Research



Use Cases


n the Hospital, Portrait Shot of Topless Female Patient Undergoing Mammogram Screening Procedure. Healthy Young Female Does Cancer Preventive Mammography Scan. Modern Hospital with High Tech Machines.
(c) Adobe Stock 212622163

Team:  Can Aykul, Jonas Wallat, Dr. Cameron Pierson, Prof. Dr. Maria-Esther Vidal

In the project "Breast Cancer Network Hannover", which focuses on breast cancer, Prof. Tjoung-Won Park-Simon and Dr. Thilo Dörk-Bousset from the Department of Gynaecology (MHH) are cooperating with the Leibniz AI Lab to identify factors for therapeutic success in patients diagnosed with breast cancer. For this purpose, standardized data of about 5000 patients of the regional network "Network Breast Cancer" will be analysed. In a first step, medical history data of the patient and her family, tumor characteristics, therapy data, data on follow-up examinations and survival, genetic information as well as socioeconomic data of the patient will be integrated to enable a comprehensive analysis. Special emphasis will be placed on the association of socioeconomic aspects such as education and migration background with therapeutic success. Another focus is on the identification of sub-populations of patients based on the success of different therapy options to enable targeted, personalized therapy. In particular, the project aims to give optimized suggestions which patients will benefit more from neo-adjuvant therapy and which patients will benefit more from surgery.

While the current approach to predict relapse probability is using a logistic regression model, we aim to expand to more involved models such as decision trees, random forests, neural networks and introducing existing domain knowledge on breast cancer using knowledge graphs. Hence, a knowledge graph will be modeled and populated based on obtained patient data. Building upon benchmark knowledge graph embedding models such as TransE [1], ComplEx [2] and RotatE [3] a framework that can incorporate existing biomedical ontologies (e.g. Gene Ontology) will be developed and thence relapse probability of a treatment will be predicted. On top of this, in order to assist decision making of the clinician, a drug-drug interaction knowledge graph will be used to learn latent semantic representations of drugs/medications to predict potentially harmful drug interactions that may occur if a patient is required to take multiple medications simultaneously. While introducing more complex models, we will need to balance model performance and interpretability of our approaches. Especially with the use of neural networks, we will use existing interpretability techniques such as LIME [4] and Shapley Values [5].

Given the ethical implications of developing and using machine learning models as healthcare decision support systems, we use this opportunity to evaluate an existing ethical framework in parallel to developing the solutions described above: The rapid and increasing development of machine learning in healthcare applications (ML-HCAs) requires ethical examination to assess the impact of novel medical devices and methods on patient and society. It is imperative that such ethical examinations are made to elucidate the associated ethical considerations, whether known or new. As medical technology advances so must the concurrent ethical examination of use and scope, such as the nature of system application, the data underwriting said system, and impacts to patient, society, and healthcare. Such ethical examination is imperative to avoid embedding or amplifying biases into machine learning tools used in healthcare.

While ethical frameworks have been proposed (e.g., Floridi & Strait, 2020; Saltz & Dewar, 2019), Char and colleagues (2020) develop a framework is thoroughly and clearly constructed from pre-existing literature to systematically identify ethical considerations specific to ML-HCAs. While some argue for an ‘ethicist-as-designer’ auditing the developmental process of machine learning tools (van Wynsberghe & Robbins, 2014), there is increased benefit of implementation of such an ethical identification framework with a research team. As has been suggested elsewhere (e.g, Armstrong, 2017; Blay et al., 2012), the development of AI in medicine ought to be interdisciplinary and/or by co-design. Therefore, implementation of Char and colleague’s (2020) framework with a research team provides the benefit of auditing (i.e., van Wynsberghe & Robbins, 2014) from the investigators of this study, while also promoting ethical consideration identification and management in situ of the research group. Such implementation would promote the ethical development of ML-HCAs. The proposed framework, however, has yet to be independently evaluated. Thus, we aim to evaluate Char and colleagues’ (2020) pipeline framework within the context of a research group seeking to develop machine learning techniques to identify biomarkers of breast cancer patients to predict patient success to chemotherapy treatment.

References:

[1] Bordes, Antoine, et al. "Translating embeddings for modeling multi-relational data." Advances in neural information processing systems 26 (2013).
[2] Trouillon, Théo, et al. "Complex embeddings for simple link prediction." International conference on machine learning. PMLR, 2016.
[3] Sun, Zhiqing, et al. "Rotate: Knowledge graph embedding by relational rotation in complex space." arXiv preprint arXiv:1902.10197 (2019).
[4] M. Ribeiro - “Why Should I Trust You?” Explaining the Predictions of Any Classifier - https://dl.acm.org/doi/pdf/10.1145/2939672.2939778
[5] S. Lundberg - A Unified Approach to Interpreting Model Predictions -
https://www.semanticscholar.org/paper/A-Unified-Approach-to-Interpreting-Model-Lundberg-Lee/442e10a3c6640ded9408622005e3c2a8906ce4c2 

Happy doctor supporting positive child with cancer wearing headscarf
(c) Adobe Stock 226717464

Team: Michelle Tang, PD Dr. Anke Bergmann

B-progenitor acute lymphoblastic leukemia (B-ALL) is the most common pediatric malignancy. Next Generation Sequencing (NGS) technologies have been incorporated into routine diagnostics. Among them, the cost-effective targeted RNA sequencing is particularly appealing. We analyzed targeted RNA sequencing on ~1,500 pediatric ALL patients from the German pediatric ALL study groups.  We combine UMAP (Uniform Manifold Approximation and Projection) and supervised machine learning algorithms to build an interactive tool for visualization and prediction of diagnostic subgroups. We explore a variety of machine learning techniques including gene network informed neural networks to build our predictive model. The tool helps to stratify patients without aberrant fusion or aneudiploidy, validate conventional diagnostic methods and discover new subgroups. In the future, we plan to expand such AI assisted diagnostic tool to more clinical , transcriptomic and epigenetic data. The proposed workflow will greatly complement the current diagnostic routine, provide better treatment options for patients and pave the way for personalized oncology. 

Man with Parkinsons disease Team: Soumyadeep Roy, Salomon Kabongo Kabenamualu, Prof. Niloy Ganguly, Prof. Dr. Helge Frieling, Dr. Stefanie Mücke, Dominik Wolff 
 
In the project "Big Data in Psychiatric Disorders", Prof. Dr. Helge Frieling of the Department of Psychiatry, Social Psychiatry and Psychotherapy (MHH) is working together with the Leibniz AI Lab on the focus areas of schizophrenia and neurodegenerative diseases. In the first sub-project, genetic information from around 50,000 patients diagnosed with schizophrenia is being evaluated using artificial intelligence in order to identify possible subtypes. The hypothesis here is that schizophrenia as a phenotype is based on a wide variety of causes that require differentiated diagnosis and therapy. We will focus on this project and have completed the data request formalities. However, we are yet to receive the data from NIMH.  
 
Therefore, we are working on patient subtyping of Parkinson‘s disease, a neuro-degenerative disease, using clinical and genetic data. Most works focus of patient subtyping of Parkinson Disease (PD) based on motor symptoms and typically the population consider older population (above the age of 60 years). Recently, researchers also include non-motor symptoms to define patient subtypes because non-motor symptoms often precede the development of classical motor signs and contribute significantly to overall prognosis. Specifically, we plan to identify patient subtypes in younger patients with PD (below the age of 60 years) in terms of clinical and genetic data. We are also interested in patients with comorbodities like schizophrenia, severe depression. We have developed a binary classification model for predicting whether a patient has PD or not. We use the learnt decision tree to determine the patient subtypes; this is the first approach we take to overcome the limitation that the ground truth patient subtype labels are not available. Currently, we are performing a characterisation study of PD patient subtypes in terms of clinical data. In future, we plan to further characterize these clinical patient subtypes in terms of their genotype data. Along the same lines, we are currently exploring a second approach for patient subtyping where we directly cluster the patients in terms of their genotype data (SNP data).
Doctors in ICU discussing

Team: Leonie Basso, Jingge Xiao, Seham Nasr, Dr. Zhao Ren, Prof. Antje Wulff, PD. Dr. Thomas Jack, PD. Dr. Henning Rathert, Marcel Mast, Prof. Michael Marschollek,  Prof. Wolfgang Nejdl


In the project of “Pediatric Intensive Care Unit (PICU) use case”, Professor Antje Wulff, PD Dr. Thomas Jack, PD. Dr. Henning Rathert, Marcel Mast and Prof. Michael Marschollek from Hannover Medical School are working with the Leibniz AI Lab on the target of automatically detecting organ dysfunction in PICUs. Due to immediate decision-making with high risk and stress at a high level for clinicians in ICU wards, a data-intensive environment, it is essential to develop automatic decision-making models with the state-of-the-art machine learning and deep learning topologies; thus, promoting the development of real-time models for making decisions and mitigating the pressure of clinicians. More importantly, there are several difficulties during the decision-making procedure in PICUs: i) Different diseases dominate specific age groups from 0 to 18 years, and ii) normative values spread widely in different age groups. However, there are only a few research studies working on analysis of the data collected from PICU wards. In this regard, the project of PICU use case focuses on predicting organ dysfunction based on PICU data. There are two major branches that have been planned in this project. In the following, the two branches will be introduced.


i) We will focus on processing the clinical data which mainly contains vital signs (e.g., respiratory rate, heart rate, etc), laboratory parameters (e.g., leucocytes), and patient data (e.g., height, weight, etc).


ii) A new database of the waveform data (e.g., electrocardiogram) from the bedside monitors will be collected. The benchmark will be set up when the data is collected and pre-processed (e.g., anonymization) and a series of machine learning and deep learning approaches will be applied.


In summary, the research of this project is expected to facilitate related research studies in the applications of AI in PICU wards.


COVID-19, a disease caused by SARS-CoV2, can take many different forms, ranging in clinical severity from mild or asymptomatic illness to acute conditions such as ARDS (acute respiratory distress syndrome) and death. Several studies have already shown that, in addition to demographic factors and pre-existing conditions, genetic predisposition may play an important role in disease development. To better understand the pathophysiology and progression of COVID-19, clinicians and researchers at Hannover Medical School (MHH) have been collecting patient samples and data in the COVID-19 Biobank funded by the Lower Saxony Ministry of Science and Culture (MWK) since the beginning of the pandemic.


