bärmann A., Gemander P., Martin A. (2023): A Stochastic Optimization Approach to Energy-Efficient Underground Timetabling Under Uncertain Dwell and Running Times. (2023) In: Transportation Science

[164] Bärmann A., Gemander P., Merkert M., Wiertz A., Martínez F. (2023): Algorithms for the clique problem with multiple-choice constraints under a series–parallel dependency graph. In: Discrete Applied Mathematics

[163] Beer A., Draganov A., Hohma E., Jahn P., Frey C.M.M., Assent I. (2023): Connecting the Dots--Density-Connectivity Distance unifies DBSCAN, k-Center and Spectral Clustering. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

[162] Deutel M., Kontes G., Mutschler C., Teich, J. (2023): Augmented Random Search for Multi-Objective Bayesian Optimization of Neural Networks. In: ArXiv preprint

[161] Deutel, M., Mutschler, C., Teich, J. (2023): microYOLO: Towards Single-Shot Object Detection on Microcontrollers. In Proceedings of the ITEM: IoT, Edge, and Mobile for Embedded Machine Learning

[160] Deutel M., Woller P., Mutschler C., Teich J. (2023): Energy-efficient Deployment of Deep Learning Applications on Cortex-M based Microcontrollers using Deep Compression. In: MBMV 2023; 26th Workshop

[159] Firmbach D., Benz M., Kuritcyn P., Bruns V., Lang-Schwarz C., Stuebs F.A., Merkel S., Leikauf L., Braunschweig A., Oldenburger A., Gloßner L., Abele N., Eck C., Matek C., Hartmann A., Geppert C.I. (2023): Tumor–Stroma Ratio in Colorectal Cancer—Comparison between Human Estimation and Automated Assessment. In: Cancers

[158] Gilhuber S., Busch J., Rotthues D., Frey C.M.M., Seidl T. (2023): DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification. In: Machine Learning and Knowledge Discovery in Databases: Research Track: European Conference, ECML PKDD 2023

[157] Gilhuber S., Hvingelby R., Fok M.L.A., Seidl T. (2023): How to Overcome Confirmation Bias in Semi-Supervised Image Classification by Active Learning. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases

[156] Goschenhofer J., Bischl B., Kira Z. (2023): ConstraintMatch for semi-constrained Clustering. In: International Joint Conference on Neural Networks (IJCNN)

[155] Gutina D., Bärmann A., Roeder G., Schellenberger M., Liers F. (2023): Optimization over decision trees: a case study for the design of stable direct-current electricity networks. In: Optimization and Engineering 24

[154] Götz T., Arora P., Erick F.X., Holzer N., Sawant S. (2023): Self-supervised representation learning using multimodal Transformer for emotion recognition. In: iWOAR '23: Proceedings of the 8th international Workshop on Sensor-Based Activity Recognition and Artificial Intelligence

[153] Heidrich L., Slany E., Scheele S., Schmid U. (2023): FairCaipi: A Combination of Explanatory Interactive and Fair Machine Learning for Human and Machine Bias Reduction. In: Machine Learning and Knowledge Extraction 5

[152] Heinlein L., Benz M., Kuritcyn P., Bruns V., Hartmann A., Keil F., Geppert C. I., Evert K., Wittenberg T. (2023): Lymph node metastases detection in Whole Slide Images using prototypical patterns and transformer-guided multiple instance learning. In: Current Directions in Biomedical Engineering

[151] Jain V., Fokow V., Wicht J., Wetzker U. (2023): A Dynamic Time Warping based Method to Synchronize Spectral and Protocol Domains for Troubleshooting Wireless Communication. In: IEEE Access

[150] Karl F., Pielok T., Moosbauer J., Pfisterer F., Coors S., Binder M., Schneider L., Thomas J., Richter J., Lang M., Garrido-Merchán E. C., Branke J., Bischl B. (2023): Multi-Objective Hyperparameter Optimization in Machine Learning — An Overview. In: ACM Transactions on Evolutionary Learning and Optimization