Broad molecular characterizations have been performed on the collected biospecimens, particularly on material from patients with severe clinical courses requiring intensive care and respiratory support. These global analyses include sequencing of the patient genome, gene expression, and the methylation state of specific bases in the genome (epigenome). These data are complemented by high-resolution optical analyses of structural DNA variants that may be associated with increased disease risk. In addition, a broad clinical dataset on all patients was collected by the Hannover Unified Biobank (HUB) in collaboration with the Pneumology Department of the MHH, which includes information on COVID-19 patients' previous disease, disease severity, therapeutic measures, complications, and disease outcome.


To bring together this extensive collection of molecular and clinical data, already comprising over 14 TB in its raw state, in an integrative analysis, the HUB is collaborating with scientists from the L3S Future Laboratory and Prof. Yang Li from the Helmholtz Centre for Infection Research (HZI). The integrative data analysis aims to bring together the different data layers and identify prognostic molecular markers or early disease patterns associated with further disease progression.



Future Lab Seminars



Publications


[2024] [2023] [2022] [2021] [2020] [2019] [2018] [2017] [2016] [2015] [2014]

2024

  • M{ü}ntefering, F., Adhisantoso, Y. G., Chandak, S., Ostermann, J., Hernaez, M., and Voges, J. (2024)Genie: the first open-source ISO/IEC encoder for genomic data, Communications Biology 7.
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  • Cong, Y., Xu, M., Simon, C., Chen, S., Ren, J., Xie, Y., Rosenhahn, B., Xiang, T., and He, S. (2024)FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing. In International Conference on Learning Representations (ICLR).
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  • Lange, A., Xu, R., K{ä}ding, M., Marx, S., and Ostermann, J. (2024)Matched Filter for Acoustic Emission Monitoring in Noisy Environments: Application to Wire Break Detection (accepted), acoustics, Special Issue: Advances in Industrial and Research Applications of Acoustic Emission Testing.
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  • Hinrichs, R. (2024)Kompression der Erregungsmuster von Cochlea-Implantaten, VDI Verlag.
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  • Norrenbrock, T., Rudolph, M., and Rosenhahn, B. (2024)Q-SENN: Quantized Self-Explaining Neural Networks. In AAAI Technical Track on Safe, Robust and Responsible AI, pp. 21482–21491.
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  • Xu, R., Beltran-Gutierrez, R. E., K{ä}ding, M., Lange, A., Marx, S., and Ostermann, J. (2024)Frequency dependent amplitude response of different couplant materials for mounting piezoelectric sensors, NDT \& E International 141.
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  • Kluger, F., and Rosenhahn, B. (2024)PARSAC: Accelerating Robust Multi-Model Fitting with Parallel Sample Consensus. In AAAI.
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  • Mahlau, Y., Schubert, F., and Rosenhahn, B. (2024)Mastering Zero-Shot Interactions in Cooperative and Competitive Simultaneous Games. In Proceedings of the 41st International Conference on Machine Learning (ICML).
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  • Chen, Y.-H., Gao, Z.-L., Benjak, M., and Peng, W.-H. (2024)Response to Call for Learning-Based Video Codecs for Study of Quality Assessment by NYCU and LUH, 14th Meeting of ISO/IEC JTC 1/SC 29/AG 5 Document m66163.
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  • Reinders, C., Yang, M. Y., and Rosenhahn, B. (2024)Two Worlds in One Network: Fusing Deep Learning and Random Forests for Classification and Object Detection, Volunteered Geographic Information.
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  • Hinrichs, R., Gerkens, K., Lange, A., and Ostermann, J. (2024)Blind Extraction of Guitar Effects Through Blind System Inversion and Neural Guitar Effect Modeling, EURASIP Journal on Audio, Speech, and Music Processing.
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  • Chen, Y.-H., Chen, C.-W., Gao, Z.-L., Benjak, M., and Peng, W.-H. (2024)Results on the Bit Rate Fitting for MaskCRT, 15th Meeting of ISO/IEC JTC 1/SC 29/AG 5 Document m67455.
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  • Wallat, J., Jatowt, A., and Anand, A. (2024)Temporal Blind Spots in Large Language Models, ACM International Conference on Web Search and Data Mining (WSDM) (Proceeding, no out yet, Ed.) 17.
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  • Kluger, F., Brachmann, E., Yang, M. Y., and Rosenhahn, B. (2024)Robust Shape Fitting for 3D Scene Abstraction, IEEE Transactions on Pattern Analysis and Machine Intelligence.
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  • Benjak, M., and Ostermann, J. (2024)Comparison between LCEVC and VVC, 15th Meeting of ISO/IEC JTC 1/SC 29/WG 4 Document m67212.
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  • Kruse, M., Rudolph, M., Woiwode, D., and Rosenhahn, B. (2024)SplatPose \& Detect: Pose-Agnostic 3D Anomaly Detection. In Computer Vision and Pattern Recognition Workshops (CVPRW).
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  • Chen, S., Xu, M., Ren, J., Cong, Y., He, S., Xie, Y., Sinha, A., Luo, P., Xiang, T., and Perez-Rua, J.-M. (2024)GenTron: Delving Deep into Diffusion Transformers for Image and Video Generation. In Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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  • Cong, Y., Xu, M., Simon, C., Chen, S., Ren, J., Xie, Y., Perez-Rua, J.-M., Rosenhahn, B., Xiang, T., and He, S. (2024)FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing. In International Conference on Learning Representations (ICLR).
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  • Chen, Y.-H., Cheng, C.-W., Benjak, M., and Peng, W.-H. (2024)Progress report on MaskCRT, 15th Meeting of ISO/IEC JTC 1/SC 29/AG 5 Document m66976.
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  • Maier, H. B., Neyazi, A., Bundies, G. L., Meyer-Bockenkamp, F., Bleich, S., Pathak, H., Ziert, Y., Neuhaus, B., M{ü}ller, F.-J., Pollmann, I., Illig, T., M{ü}cke, S., M{ü}ller, M., M{ö}ller, B. K., Oeltze-Jafra, S., Kacprowski, T., Voges, J., M{ü}ntefering, F., Scheiber, J., Reif, A., Aichholzer, M., Reif-Leonhard, C., Schmidt-Kassow, M., Hegerl, U., Reich, H., Unterecker, S., Weber, H., Deckert, J., B{ö}ssel-Debbert, N., Grabe, H. J., Lucht, M., and Frieling, H. (2024)Validation of the predictive value of BDNF -87 methylation for antidepressant treatment success in severely depressed patients—a randomized rater-blinded trial, Trials 25.
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  • Chen, Y.-H., Gao, Z.-L., Yao, Y.-C., Ho, K.-W., Benjak, M., and Peng, W.-H. (2024)Bitstream Generation and Bit Rate Fitting Results of MaskCRT for CVQM HD Sequences, 15th Meeting of ISO/IEC JTC 1/SC 29/AG 5 Document m68079.
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2023

  • Ekaputra, F. J., Llugiqi, M., Sabou, M., Ekelhart, A., Paulheim, H., Breit, A., Revenko, A., Waltersdorfer, L., Farfar, K. E., and Auer, S. (2023)Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG. In The Semantic Web - 20th International Conference, {ESWC} 2023, Hersonissos, Crete, Greece, May 28 - June 1, 2023, Proceedings (Pesquita, C., Jim{{é}}nez{-}Ruiz, E., McCusker, J. P., Faria, D., Dragoni, M., Dimou, A., Troncy, R., and Hertling, S., Eds.), pp. 372–389, Springer.
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  • Wehrbein, T., Rosenhahn, B., Matthews, I., and Stoll, C. (2023)Personalized 3D Human Pose and Shape Refinement. In International Conference on Computer Vision Workshops (ICCVW).
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  • Olson, C., Wagner, L., and Dockhorn, A. (2023)Evolutionary Optimization of Baba Is You Agents. In 2023 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8.
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  • Basso, L., Ren, Z., and Nejdl, W. (2023)Efficient ECG-Based Atrial Fibrillation Detection via Parameterised Hypercomplex Neural Networks. In 2023 31st European Signal Processing Conference (EUSIPCO), pp. 1375–1379.
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  • Antoniou, G., and Batsakis, S. (2023)Defeasible Reasoning with Large Language Models–Initial Experiments and Future Directions. In Proceedings of the17th International Rule Challenge, RuleML 2023, p. 7687.
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  • Krause, L. M. K., Manderfeld, E., Gnutt, P., Vogler, L., Wassick, A., Richard, K., Rudolph, M., Hunsucker, K. Z., Swain, G. W., Rosenhahn, B., and Rosenhahn, A. (2023)Semantic Segmentation for Fully Automated Macrofouling Analysis on Coatings after Field Exposure, Biofouling 39, 64–79.
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  • Adhisantoso, Y. G. (2023)Cross-check of Philips’s Response to Core Experiment for Annotation Data Indexing using B-Tree m62224, ISO/IEC JTC 1/SC 29/WG 8.
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  • Ren, Z., Nguyen, T. T., Chang, Y., and Schuller, B. W. (2023)Fast yet effective speech emotion recognition with self-distillation. In ICASSP.
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  • Hachmann, H., and Rosenhahn, B. (2023)Color-aware Deep Temporal Backdrop Duplex Matting System. In MMSys ’23: Proceedings of the 14th ACM Multimedia Systems Conference.
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  • Benjamins, C., Eimer, T., Schubert, F., Mohan, A., D{ö}hler, S., Biedenkapp, A., Rosenhahn, B., Hutter, F., and Lindauer, M. (2023)Contextualize Me - The Case for Context in Reinforcement Learning, Transactions on Machine Learning Research.
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  • Tang, M., Antic, Z., Fardzadeh, P., Schröder, C., Pietzsch, S., Lentes, J., Hofmann, W., von Wolfggersdorff, L., Zimmerman, M., Horstmann, M., Nejdl, W., Cario, G., Stanulla, M., and Bergmann, A. K. (2023)A machine learning based clinical platform for cancer subtyping and data integration in hematological malignancies, Annals of Oncology 34, S550.
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  • Rumberg, L., Gebauer, C., Ehlert, H., Wallbaum, M., L{ü}dtke, U., and Ostermann, J. (2023)Uncertainty Estimation for Connectionist Temporal Classification Based Automatic Speech Recognition. In Accepted to Interspeech 2023.
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  • Hussein, H., Farfar, K. E., Oelen, A., Karras, O., and Auer, S. (2023)Increasing Reproducibility in Science by Interlinking Semantic Artifact Descriptions in a Knowledge Graph. In Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration - 25th International Conference on Asia-Pacific Digital Libraries, {ICADL} 2023, Taipei, Taiwan, December 4-7, 2023, Proceedings, Part {II} (Goh, D. H.- }Lian, Chen, S.- }Jiun, and Tuarob, S., Eds.), pp. 220–229, Springer.
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  • Rudolph, M., Wehrbein, T., Rosenhahn, B., and Wandt, B. (2023)Asymmetric Student-Teacher Networks for Industrial Anomaly Detection. In Winter Conference on Applications of Computer Vision (WACV).
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  • Fathalla, S., Lange, C., and Auer, S. (2023)An Upper Ontology for Modern Science Branches and Related Entities. In The Semantic Web - 20th International Conference, {ESWC} 2023, Hersonissos, Crete, Greece, May 28 - June 1, 2023, Proceedings (Pesquita, C., Jim{{é}}nez{-}Ruiz, E., McCusker, J. P., Faria, D., Dragoni, M., Dimou, A., Troncy, R., and Hertling, S., Eds.), pp. 436–453, Springer.
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  • Schier, M., Reinders, C., and Rosenhahn, B. (2023)Learned Fourier Bases for Deep Set Feature Extractors in Automotive Reinforcement Learning. In 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), p. to appear.
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  • Wallat, J., Beringer, F., Anand, A., and Anand, A. (2023)Probing BERT for Ranking Abilities. In Advances in Information Retrieval - 45th European Conference on Information Retrieval, {ECIR} 2023, Dublin, Ireland, April 2-6, 2023, Proceedings, Part {II} (Kamps, J., Goeuriot, L., Crestani, F., Maistro, M., Joho, H., Davis, B., Gurrin, C., Kruschwitz, U., and Caputo, A., Eds.), pp. 255–273, Springer.
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  • Adhisantoso, Y. G., Voges, J., Rohlfing, C., Tunev, V., Ohm, J.-R., and Ostermann, J. (2023)GVC: Efficient Random Access Compression for Gene Sequence Variations. In BMC Bioinformatics, p. 13.
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  • Adhisantoso, Y. G., Voges, J., and Ostermann, J. (2023)PEKORA: High-Performance 3D Genome Reconstruction Using K-th Order Spearman’s Rank Correlation Approximation [Talk]. In ISMB/ECCB 2023.
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  • Rosenhahn, B. (2023)Optimization of Sparsity-Constrained Neural Networks as a Mixed Integer Linear Program, Journal of Optimization Theory and Applications 1–24.
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  • Dockhorn, A., Eberhardinger, M., Loiacono, D., Liebana, D. P., and Veltkamp, R. (2023)Pokegen, Human-Game AI Interaction (Dagstuhl Seminar 22251) 12, 39–42.
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  • Kaiser, T., Reinders, C., and Rosenhahn, B. (2023)Compensation Learning in Semantic Segmentation. In Computer Vision and Pattern Recognition Workshops (CVPRW).
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  • Auer, S., Barone, D. A. C., Bartz, C., Cortes, E. G., Jaradeh, M. Y., Karras, O., Koubarakis, M., Mouromtsev, D., Pliukhin, D., Radyush, D., Shilin, I., Stocker, M., and Tsalapati, E. (2023, March)SciQA benchmark: Dataset and {RDF} dump (Version 5), Zenodo.
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  • Lee, C.-S., Wang, M.-H., Chen, C.-Y., Yang, F.-J., and Dockhorn, A. (2023)Genetic Assessment Agent for High-School Student and Machine Co-Learning Model Construction on Computational Intelligence Experience. In 2023 IEEE Congress on Evolutionary Computation, pp. 1–8.
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  • Awiszus, M., Dockhorn, A., Hoover, A. K., Liapis, A., Lucas, S. M., Eladhari, M. P., Schrum, J., and Volz, V. (2023)Language Models for Procedural Content Generation, Human-Game AI Interaction (Dagstuhl Seminar 22251) 12, 34–37.
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  • Nandy, A., Kapadnis, M. N., Goyal, P., and Ganguly, N. (2023)CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural Text. In The 2023 Conference on Empirical Methods in Natural Language Processing.
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  • Roy, S., Wallat, J., Sundaram, S. S., Nejdl, W., and Ganguly, N. (2023)GENEMASK: Fast Pretraining of Gene Sequences to Enable Few-Shot Learning. In Frontiers in Artificial Intelligence and Applications, pp. 2002–2009.
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  • Adhisantoso, Y. G., and Voges, J. (2023)Cross-check of M62859 Results on Updated CE Results for Annotation Data Indexing Using B-Tree, ISO/IEC JTC 1/SC 29/WG 8.
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  • Gebauer, C., Rumberg, L., Ehlert, H., L{ü}dtke, U., and Ostermann, J. (2023)Exploiting Diversity of Automatic Transcripts from Distinct Speech Recognition Techniques for Children’s Speech. In Accepted to Interspeech 2023.
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  • Poker, Y., von Hardenberg, S., Hofmann, W., Tang, M., Baumann, U., Schwerk, N., Wetzke, M., Lindenthal, V., Auber, B., Schlegelberger, B., Ott, H., von Bismarck, P., Viemann, D., Dressler, F., Klemann, C., and Bergmann, A. K. (2023)Systematic genetic analysis of pediatric patients with autoinflammatory diseases, Frontiers in Genetics, Frontiers Media {SA} 14.
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  • Cook, M., Awiszus, M., Cakmak, D., Denisova, A., Dockhorn, A., Harteveld, C., Liapis, A., Eladhari, M. P., Liebana, D. P., Rombout, L., and Thompson, T. (2023)AI for Romantic Comedies, Human-Game AI Interaction (Dagstuhl Seminar 22251) 12, 37–39.
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  • Liapis, A., Awiszus, M., Champandard, A. J., Cook, M., Denisova, A., Dockhorn, A., Thompson, T., and Zhu, J. (2023)Artificial Intelligence for Audiences, Human-Game AI Interaction (Dagstuhl Seminar 22251) 12, 50–54.
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  • Kuhnke, F., and Ostermann, J. (2023)Domain Adaptation for Head Pose Estimation Using Relative Pose Consistency, IEEE Transactions on Biometrics, Behavior, and Identity Science.
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  • Safikhani, P., and Broneske, D. (2023)Enhancing AutoNLP with fine-tuned BERT models: An evaluation of text representation methods for AutoPyTorch., International Conference on Machine Learning Techniques and NLP 13.
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  • Avetisyan, H., Safikhani, P., and Broneske, D. (2023)Laughing Out Loud – Exploring AI-Generated and Human-Generated Humor, International Conference on Soft Computing, Artificial Intelligence and Applications (SCAI 2023).
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  • L{ü}dtke, U., Bornman, J., de Wet, F., Heid, U., Ostermann, J., Rumberg, L., der Linde, J. V., and Ehlert, H. (2023)Multidisciplinary Perspectives on Automatic Analysis of Children’s Language Samples: Where Do We Go from Here?, Folia Phoniatrica et Logopaedica 75, 1–12.
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  • Rosenhahn, B., and Osborne, T. (2023)Monte Carlo Graph Search for Quantum Circuit Optimization (Accepted), Physical Review A.
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  • Brockmann*, J. T., Rudolph*, M., Rosenhahn, B., Wandt, B., and equal contribution), (*. (2023)The voraus-AD Dataset for Anomaly Detection in Robot Applications, Transactions on Robotics.
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  • W{ö}rz, N., Woiwode, D., Sondheim, J., Behrens, D., Rudy, D., and Gl{ü}cksklee, T. (2023)Pflanzenforschung an Bord der ISS, BIOspektrum 29 29, 557.
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  • Roy, S., Wallat, J., Sundaram, S. S., Nejdl, W., and Ganguly, N. (2023)GeneMask: Fast Pretraining of Gene Sequences to Enable Few-Shot Learning, CoRR abs/2307.15933.
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  • D’Souza, J., Hrou, M., and Auer, S. (2023)Evaluating Prompt-Based Question Answering for Object Prediction in the Open Research Knowledge Graph. In Database and Expert Systems Applications - 34th International Conference, {DEXA} 2023, Penang, Malaysia, August 28-30, 2023, Proceedings, Part {I} (Strauss, C., Amagasa, T., Kotsis, G., Tjoa, A. M., and Khalil, I., Eds.), pp. 508–515, Springer.
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  • Safikhani, P., Avetisyan, H., F{ö}ste-Eggers, D., and Broneske, D. (2023)Automated occupation coding with hierarchical features: A data-centric approach to classification with pre-trained language models., Discover Artificial Intelligence.
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  • Xu, L., Dockhorn, A., and Perez-Liebana, D. (2023)Elastic Monte Carlo Tree Search, IEEE Transactions on Games.
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  • Rabby, G., D’Souza, J., Oelen, A., Dvorackova, L., Sv{{á}}tek, V., and Auer, S. (2023)Impact of {COVID-19} research: a study on predicting influential scholarly documents using machine learning and a domain-independent knowledge graph, J. Biomed. Semant. 14, 18.
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  • Chang, Y., Ren, Z., Nguyen, T. T., Qian, K., and Schuller, B. W. (2023)Knowledge transfer for on-device speech emotion recognition with neural structured learning. In ICASSP.
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  • Cong, Y., Yang, M., and Rosenhahn, B. (2023)RelTR: Relation Transformer for Scene Graph Generation, IEEE transactions on pattern analysis and machine intelligence (TPAMI).
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  • D’Souza, J., Mulang’, I. O., and Auer, S. (2023)Ranking facts for explaining answers to elementary science questions, Nat. Lang. Eng. 29, 228–253.
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  • Pestel-Schiller, U., Busch, J., Meinicke, P.-R., and Ostermann, J. (2023)Gain Adapted Quantization in HEVC Coding Applied to Drone Remote Sensing. In 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS).
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  • Glandorf*, P., Kaiser*, T., Rosenhahn, B., and (*contributed equally). (2023)HyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive Regularization. In International Conference on Computer Vision Workshops (ICCVW).
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  • Mohan, A., Benjamins, C., Wienecke, K., Dockhorn, A., and Lindauer, M. (2023)AutoRL Hyperparameter Landscapes. In Second International Conference on Automated Machine Learning, pp. 1–14.
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  • Xu, R., Lange, A., K{ä}ding, M., Marx, S., and Ostermann, J. (2023)Energy spectral analysis of wire breaks in post-tensioned tendons for wind turbines. In 19th EAWE PhD Seminar on Wind Energy, pp. 204–207.
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  • Dockhorn, A., and Kruse, R. (2023)State and Action Abstraction for Search and Reinforcement Learning Algorithms, Artificial Intelligence in Control and Decision-making Systems 181–198.
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  • M{ü}ntefering, F., Ostermann, J., and Voges, J. (2023)BACON: Bacterial Clone Recognition from Metagenomic Sequencing Data, AICPM 2023.
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  • Xie, H.-S., Chen, Y.-H., Peng, W.-H., Benjak, M., and Ostermann, J. (2023)Rate Adaptation for Learned Two-layer B-frame Coding without Signaling Motion Information. In IEEE International Conference on Visual Communications and Image Processing (VCIP).
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  • Giglou, H. B., D’Souza, J., and Auer, S. (2023)LLMs4OL: Large Language Models for Ontology Learning. In The Semantic Web - {ISWC} 2023 - 22nd International Semantic Web Conference, Athens, Greece, November 6-10, 2023, Proceedings, Part {I} (Payne, T. R., Presutti, V., Qi, G., Poveda{-}Villal{{ó}}n, M., Stoilos, G., Hollink, L., Kaoudi, Z., Cheng, G., and Li, J., Eds.), pp. 408–427, Springer.
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  • Auer, S., Barone, D. A. C., Bartz, C., Cortes, E. G., Jaradeh, M. Y., Karras, O., Koubarakis, M., Mouromtsev, D., Pliukhin, D., Radyush, D., Shilin, I., Stocker, M., and Tsalapati, E. (2023, March)SciQA benchmark: Dataset and {RDF} dump (Version 1.0.1), Zenodo.
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  • Papadakis, E., Baryannis, G., Batsakis, S., Adamou, M., Huang, Z., and Antoniou, G. (2023)ADHD-KG: A Knowledge Graph of Attention Deficit Hyperactivity Disorder, Health Information Science and Systems.
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  • Jaradeh, M. Y., Singh, K., Stocker, M., Both, A., and Auer, S. (2023)Information extraction pipelines for knowledge graphs, Knowl. Inf. Syst. 65, 1989–2016.
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  • Ganguly, N., Fazlija, D., Badar, M., Fisichella, M., Sikdar, S., Schrader, J., Wallat, J., Rudra, K., Koubarakis, M., Patro, G. K., Amri, W. Z. E., and Nejdl, W. (2023)A Review of the Role of Causality in Developing Trustworthy AI Systems.
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  • Xiao, J., Basso, L., Nejdl, W., Ganguly, N., and Sikdar, S. (2023)IVP-VAE: Modeling EHR Time Series with Initial Value Problem Solvers, arXiv preprint arXiv:2305.06741.
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  • Lange, A., K{ä}ding, M., Xu, R., Marx, S., and Ostermann, J. (2023)Semi-supervised learning for acoustic emission monitoring of tendons in prestressed concrete bridges. In 14th International Workshop on Structural Health Monitoring (IWSHM).
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  • Roy, S., Ganguly, N., Sural, S., and Rudra, K. (2023)Interpretable Clinical Trial Search using Pubmed Citation Network. In 2023 IEEE International Conference on Digital Health (ICDH), pp. 328–338.
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  • Rohlfing, C., Meyer, T., Schneider, J., and Voges, J. (2023)Python Wrapper for Context-based Adaptive Binary Arithmetic Coding. In 2023 IEEE International Conference on Visual Communications and Image Processing (VCIP).
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  • Kabongo, S., D’Souza, J., and Auer, S. (2023)Zero-Shot Entailment of Leaderboards for Empirical {AI} Research. In {ACM/IEEE} Joint Conference on Digital Libraries, {JCDL} 2023, Santa Fe, NM, USA, June 26-30, 2023, pp. 237–241, {IEEE}.
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  • Schubert, F., Benjamins, C., D{ö}hler, S., Rosenhahn, B., and Lindauer, M. (2023)POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning, Transactions on Machine Learning Research.
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  • Kuhnke, F. (2023)Unsupervised Domain Adaptation for Real-World Head Pose Estimation from Synthetic Data 144.
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  • Schier, M., Reinders, C., and Rosenhahn, B. (2023)Deep Reinforcement Learning for Autonomous Driving Using High-Level Heterogeneous Graph Representations. In International Conference on Robotics and Automation (ICRA), p. to appear.
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  • Gebauer, C., Rumberg, L., and Ostermann, J. (2023)Pronunciation Modeling for Children’s Speech. In Elektronische Sprachsignalverarbeitung (ESSV), pp. 79–86.
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  • Zhu, J., Awiszus, M., Cook, M., Dockhorn, A., Eberhardinger, M., Loiacono, D., Lucas, S. M., Matran-Fernandez, A., Liebana, D. P., Thompson, T., and Veltkamp, R. (2023)Explainable AI for Games, Human-Game AI Interaction (Dagstuhl Seminar 22251) 12, 73–75.
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  • Hachmann, H., and Rosenhahn, B. (2023)Human Spine Motion Capture using Perforated Kinesiology Tape. In Computer Vision and Pattern Recognition Workshops (CVPRW).
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  • Hinrichs, R., Sitcheu, A. J. Y., and Ostermann, J. (2023)Continuous Sign-Language Recognition using Transformers and Augmented Pose Estimation. In Proceedings of the International Conference on Pattern Recognition Applications and Methods.
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  • Hinrichs, R., Bilsky, J., and Ostermann, J. (2023)Vector-Quantized Feedback Recurrent Autoencoders for the Compression of the Stimulation Patterns of Cochlear Implants at Zero Delay. In Proceedings of the 24th International Conference on Digital Signal Processing.
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  • Cong, Y., Yi, J., Rosenhahn, B., and Yang, M. (2023)SSGVS: Semantic Scene Graph-to-Video Synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.
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  • Ehlert, H., Beaulac, E., Wallbaum, M., Gebauer, C., Rumberg, L., Ostermann, J., and L{ü}dtke, U. (2023)Collecting and Annotating Natural Child Speech Data – Challenges and Interdisciplinary Perspectives. In Elektronische Sprachsignalverarbeitung (ESSV), pp. 72–78.
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2022