[149] Kemeter L. M., Hvingelby R., Sierak P., Schön T., Gosswami B. (2023): Towards reducing data acquisition and labeling for defect detection using simulated data. In: Proceedings of the 22th Conference on European Conference on Machine Learning and Principles and Practice of Knowledge Discovery ECML’23

[148] Kletzander R., Benz M., Bruns V., Kuritcyn P., Firmbach D., Matek C., Geppert C. I., Hartmann A., Eckstein M. (2023): Interactive AI training with minimal annotations. In: Jahrestagung der Deutschen Gesellschaft für Pathologie (DGP)

[147] Kletzander R., Kuritcyn P., Bruns V., Eckstein M., Geppert C. I., Hartmann A., Benz M. (2023): Domain Transfer in Histopathology using Multi-Protonets with Interactive Prototype Adaptation. In: Current Directions in Biomedical Engineering 2023

[146] Kuritcyn P., Bruns V., Hartmann A., Geppert C. I., Benz M. (2023): Uncertainty calibrated deep tissue classification in histopathology. In: 19thEuropean Congress on Digital Pathology (ECDP 2023)

[145] Küpper C., Rösch J., Porada A., Loidl K., Seitz J., Winkler H. (2023): Use Case Driven Feasibility Study on the Technical Capabilities of 5G Indoor Positioning in the Automotive Production. In: IEEE 13th Intl. Conf. on Indoor Positioning and Indoor Navigation (IPIN) CEUR-WS

[144] Lang T., Sauer T., Wittenberg T., Gerth S., Uhlmann, N. (2023): OntoSeg – Segmen­ta­tion of Large Volumetric Datasets Using Semantic Knowledge. In: IEEE 17th International Conference on Se­man­tic Computing (ICSC)

[143] Löffler C., Fallah K., Fenu S., Zanca D., Eskofier B., Rozell C. J., Mutschler C. (2023): Active Learning of Ordinal Embeddings: A User Study on Football Data. In: Transactions on Machine Learning Research!

[142] Marzilger R. (2023): Identify Game Tactics in Soccer by Clustering Positional Data. In: Data Science Blog medium.com.

[141] Mattick A., Mutschler C. (2023): Reinforcement Learning for Node Selection in Branch-and-Bound. In: ArXiv preprint

[140] Medvedev V., Erdmann A., Rosskopf A. (2023): Modeling of Near-and Far-Field Diffraction from EUV Absorbers Using Physics-Informed Neural Networks. In: Photonics & Electromagnetics Research Symposium (PIERS)

[139] Meyer N., Scherer D. D., Plinge A., Mutschler C., Hartmann M. J. (2023): Quantum Natural Policy Gradients: Towards Sample-Efficient Reinforcement Learning. In: 2023 IEEE International Conference on Quantum Computing and Engineering (QCE)

[138] Meyer N., Scherer D. D., Plinge A., Mutschler C., Hartmann M. J. (2023): Quantum Policy Gradient Algorithm with Optimized Action Decoding. In: Proceedings of the 40th International Conference on Machine Learning

[137] Meyer N., Ufrecht C., Periyasamy M., Scherer D. D., Plinge A., Mutschler C. (2023): A Survey on Quantum Reinforcement Learning. In: arXiv preprint

[136] Ohlenforst T., Schreiber M., Kreyß F., Schrauth M. (2023): Enabling distributed inference of large neural networks on resource constrained edge devices using ad hoc networks. In: Distributed Computing and Artificial Intelligence, 20th International Conference

[135] Oppelt M. P., Foltyn A., Deuschel J., Lang N. R., Holzer N., Eskofier B. M., Yang, S. H. (2023): ADABase: A Multimodal Dataset for Cognitive Load Estimation. In: Sensors 2023

[134] Ott F., Heublein L., Rügamer D., Bischl B., Mutschler C. (2023): Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition. In: IEEE Access

[133] Ott F., Heublein L., Rügamer D., Bischl B., Mutschler C. (2023): Fusing Structure from Motion and Simulation-Augmented Pose Regression from Optical Flow for Challenging Indoor Environments. In: arXiv:2304.