  • Chen, M.-H., Mudgal, G., Chen, W.-F., and Wachsmuth, H. (2022)Investigating the argumentation structures of EFL learners from diverse language backgrounds. In EUROCALL.
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  • Reinders, C., Schubert, F., and Rosenhahn, B. (2022)ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing. In Arxiv Preprint.
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  • Schäfer, J., Tang, M., Luu, D., Bergmann, A. K., and Wiese, L. (2022)Graph4Med: a web application and a graph database for visualizing and analyzing medical databases, BMC bioinformatics (Zandomeneghi, S., Ed.) 23.
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  • Hvarfner, C., Stoll, D., Souza, A., Nardi, L., Lindauer, M., and Hutter, F. (2022)piBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization. In 10th International Conference on Learning Representations, ICLR’22, pp. 1–30.
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  • Awiszus, M., Schubert, F., and Rosenhahn, B. (2022)Wor(l)d-GAN: Towards Natural Language Based PCG in Minecraft, IEEE Transactions on Games.
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  • Hinrichs, R., Gerkens, K., Lange, A., and Ostermann, J. (2022)Classification of Guitar Effects and Extraction of their Parameter Settings from Instrument Mixes Using Convolutional Neural Networks. In EvoMUSART 2022.
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  • Sharma, S., Nayak, T., Bose, A., Meena, A. K., Dasgupta, K., Ganguly, N., and Goyal, P. (2022){FinRED}: A Dataset for Relation Extraction in Financial Domain. In Companion Proceedings of the Web Conference 2022, {ACM}.
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  • Patro, G. K., Jana, P., Chakraborty, A., Gummadi, K. P., and Ganguly, N. (2022)Scheduling Virtual Conferences Fairly: Achieving Equitable Participant and Speaker Satisfaction. In Proceedings of the {ACM} Web Conference 2022, {ACM}.
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  • Dong, N., Mücke, S., and Khosla, M. (2022)MuCoMiD: A Multitask Graph Convolutional Learning Framework for miRNA-Disease Association Prediction, IEEE/ACM Transactions on Computational Biology and Bioinformatics 19, 3081–3092.
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  • Dong, T. N., Schrader, J., M{ü}cke, S., and Khosla, M. (2022)A Message Passing framework with Multiple data integration for miRNA-Disease association prediction, Scientific Reports 12, 16259.
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  • Benjamins, C., Eimer, T., Schubert, F., Mohan, A., Biedenkapp, A., Rosenhahn, B., Hutter, F., and Lindauer, M. (2022)Contextualize Me -- The Case for Context in Reinforcement Learning, Arxiv Preprint, arXiv.
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  • Stahl, M., Spliethöver, M., and Wachsmuth, H. (2022)To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation. In Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science.
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  • Grimm, E., Kuhnke, F., Gajdt, A., Ostermann, J., and Knoche, M. (2022)Accurate Quantification of Anthocyanin in Red Flesh Apples Using Digital Photography and Image Analysis, Horticulturae 8.
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  • Lange, A., Hinrichs, R., and Ostermann, J. (2022)Localized Damage Detection in Wind Turbine Rotor Blades using Airborne Acoustic Emissions (accepted). In 9th Asia-Pacific Workshops on Structural Health Monitoring 2022 (APWSHM 2022).
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  • Poddar, S., Mondal, M., Misra, J., Ganguly, N., and Ghosh, S. (2022)Winds of Change: Impact of {COVID}-19 on Vaccine-Related Opinions of Twitter Users, Proceedings of the International {AAAI} Conference on Web and Social Media, Association for the Advancement of Artificial Intelligence ({AAAI}) 16, 782–793.
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  • Alshomary, M., and Stahl, M. (2022)Argument Novelty and Validity Assessment via Multitask and Transfer Learning. In Proceedings of the 9th Workshop on Argument Mining, pp. 111–114, International Conference on Computational Linguistics.
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  • Kellermann, C., Neumann, E., and Ostermann, J. (2022)Prediction of variable forecast horizons with artificial neural networks by embedding the temporal resolution warping. In International Conference on Control, Automation and Diagnosis (ICCAD), pp. 1–5.
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  • Chen, W.-F., Chen, M.-H., Mudgal, G., and Wachsmuth, H. (2022)Analyzing Culture-Specific Argument Structures in Learner Essays. In Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022), pp. 51–61.
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  • Wittig, A., Miranda, F., Hölzer, M., Altenburg, T., Bartoszewicz, J. M., Beyvers, S., Dieckmann, M. A., Genske, U., Giese, S. H., Nowicka, M., Richard, H., Schiebenhoefer, H., Schmachtenberg, A.-J., Sieben, P., Tang, M., Tembrockhaus, J., Renard, B. Y., and Fuchs, S. (2022){CovRadar}: continuously tracking and filtering {SARS}-{CoV}-2 mutations for genomic surveillance, Bioinformatics (Kelso, J., Ed.), Oxford University Press ({OUP}) 38, 4223–4225.
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  • Spliethöver, M., Keiff, M., and Wachsmuth, H. (2022)No Word Embedding Model Is Perfect: Evaluating the Representation  Accuracy for Social Bias in the Media. In Proceedings of The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022), Association for Computational Linguistics.
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  • Ren, Z., Nguyen, T. T., and Nejdl, W. (2022)Prototype learning for interpretable respiratory sound analysis. In {IEEE} International Conference on Acoustics, Speech and Signal Processing, {ICASSP} 2022, Virtual and Singapore, 23-27 May 2022, pp. 9087–9091, {IEEE}.
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  • Ren, Z., Chang, Y., Nejdl, W., and Schuller, B. W. (2022)Learning complementary representations via attention-based ensemble learning for cough-based COVID-19 recognition, Acta Acustica 6, 1–5.
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  • Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Deng, D., Benjamins, C., Ruhkopf, T., Sass, R., and Hutter, F. (2022)SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization, Journal of Machine Learning Research 23 (2022) 1–9.
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  • Alshomary, M., Rieskamp, J., and Wachsmuth, H. (2022)Generating Contrastive Snippets for Argument Search. In Proceedings of the 9th International Conference on Computational Models of Argument, pp. 21–31.
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  • Kiesel, J., Alshomary, M., Handke, N., Cai, X., Wachsmuth, H., and Stein, B. (2022)Identifying the Human Values behind Arguments. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, pp. 4459–4471.
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  • Pandey, P. K., Adhikari, B., Mazumdar, M., and Ganguly, N. (2022)Modeling Signed Networks as 2-Layer Growing Networks, IEEE Transactions on Knowledge and Data Engineering 34, 3377–3390.
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  • Xuan, Q. L., Adhisantoso, Y. G., Munderloh, M., and Ostermann, J. (2022)Uncertainty-Aware Remaining Useful Life Prediction for Predictive Maintenance Using Deep Learning (accepted). In 16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME.
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  • Adhisantoso, Y. G. (2022)Recommendation on MPEG-G Part 6 Record Structure m61340, ISO/IEC JTC 1/SC 29/WG 8.
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  • Hinrichs, R., Ortmann, F., and Ostermann, J. (2022)Vector-Quantized Zero-Delay Deep Autoencoders for the Compression of Electrical Stimulation Patterns of Cochlear Implants Using STOI. In IEEE EMBS 2022.
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  • Rumberg, L., Gebauer, C., Ehlert, H., L{ü}dtke, U., and Ostermann, J. (2022)Improving Phonetic Transcriptions of Children’s Speech by Pronunciation Modelling with Constrained CTC-Decoding. In Proceedings INTERSPEECH 2022 – 23rd Annual Conference of the International Speech Communication Association.
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  • Sass, R., Bergman, E., Biedenkapp, A., Hutter, F., and Lindauer, M. (2022)DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning. In ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML), arXiv.
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  • Benjamins, C., Jankovic, A., Raponi, E., van der Blom, K., Lindauer, M., and Doerr, C. (2022)Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis. In Workshop on Meta-Learning (MetaLearn 2022).
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  • Biedenkapp, A., Speck, D., Sievers, S., Hutter, F., Lindauer, M., and Seipp, J. (2022)Learning Domain-Independent Policies for Open List Selection. In Proceedings of the 3rd ICAPS workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL), pp. 1–9.
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  • Alshomary, M., El Baff, R., Gurcke, T., and Wachsmuth, H. (2022)The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, pp. 8782–8797.
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  • Parker-Holder, J., Rajan, R., Song, X., Biedenkapp, A., Miao, Y., Eimer, T., Zhang, B., Nguyen, V., Calandra, R., Faust, A., Hutter, F., and Lindauer, M. (2022)Automated Reinforcement Learning (AutoRL): A Survey and Open Problems, Journal of Artificial Intelligence Research 74 (2022) 517–568.
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  • Mast, M., Marschollek, M., Jack, T., Wulff, A., and Elise Study, G. (2022)Developing a Data Driven Approach for Early Detection of SIRS in Pediatric Intensive Care Using Automatically Labeled Training Data, Stud Health Technol Inform 289, 228–231.
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  • Schier, M., Reinders, C., and Rosenhahn, B. (2022)Constrained Mean Shift Clustering. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM).
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  • Hinrichs, R., Liang, K., Lu, Z., and Ostermann, J. (2022)Improved Compression of Artificial Neural Networks through Curvature-Aware Training. In Proceedings of the IEEE World Congress on Computational Intelligence.
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  • Pestel-Schiller, U., and Ostermann, J. (2022)Impact of Spatial Resolution and Zoom on Interpreter-Based Evaluation of Compressed SAR Images. In 14th European Conference on Synthetic Aperture Radar.
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  • Benjak, M., Aust, N., Samayoa, Y., and Ostermann, J. (2022)Neural Network-based Error Concealment for B-Frames in VVC. In 2022 IEEE International Symposium on Circuits and Systems (ISCAS).
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  • Fehring, L., Hanselle, J., and Tornede, A. (2022)HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection. In NeurIPS Workshop on Meta Learning (MetaLearn 2022).
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  • Voges, J. (2022)Compression of DNA Sequencing Data, Fortschritt-Berichte VDI.
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  • Benjamins, C., Jankovic, A., Raponi, E., Blom, {Koen van der}, Lindauer, M., and Doerr, C. (2022)Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis. In 6th Workshop on Meta-Learning at NeurIPS 2022.
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  • Wachsmuth, H., and Alshomary, M. (2022)“Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning How to Explain. In Proceedings of the 29th International Conference on Computational Linguistics, pp. 344–354.
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  • Dong, T. N., Schrader, J., Mücke, S., and Khosla, M. (2022)A message passing framework with multiple data integration for miRNA-disease association prediction, Scientific Reports, Springer Science and Business Media LLC 12.
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  • Dockhorn, A. (2022)Choosing Representation, Mutation, and Crossover in Genetic Algorithms, IEEE Computational Intelligence Magazine 17, 52–53.
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  • Bondarenko, A., Fr{ö}be, M., Kiesel, J., Syed, S., Gurcke, T., Beloucif, M., Panchenko, A., Biemann, C., Stein, B., Wachsmuth, H., Potthast, M., and Hagen, M. (2022)Overview of Touch{é} 2022: Argument Retrieval: Argument Retrieval: Extended Abstract. In Advances in Information Retrieval (Hagen, M., Verberne, S., Macdonald, C., Seifert, C., Balog, K., N{\o}rv{\aa}g, K., and Setty, V., Eds.) Part 2., pp. 339–346, Springer Science and Business Media Deutschland GmbH, Germany.
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  • Geisler, S., Vidal, M.-E., Cappiello, C., Loscio, B. F., Gal, A., Jarke, M., Lenzerini, M., Missier, P., Otto, B., Paja, E., Pernici, B., and Rehof, J. (2022)Knowledge-Driven Data Ecosystems Toward Data Transparency, Journal of Data and Information Quality, Association for Computing Machinery ({ACM}) 14, 1–12.
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  • Adriaensen, S., Biedenkapp, A., Shala, G., Awad, N., Eimer, T., Lindauer, M., and Hutter, F. (2022)Automated Dynamic Algorithm Configuration, Journal of Artificial Intelligence Research, Morgan Kaufmann Publishers, Inc.
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  • Wagner, L., Olson, C., and Dockhorn, A. (2022)Generalizations of Steering - A Modular Design. In 2022 IEEE Conference on Games (CoG), pp. 1–4.
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  • Nayak, T., Sharma, S., Butala, Y., Dasgupta, K., Goyal, P., and Ganguly, N. (2022)A Generative Approach for Financial Causality Extraction. In Companion Proceedings of the Web Conference 2022, {ACM}.
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  • Rosenhahn, B. (2022)Mixed Integer Linear Programming for Optimizing a Hopfield Network. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), pp. 1–17.
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  • Adhisantoso, Y. G., Xuan, Q. L., Kellerman, C., Munderloh, M., and Ostermann, J. (2022)Introduction to deep degradation metric in smart production ecosystems. In 16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME.
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  • Iosifidis, V., Papadopulous, S., Rosenhahn, B., and Ntoutsi, E. (2022)AdaCC: cumulative cost-sensitive boosting for imbalanced classification, Knowledge and Information Systems 38.
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  • Rumberg, L., Gebauer, C., Ehlert, H., Wallbaum, M., Bornholt, L., Ostermann, J., and L{ü}dtke, U. (2022)kidsTALC: A Corpus of 3- to 11-year-old German Children’s Connected Natural Speech. In Proceedings INTERSPEECH 2022 – 23rd Annual Conference of the International Speech Communication Association.
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  • Wolff, J., Klimke, A., Marschollek, M., and Kacprowski, T. (2022)Forecasting admissions in psychiatric hospitals before and during Covid-19: a retrospective study with routine data, Sci Rep 12, 15912.
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  • Truong, G., Le, H., Suter, D., Zhang, E., and Gilani, S. Z. (2021)Unsupervised Learning for Robust Fitting: A Reinforcement Learning Approach. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10343–10352.
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  • Voges, J., Hernaez, M., Mattavelli, M., and Ostermann, J. (2021)An Introduction to MPEG-G: The First Open ISO/IEC Standard for the Compression and Exchange of Genomic Sequencing Data, Proceedings of the IEEE 109, 1607–1622.
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  • Benjamins, C., Eimer, T., Schubert, F., Biedenkapp, A., Rosenhahn, B., Hutter, F., and Lindauer, M. (2021)CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning. In NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning.
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  • Dockhorn, A., and Kruse, R. (2021)Fuzzy Modeling in Game AI, Journal of Pure and Applied Mathematics 12, 54–68.
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  • Dockhorn, A., Hurtado-Grueso, J., Jeurissen, D., Xu, L., and Perez-Liebana, D. (2021)Game State and Action Abstracting Monte Carlo Tree Search for General Strategy Game-Playing. In Proceedings of the 2021 IEEE Conference on Games (CoG), pp. 1–8.
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  • Perez-Liebana, D., Guerrero-Romero, C., Dockhorn, A., Xu, L., Hurtado, J., and Jeurissen, D. (2021)Generating Diverse and Competitive Play-Styles for Strategy Games. In 2021 IEEE Conference on Games (CoG), pp. 1–8.
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  • Hornakova*, A., Kaiser*, T., Rosenhahn, B., Swoboda, P., Henschel, R., and equal contribution), (*. (2021)Higher Order Multiple Object Tracking for Crowded Scenes, Computer Vision and Pattern Recognition Workshops (CVPRW).
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  • Dockhorn, A., Mostaghim, S., Kirst, M., and Zettwitz, M. (2021)Multi-Objective Optimization and Decision-Making in Context Steering. In 2021 IEEE Conference on Games (CoG), pp. 1–8.
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  • Adhisantoso, Y. G., and Ostermann, J. (2021)Efficient Coding of Contact Matrices m57789, ISO/IEC JTC 1/SC 29/WG 8.
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  • Das, S., Patibandla, H., Bhattacharya, S., Bera, K., Ganguly, N., and Bhattacharya, S. (2021)TMCOSS: Thresholded Multi-Criteria Online Subset Selection for Data-Efficient Autonomous Driving. In ICCV.
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  • Gritzner, D., Hinrichs, H., Stetter, C., Wielert, H., Breitner, M. H., and Ostermann, J. (2021)Wind Turbine Localization in Satellite and Aerial Images. In Proceedings of the Wind Energy Science Conference 2021, pp. 40–41.
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  • Pestel-Schiller, U., Hu, K., Gritzner, D., and Ostermann, J. (2021)Determination of Relevant Hyperspectral Bands Using a Spectrally Constrained CNN. In 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Paper 15.
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  • Kellermann, C., Neumann, E., and Ostermann, J. (2021)A New Preprocessing Approach to Reduce Computational Complexity for Time Series Forecasting with Neuronal Networks: Temporal Resolution Warping. In 2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC), pp. 324–328.
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  • Benjak, M., Meuel, H., Laude, T., and Ostermann, J. (2021)Enhanced Machine Learning-based Inter Coding for VVC. In 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (ICAIIC 2021).
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  • Hutter, F., Fuks, L., Lindauer, M., and Awad, N. (2021)Method, device and computer program for producing a strategy for a robot.
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  • Adhisantoso, Y. G., and Ostermann, J. (2021)Method for the Coding of Contact Matrix m56622, ISO/IEC JTC 1/SC 29/WG 8.
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  • Hinrichs, R., Gajecki, T., Ostermann, J., and Nogueira, W. (2021)A subjective and objective evaluation of a codec for the electrical stimulation patterns of cochlear implants, Journal of the Acoustic Society of America.
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  • Roy, S., Sural, S., Chhaya, N., Natarajan, A., and Ganguly, N. (2021)An Integrated Approach for Improving Brand Consistency of Web Content: Modeling, Analysis and Recommendation., ACM Trans. Web 15, 9:1–9:25.
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  • Mukherjee, R., Naik, A., Poddar, S., Dasgupta, S., and Ganguly, N. (2021)Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach. In SIGIR 2021.
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  • Schubert, F., Eimer, T., Rosenhahn, B., and Lindauer, M. (2021)Automatic Risk Adaptation in Distributional Reinforcement Learning. In Arxiv Preprint.
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  • Koley, P., Saha, A., Bhattacharya, S., Ganguly, N., and De, A. (2021)Demarcating Endogenous and Exogenous Opinion Dynamics: An Experimental Design Approach, ACM Trans. Knowl. Discov. Data, Association for Computing Machinery, New York, NY, USA 15.
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  • Samanta, B., Agrawal, M., and Ganguly, N. (2021)A Hierarchical VAE for Calibrating Attributes while Generating Text using Normalizing Flow, pp. 2405–2415, Association for Computational Linguistics.
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  • Mukherjee, A., Mallick, M., Chakraborty, S., and Ganguly, N. (2021)Unsupervised Topology Assessment in Smart Homes. In 8th ACM IKDD CODS and 26th COMAD, pp. 193–197, Association for Computing Machinery, Bangalore, India.
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  • Olatunji, I. E., Nejdl, W., and Khosla, M. (2021)Membership inference attack on graph neural networks. In IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (short version presented in ICLR-21 Workshop on Distributed and Private Machine Learning (DPML) ).
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  • Schubert, F., Awiszus, M., and Rosenhahn, B. (2021)TOAD-GAN: a Flexible Framework for Few-Shot Level Generation in Token-Based Games, IEEE Transactions on Games.
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  • Eggensperger, K., M{ü}ller, P., Mallik, N., Feurer, M., Sass, R., Klein, A., Awad, N., Lindauer, M., and Hutter, F. (2021)HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. In Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) (Datasets and Benchmarks Track).
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  • Rudolph, M., Wandt, B., and Rosenhahn, B. (2021)Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows. In Winter Conference on Applications of Computer Vision (WACV).
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  • Biedenkapp, A., Rajan, R., Hutter, F., and Lindauer, M. (2021)TempoRL: Learning When to Act. In Proceedings of the international conference on machine learning (ICML).
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  • Zimmer, L., Lindauer, M., and Hutter, F. (2021)Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL, IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 3079–3090.
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  • Zhao, B., van der Aa, H., Nguyen, T. T., Nguyen, Q. V. H., and Weidlich, M. (2021){EIRES}: Efficient Integration of Remote Data in Event Stream Processing. In Proceedings of the 2021 International Conference on Management of Data, {ACM}.
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  • Kaushal, A., Saha, A., and Ganguly, N. (2021)tWT–WT: A Dataset to Assert the Role of Target Entities for Detecting Stance of Tweets, pp. 3879–3889.
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  • Holzapfel, C., Sag, S., Graf-Schindler, J., Fischer, M., Drabsch, T., Illig, T., Grallert, H., Stecher, L., Strack, C., Caterson, I., Jebb, S., Hauner, H., and Baessler, A. (2021)Association between single nucleotide polymorphisms and weight reduction in behavioural interventions—a pooled analysis, Nutrients, MDPI 13.
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  • Tan, D. W., Gilani, S. Z., Boutrus, M., Alvares, G. A., Whitehouse, A. J., Mian, A., Suter, D., and Maybery, M. T. (2021)Facial asymmetry in parents of children on the autism spectrum, Autism Research.
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  • Kabongo, S., D’Souza, J., and Auer, S. (2021)Automated Mining of Leaderboards for Empirical {AI} Research, springer, International Conference on Asian Digital Libraries ICADL 2021: Towards Open and Trustworthy Digital Societies, 453–470.
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  • Ghosh, S., Ganguly, N., Mitra, B., and De, P. (2021)Designing an Experience Sampling Method for Smartphone Based Emotion Detection, IEEE Transactions on Affective Computing 12, 913–927.
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  • Pestel-Schiller, U., and Ostermann, J. (2021)Interpreter-Based Evaluation of Compressed SAR Images Using JPEG and HEVC Intra Coding: Compression Can Improve Usability. In 13th European Conference on Synthetic Aperture Radar.
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  • Nandy, A., Sharma, S., Maddhashiya, S., Sachdeva, K., Goyal, P., and Ganguly, N. (2021)Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework, pp. 4600–4609, Association for Computational Linguistics.
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  • Xue, Y., Kudenko, D., and Khosla, M. (2021)Graph Learning based Generation of Abstractions for Reinforcement Learning. In Adaptive and Learning Agents Workshop at AAMAS 2021.
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  • Moosbauer, J., Herbinger, J., Casalicchio, G., Lindauer, M., and Bischl, B. (2021)Explaining Hyperparameter Optimization via Partial Dependence Plots. In Proceedings of the international conference on Neural Information Processing Systems (NeurIPS).
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  • Knura, M., Kluger, F., Zahtila, M., Schiewe, J., Rosenhahn, B., and Burghardt, D. (2021)Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of Dresden, ISPRS International Journal of Geo-Information.
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  • Kellermann, C., and Ostermann, J. (2021)Estimation of unknown system states based on an adaptive neural network and Kalman filter, Procedia CIRP 99, 656–661.
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  • Nandy, A., Sharma, S., Maddhashiya, S., Sachdeva, K., Goyal, P., and Ganguly, N. (2021)Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based {QA} Framework. In Findings of the Association for Computational Linguistics: {EMNLP} 2021, Association for Computational Linguistics.
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  • Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Deng, D., Benjamins, C., Sass, R., and Hutter, F. (2021)SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. In ArXiv: 2109.09831.
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  • Hornakova*, A., Kaiser*, T., Rolinek, M., Rosenhahn, B., Swoboda, P., Henschel, R., and equal contribution), (*. (2021)Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths. In International Conference on Computer Vision (ICCV).
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  • Schubert, F., Eimer, T., Rosenhahn, B., and Lindauer, M. (2021)Towards Automatic Risk Adaption in Distributional Reinforcement Learning. In Reinforcement Learning for Real Life (RL4RealLife) Workshop in the 38th International Conference on Machine Learning (ICML).
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  • Hachmann, H., Kr{ü}ger, B., Rosenhahn, B., and Nogueira, W. (2021)Localization of Cochlear Implant Electrodes from Cone Beam Computed Tomography using Particle Belief Propagation. In International Symposium on Biomedical Imaging, ISBI.
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  • Kluger, F., Ackermann, H., Brachmann, E., Yang, M. Y., and Rosenhahn, B. (2021)Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images. In CVPR.
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  • Rumberg, L., Ehlert, H., L{ü}dtke, U., and Ostermann, J. (2021)Age-Invariant Training for End-to-End Child Speech Recognition using Adversarial Multi-Task Learning. In Proceedings INTERSPEECH 2021 -- 22th Annual Conference of the International Speech Communication Association.
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  • Luo, C., Zhao, P., Qiao, B., Wu, Y., Zhang, H., Wu, W., Lu, W., Dang, Y., Rajmohan, S., Lin, Q., and Zhang, D. (2021)NTAM: Neighborhood-Temporal Attention Model for Disk Failure Prediction in Cloud Platforms. In Proceedings of the Web Conference 2021, {ACM}.
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2020

  • Wulff, A., Mast, M., Hassler, M., Montag, S., Marschollek, M., and Jack, T. (2020)Designing an openEHR-Based Pipeline for Extracting and Standardizing Unstructured Clinical Data Using Natural Language Processing, Methods Inf Med 59, e64-e78.
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  • Cong, Y., Ackermann, H., Liao, W., Yang, M. Y., and Rosenhahn, B. (2020)NODIS: Neural Ordinary Differential Scene Understanding. In European Conference on Computer Vision (ECCV).
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  • Ostermann, J., and Hinrichs, R. (2020)Links und rechts verbinden, Unimagazin.
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  • Hu, T., Iosifidis, V., Wentong, L., Hang, Z., Yang, M. Y., Ntoutsi, E., and Rosenhahn, B. (2020)FairNN - Conjoint Learning of Fair Representations for Fair Decisions. In 23rd International Conference on Discovery Science.
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  • Samayoa, Y., and Ostermann, J. (2020)Parameter Selection for a Video Communication System based on HEVC and Channel Coding. In IEEE Latin-American Conference on Communications (LATINCOM 2020), p. 5.
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  • Dockhorn, A., Grueso, J. H., Jeurissen, D., and Perez-Liebana, D. (2020)“Stratega”: A General Strategy Games Framework. In Joint Proceedings of the AIIDE 2020 Workshops co-located with 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020); Artificial Intelligence for Strategy Games, pp. 1–7.
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  • Dockhorn, A. (2020)Vorhersagebasierte Suche f{ü}r autonomes Spielen, pp. 69–78, GI.
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  • Hu, T., Iosifidis, V., Liao, W., Zhang, H., Yang, M. Y., Ntoutsi, E., and Rosenhahn, B. (2020)FairNN - Conjoint Learning of Fair Representations for Fair Decisions.. In Discovery Science, pp. 581–595, Springer International Publishing.
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  • Meuel, H., and Ostermann, J. (2020)Analysis of Affine Motion-Compensated Prediction in Video Coding, IEEE Transactions on Image Processing 29, 7359–7374.
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  • Tan, D., Maybery, M., Gilani, S. Z., Alvares, G., Mian, A., Suter, D., and Whitehouse, A. (2020)A broad autism phenotype expressed in facial morphology, Translational Psychiatry 10.
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  • Minh, C. N. D., Gilani, S. Z., Islam, S., and Suter, D. (2020)Learning Affordance Segmentation: An Investigative Study. In DICTA2020.
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  • Dockhorn, A., and Kruse, R. (2020)Predicting Cards Using a Fuzzy Multiset Clustering of Decks, International Journal of Computational Intelligence Systems (IJCIS) 13, 1207–1217.
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  • Gaina, R. D., Balla, M., Dockhorn, A., Montoliu, R., and Perez liebana, D. (2020)TAG : A Tabletop Games Framework. In Joint Proceedings of the AIIDE 2020 Workshops co-located with 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020); CEUR Workshop Proceedings (2020), pp. 1–7.
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  • Hornakova*, A., Henschel*, R., Rosenhahn, B., Swoboda, P., and equal contribution), (*. (2020)Lifted Disjoint Paths with Application in Multiple Object Tracking, Proceedings of the 37th International Conference on Machine Learning (ICML).
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  • Samayoa, Y., and Ostermann, J. (2020)Modified Active Constellation Extension Algorithm for PAPR Reduction in OFDM Systems. In 2020 Wireless Telecommunications Symposium (WTS), p. 5.
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  • Eimer, T., Biedenkapp, A., Hutter, F., and Lindauer, M. (2020)Towards Self-Paced Context Evaluations for Contextual Reinforcement Learning. In Workshop on Inductive Biases, Invariances and Generalization in Reinforcement Learning (BIG@ICML’20).
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  • Dockhorn, A., and Kruse, R. (2020)Forward Model Learning for Motion Control Tasks. In 2020 IEEE 10th International Conference on Intelligent Systems (IS), pp. 1–5.
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  • J{ü}rgens, H., Hinrichs, R., and Ostermann, J. (2020)Recognizing Guitar Effects and Their Parameter Settings. In Proceedings of the DAFx2020 (Vol I).
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  • Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M., and Hutter, F. (2020)Auto-Sklearn 2.0: The Next Generation. In arXiv:2007.04074 [cs.LG].
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  • Scheffner, I., Gietzelt, M., Abeling, T., Marschollek, M., and Gwinner, W. (2020)Patient Survival After Kidney Transplantation: Important Role of Graft-sustaining Factors as Determined by Predictive Modeling Using Random Survival Forest Analysis, Transplantation 104, 1095–1107.
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  • Sen, H., Wentong, L., Rezazadegan Tavakoli, H., Ying Yang, M., Rosenhahn, B., and Pugeault, N. (2020)Image Captioning through Image Transformer. In .
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  • Ackermann, H., Meuel, H., Rosenhahn, B., and Ostermann, J. (2020)Verfahren und Vorrichtung zum Aufnehmen eines Digitalbildes 1–12.
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  • Henschel, R., von Marcard, T., and Rosenhahn, B. (2020)Accurate Long-Term Multiple People Tracking using Video and Body-Worn IMUs, IEEE Transactions on Image Processing.
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  • Kuhnke, F., Rumberg, L., and Ostermann, J. (2020)Two-Stream Aural-Visual Affect Analysis in the Wild. In 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), pp. 366–371.
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  • Biedenkapp, A., Rajan, R., Hutter, F., and Lindauer, M. (2020)Towards TempoRL: Learning When to Act. In Workshop on Inductive Biases, Invariances and Generalization in Reinforcement Learning (BIG@ICML’20).
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  • Denkena, B., Dittrich, M., Lindauer, M., Mainka, and St{ü}renburg, L. (2020)Using AutoML to Optimize Shape Error Prediction in Milling Processes. In Proceedings of 20th Machining Innovations Conference for Aerospace Industry (MIC).
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  • Perez-Liebana, D., Dockhorn, A., Grueso, J. H., and Jeurissen, D. (2020)The Design Of “Stratega”: A General Strategy Games Framework, arXiv:2009.05643 1–7.
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  • Gebauer, C., and Bennewitz, M. (2020)Penalized Bootstrapping for Reinforcement Learning in Robot Control. In International Conference on Machine Learning and Applications (CMLA).
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  • Liao, W., Cheng, X., Yang, J., Roth, S., Goesele, M., Yang, M. Y., and Rosenhahn, B. (2020)LR-CNN: Local-aware Region CNN for Vehicle Detection in Aerial Imagery. In XXIV ISPRS Congress, p. 8.
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  • Awiszus, M., Schubert, F., and Rosenhahn, B. (2020)TOAD-GAN: Coherent Style Level Generation from a Single Example. In AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Best Student Paper Award.
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  • Krause, T., and Ostermann, J. (2020)Damage Detection for Wind Turbine Rotor Blades Using Airborne Sound, Structural Control and Health Monitoring.
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  • Dockhorn, A. (2020)Dissertation: Prediction-based Search for Autonomous Game-Playing, Otto von Guericke University Magdeburg 1–231.
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  • Kluger, F., Brachmann, E., Ackermann, H., Rother, C., Yang, M. Y., and Rosenhahn, B. (2020)CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus. In Computer Vision and Pattern Recognition (CVPR).
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  • Reinders, C., and Rosenhahn, B. (2020)Neuronale Netze: Angriffe und Verteidigung - Ich sehe was, was du nicht siehst, iX Developer 2020 – Machine Learning 2.0.
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  • Voges, J., Paridaens, T., M{ü}ntefering, F., Mainzer, L. S., Bliss, B., Yang, M., Ochoa, I., Fostier, J., Ostermann, J., and Hernaez, M. (2020)GABAC: an arithmetic coding solution for genomic data, Bioinformatics 36, 2275–2277.
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  • Kluger, F., Ackermann, H., Yang, M. Y., and Rosenhahn, B. (2020)Temporally Consistent Horizon Lines. In International Conference on Robotics and Automation (ICRA).
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  • Gritzner, D., and Ostermann, J. (2020)USING SEMANTICALLY PAIRED IMAGES TO IMPROVE DOMAIN ADAPTATION FOR THE SEMANTIC SEGMENTATION OF AERIAL IMAGES, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences 483–492.
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  • Zell, P., Rosenhahn, B., and Wandt, B. (2020)Weakly-supervised Learning of Human Dynamics. In European Conference on Computer Vision (ECCV).
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  • S{ü}dbeck, S., Krause, T., and Ostermann, J. (2020)Non-Line-of-Sight Time-Difference-of-Arrival Localization with Explicit Inclusion of Geometry Information in a Simple Diffraction Scenario. In IEEE MMSP 2020 - IEEE International Workshop on Multimedia Signal Processing.
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  • Dockhorn, A., Saxton, C., and Kruse, R. (2020)Association Rule Mining for Unknown Video Games, Fuzzy Approaches for Soft Computing and Approximate Reasoning: Theories and Applications 257–270.
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  • Dockhorn, A., and Lucas, S. (2020)Local Forward Model Learning for GVGAI Games. In IEEE Conference on Computational Intelligence and Games, CIG, pp. 716–723.
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  • Gaina, R. D., Balla, M., Dockhorn, A., Montoliu, R., and Perez-Liebana, D. (2020)Design and Implementation of TAG: A Tabletop Games Framework., arXiv:2009.12065.
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  • Speck, D., Biedenkapp, A., Hutter, F., Mattm{ü}ller, R., and Lindauer, M. (2020)Learning Heuristic Selection with Dynamic Algorithm Configuration. In Proceedings of international workshop on Bridging the Gap Between AI Planning and Reinforcement Learning at ICAPS.
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  • Eggensperger, K., Haase, K., M{ü}ller, P., Lindauer, M., and Hutter, F. (2020)Neural Model-based Optimization with Right-Censored Observations. In CoRR.
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  • Shala, G., Biedenkapp, A., Awad, N., Adriaensen, S., Lindauer, M., and Hutter, F. (2020)Learning Step-Size Adaptation in CMA-ES. In Proceedings of the Sixteenth International Conference on Parallel Problem Solving from Nature ({PPSN}’20).
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  • Gra{{\"s}}hof, S., Ackermann, H., Brandt, S., and Ostermann, J. (2020)Multilinear Modelling of Faces and Expressions, Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
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  • Benjak, M., and Ostermann, J. (2020)Applications suitable for AI-based data compression, 1st Meeting of ISO/IEC JTC 1/SC 29/WG 2 Document m55424.
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  • Hartmann, F., Sommer, A., Pestel-Schiller, U., and Osterman, J. (2020)A scheme for stabilizing the image generation for VideoSAR. In 13th European Conference on Synthetic Aperture Radar.
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  • Adhisantoso, Y. G., Rohlfing, C., Voges, J., and Ostermann, J. (2020)Extension to method for the coding of genomic variants m55355, ISO/IEC JTC 1/SC 29/WG 8.
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  • {Fayyazifar}, N., {Ahderom}, S., {Suter}, D., {Maiorana}, A., and {Dwivedi}, G. (2020)Impact of Neural Architecture Design on Cardiac Abnormality Classification Using 12-lead ECG Signals. In 2020 Computing in Cardiology, pp. 1–4.
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  • Souza, A., Nardi, L., Oliveira, L., Olukotun, K., Lindauer, M., and Hutter, F. (2020)Prior-guided Bayesian Optimization. In arxiv:2006.14608[cs.LG].
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  • Sen, H., Wentong, L., Tavakoli, H. R., Yang, M. Y., Rosenhahn, B., and Pugeault, N. (2020)Image Captioning through Image Transformer. In Asian Conference on Computer Vision (ACCV).
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  • Cheng, H., Liao, W., Ying, Y. M., Sester, M., and Rosenhahn, B. (2020)MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic. In 23rd International Conference on Intelligent Transportation Systems (ITSC).
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  • Krause, L., Koc, J., Rosenhahn, B., and Rosenhahn, A. (2020)Fully Convolutional Neural Network for Detection and Counting of Diatoms on Coatings after Short-Term Field Exposure, Environmental Science and Technology 54, 10022–10030.
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  • Awiszus, M., Schubert, F., and Rosenhahn, B. (2020, October)TOAD-GAN: Coherent Style Level Generation from a Single Example.
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  • Rudolph, M., Wandt, B., and Rosenhahn, B. (2020, August)Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows..
    AbstractURLBibTeXEndNoteBibSonomy
  • Luo, C., Zhao, P., Chen, C., Qiao, B., Du, C., Zhang, H., Wu, W., Cai, S., He, B., Rajmohan, S., and Lin, Q. (2020)PULNS: Positive-Unlabeled Learning with Effective Negative Sample Selector. In , pp. 8784–8792.
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  • Wallat, J., Singh, J., and Anand, A. (2020)BERTnesia: Investigating the capture and forgetting of knowledge in BERT. In Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, BlackboxNLP@EMNLP 2020, Online, November 2020 (Alishahi, A., Belinkov, Y., Chrupala, G., Hupkes, D., Pinter, Y., and Sajjad, H., Eds.), pp. 174–183, Association for Computational Linguistics.
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  • Wallat, J., Singh, J., and Anand, A. (2020)BERTnesia: Investigating the capture and forgetting of knowledge in BERT.. In Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, pp. 174–183, Association for Computational Linguistics, Online.
    AbstractURLBibTeXEndNoteBibSonomy
  • Liu, Z., Pavao, A., Xu, Z., Escalera, S., Ferreira, F., Guyon, I., Hong, S., Hutter, F., Ji, R., Jacques, J., Li, G., Lindauer, M., Luo, Z., Madadi, M., Nierhoff, T., Niu, K., Pan, C., Stoll, D., Treguer, S., Wang, J., Wang, P., Wu, C., Xiong, Y., Zela, A., and Zhang, Y. (2020)Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019. In HAL.
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  • Adhisantoso, Y. G., Rohlfing, C., Voges, J., and Ostermann, J. (2020)Method for the coding of genotype likelihood of variant m55356, ISO/IEC JTC 1/SC 29/WG 8.
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  • Zimmer, L., Lindauer, M., and Hutter, F. (2020)Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL. In arxiv:2006.13799[cs.LG].
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  • Awad, N., Shala, G., Deng, D., Mallik, N., Feurer, M., Eggensperger, K., Biedenkapp, A., Vermetten, D., Wang, H., Carola, D., Lindauer, M., and Hutter, F. (2020)Squirrel: A Switching Hyperparameter Optimizer, arxiv.
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2019