[132] Ott J., Stahlke M., Kram S., Feigl T., Mutschler C. (2023):  Multipath Delay Estimation in Complex Environments using Transformer. In: IEEE 13th Intl. Conf. on Indoor Positioning and Indoor Navigation (IPIN)

[131] Parthasarathy D., Kontes G., Plinge A., Mutschler C. (2023): C-MCTS: Safe Planning with Monte Carlo Tree Search. In: ArXiv preprint

[130] Periyasamy M., Hölle M., Wiedmann M., Scherer D. D., Plinge A., Mutschler C. (2023): BCQQ: Batch-Constraint Quantum Q-Learning with Cyclic Data Re-uploading. In: arXiv preprint

[129] Prager R. P., Dietrich K., Schneider L., Schäpermeier L., Bischl B., Kerschke P., Trautmann H., Mersmann O. (2023): Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features. In: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms

[128] Rodemann J., Goschenhofer J., Dorigatti E., Nagler T., Augustin T. (2023): Approximately Bayes-optimal pseudo-label selection. In: Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence

[127] Sawant S., Erick F. X., Arora P., Pahl J., Foltyn A., Holzer N., Götz T. (2023): Transformer-based Self-supervised Representation Learning for Emotion Recognition Using Bio-signal Feature Fusion. In: 11th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)

[126] Schneider L., Bischl B., Thomas J. (2023): Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models. In: Proceedings of the Genetic and Evolutionary Computation Conference.

[125] Schwanninger R., Roeder G., Wienzek P., Lavery M., Wunder B., Schellenberger M., Lorentz V., Maerz M. (2023): Experimental Assessment of LVDC-Grid Stability Optimization using Circuit Simulation and Machine Learning. In: 2023 IEEE Fifth International Conference on DC Microgrids (ICDCM) 2023

[124] Schön T., Gosswami B., Hvingelby R., Suth D., Kemeter L. M., Sierak P. (2023): Automated defect recognition in X-ray projections using neural networks trained on simulated and real-world data. In: Proceedings of the 12th Conference on Industrial Computed Tomograph ICT’23

[123] Singh A., Wittenberg T., Salman M., Holzer N., Göb S., Pahl J., Götz T., Sawant S. (2023): Bio-signal based multimodal fusion with bilinear model for emotion recognition. In: 1st International Workshop on Affective Computing and Health Care: new research and industrial perspectives, a joint event of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

[122] Stahlke M., Feigl T., Kram S., Eskofier B. M., Mutschler C. (2023): Uncertainty-based Fingerprinting Model Monitoring for Radio Localization. In: IEEE 13th Intl. Conf. on Indoor Positioning and Indoor Navigation (IPIN)

[121] Stahlke M., Feigl T., Kram S., Eskofier B. M., Mutschler C. (2023): Uncertainty-based Fingerprinting Model Monitoring for Radio Localization (Extended Version). (2023) In: IEEE Journal of Indoor and Seamless Positioning and Navigation (J-ISPIN)

[120] Wagner F., Bärmann A., Liers F., Weissenbäck M. (2023): Improving Quantum Computation by Optimized Qubit Routing. In: Journal of Optimization Theory and Applications

[119] Wissing J., Scheele S. (2023): Boosting Energy Efficient Machine Learning in Smart Sensor Systems. In: Sensor and Measurement Science International (SMSI 2023).

[118] Yammine G., Kontes G., Franke N., Plinge A., Mutschler C. (2023): Efficient Beam Search for Initial Access Using Collaborative Filtering. In: 2023 IEEE Wireless Communications and Networking Conference (WCNC)

[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.

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