  • Dockhorn, A., and Mostaghim, S. (2019)Introducing the Hearthstone-AI Competition, arXiv:1906.04238 1–4.
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  • Dockhorn, A., Schwensfeier, T., and Kruse, R. (2019)Fuzzy Multiset Clustering for Metagame Analysis. In Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019), pp. 536–543.
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  • Ostermann, J., Denkena, B., Bergmann, B., Schmidt, A., Krause, T., and Voges, J. (2019)Compression of Machine Tool Data, ISO/IEC JTC1/SC29/WG11.
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  • Wilbers, D., Rumberg, L., and Stachniss, C. (2019)Approximating marginalization with sparse global priors for sliding window SLAM-graphs.. In 2019 Third IEEE International Conference on Robotic Computing (IRC), pp. 25–31.
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  • Lucas, S. M., Alexander, Dockhorn, V., Gaina, R. D., Bravi, I., Perez-Liebana, D., Mostaghim, S., and Kruse, R. (2019)A Local Approach to Forward Model Learning: Results on the Game of Life Game. In 2019 IEEE Conference on Games (CoG), pp. 1–8.
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  • Dockhorn, A., Lucas, S. M., Volz, V., Bravi, I., Gaina, R. D., and Perez-Liebana, D. (2019)Learning Local Forward Models on Unforgiving Games. In 2019 IEEE Conference on Games (CoG).
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  • Dengel, R., Woiwode, D., Florsch{ü}tz, N., Huber, V., Muller, T., von Pichowski, J., Rabinowitsch, A., Scholz, S., Sch{ü}lein, H., Steinweg, E., Stippel, B., St{ö}ferle, P., Wittekind, I., Wizemann, O., Zaft, A., Zembrot, L., and Griebenow, K. (2019)QUEST ON BEXUS 27. In 24th ESA Symposium on European Rocket \& Balloon Programmes and Related.
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2018

  • Dockhorn, A., and Apeldoorn, D. (2018)Forward Model Approximation for General Video Game Learning. In Proceedings of the 2018 IEEE Conference on Computational Intelligence and Games (CIG’18), pp. 425–432.
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  • Dockhorn, A., and Kruse, R. (2018)Detecting Sensor Dependencies for Building Complementary Model Ensembles. In Proceedings of the 28. Workshop Computational Intelligence, Dortmund, 29.-30. November 2018, pp. 217–234.
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  • Pichler, E., Bethmann, K., Kelb, C., and Schade, W. (2018)Rapid prototyping of all-polymer AWGs for FBG readout using direct laser lithography, Optics Letters.
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  • Sabsch, T., Braune, C., Dockhorn, A., and Kruse, R. (2018)Using a multiobjective genetic algorithm for curve approximation. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, pp. 1–6.
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  • Waltermann, C., Bethmann, K., Doering, A., Jjang, Y., Baumann, A. L., Anglemahr, M., and Schade, W. (2018)Multiple off-axis fiber Bragg gratings for 3D shape sensing, Applied Optics.
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  • Dockhorn, A., Frick, M., Akkaya, {Ü}nal, and Kruse, R. (2018)Predicting Opponent Moves for Improving Hearthstone AI. In 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2018, pp. 621–632.
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  • Dockhorn, A., Tippelt, T., and Kruse, R. (2018)Model Decomposition for Forward Model Approximation. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1751–1757.
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2017

  • Dockhorn, A., and Kruse, R. (2017)Combining cooperative and adversarial coevolution in the context of pac-man. In 2017 IEEE Conference on Computational Intelligence and Games, CIG 2017, pp. 60–67.
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  • Dockhorn, A., Doell, C., Hewelt, M., and Kruse, R. (2017)A decision heuristic for Monte Carlo tree search doppelkopf agents. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8.
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2016

  • Orighici, R., Bethmann, K., Zywietz, U., Reinhard, C., and Schade, W. (2016)All-polymer arrayed waveguide gratings at 850 nm: design, fabrication and characterization, Optics Letters.
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  • Dockhorn, A., Braune, C., and Kruse, R. (2016)Variable density based clustering. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8.
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2015

  • Pichler, E., Bethmann, K., Zywietz, U., Reinhard, C., Spad, C., Gleissner, U., Kelb, C., Roth, B., Willer, U., and Schade, W. (2015)Ring resonators in polymer foils for sensing of gaseous species. In Fiber Optic Sensors and Applications.
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  • Held, P., Dockhorn, A., and Kruse, R. (2015)On Merging and Dividing Social Graphs, Journal of Artificial Intelligence and Soft Computing Research 5, 23–49.
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  • Held, P., Dockhorn, A., Krause, B., and Kruse, R. (2015)Clustering Social Networks Using Competing Ant Hives. In 2015 Second European Network Intelligence Conference, pp. 67–74.
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  • Waltermann, C., Baumann, A. L., Bethmann, K., Doering, A., Koch, J., Angelmahr, m., and Schade, W. (2015)Femtosecond laser processing of evanescence field coupled waveguides in single mode glass fibers for optical 3D shape sensing and navigation. In Fiber Optic Sensors and Applications.
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  • Bethmann, K., Orghici, R., Pichler, E., Zywietz, U., Reinhard, C., Schmidt, T., Gleissner, U., Kelb, C., Roth, B., Willer, U., and Schade, W. (2015)New design for a wavelength demultiplexing device. In Fiber Optic Sensors and Applications.
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  • Dockhorn, A., Braune, C., and Kruse, R. (2015)An Alternating Optimization Approach based on Hierarchical Adaptations of DBSCAN. In 2015 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 749–755.
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  • Dockhorn, A. (2015)Master Thesis: Hierarchical Extensions and Cluster Validation Techniques for DBSCAN, Otto von Guericke University Magdeburg 1–80.
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