Forschung



Anwendungsbereiche


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

Im Projekt " Breast Cancer Network Hannover", das sich auf Brustkrebs konzentriert, arbeiten Prof. Tjoung-Won Park-Simon und Dr. Thilo Dörk-Bousset aus der Frauenklinik der MHH mit dem Leibniz AI Lab zusammen, um Faktoren für den Therapieerfolg bei Patientinnen mit der Diagnose Brustkrebs zu identifizieren. Dazu werden standardisierte Daten von rund 5000 Patientinnen des regionalen Netzwerks " Network Breast Cancer" ausgewertet. In einem ersten Schritt werden Anamnesedaten der Patientin und ihrer Familie, Tumormerkmale, Therapiedaten, Daten zu Nachuntersuchungen und Überleben, genetische Informationen sowie sozioökonomische Daten der Patientin integriert, um eine umfassende Analyse zu ermöglichen. Besonderes Augenmerk wird dabei auf die Assoziation von sozioökonomischen Aspekten wie Bildung und Migrationshintergrund mit dem Therapieerfolg gelegt. Ein weiterer Schwerpunkt ist die Identifizierung von Subpopulationen von Patienten auf der Grundlage des Erfolgs verschiedener Therapieoptionen, um eine gezielte, personalisierte Therapie zu ermöglichen. Insbesondere soll das Projekt optimierte Vorschläge liefern, welche Patienten eher von einer neoadjuvanten Therapie und welche eher von einer Operation profitieren werden.

Während der derzeitige Ansatz zur Vorhersage der Rückfallwahrscheinlichkeit ein logistisches Regressionsmodell verwendet, wollen wir auf komplexere Modelle wie decision trees (Entscheidungsbäume), random forests (Zufallswälder), neuronale Netze und die Einführung von vorhandenem Fachwissen über Brustkrebs unter Verwendung von Knowledge Graphs (Wissensgraphen) erweitern. Dazu wird ein Knowledge Graph modelliert und auf der Grundlage der erhaltenen Patientendaten ausgefüllt. Aufbauend auf Benchmark-Modellen zur Einbettung von Knowledge Graphs wie TransE [1], ComplEx [2] und RotatE [3] wird ein Rahmenwerk entwickelt, das bestehende biomedizinische Ontologien (z.B. Gene Ontology) einbeziehen kann, um so die Rückfallwahrscheinlichkeit einer Behandlung vorherzusagen. Darüber hinaus wird zur Unterstützung der Entscheidungsfindung des Arztes ein Knowledge Graph für Arzneimittelinteraktionen verwendet, um latente semantische Darstellungen von Arzneimitteln/Medikamenten zu erlernen und potenziell schädliche Arzneimittelinteraktionen vorherzusagen, die auftreten können, wenn ein Patient mehrere Medikamente gleichzeitig einnehmen muss. Bei der Einführung komplexerer Modelle müssen wir ein Gleichgewicht zwischen Modellleistung und Interpretierbarkeit unserer Ansätze finden. Insbesondere bei der Verwendung von neuronalen Netzen werden wir bestehende Interpretierbarkeitstechniken wie LIME [4] und Shapley-Werte [5] nutzen.

Angesichts der ethischen Implikationen der Entwicklung und Verwendung von Modellen des maschinellen Lernens als Entscheidungsunterstützungssysteme im Gesundheitswesen nutzen wir diese Gelegenheit, um parallel zur Entwicklung der oben beschriebenen Lösungen einen bestehenden ethischen Rahmen zu bewerten: Die rasche und zunehmende Entwicklung des maschinellen Lernens im Gesundheitswesen (ML-HCAs, englisch: machine learning in healthcare applications) erfordert eine ethische Prüfung, um die Auswirkungen neuartiger medizinischer Geräte und Methoden auf Patienten und Gesellschaft zu bewerten. Es ist zwingend erforderlich, dass solche ethischen Untersuchungen und die damit verbundenen ethischen Aspekte untersucht werden. In dem Maße, in dem die Medizintechnik voranschreitet, muss auch eine gleichzeitige ethische Prüfung der Nutzung und des Anwendungsbereichs erfolgen, z. B. der Art der Systemanwendung, der dem System zugrunde liegenden Daten und der Auswirkungen auf den Patienten, die Gesellschaft und das Gesundheitswesen. Eine solche ethische Prüfung ist zwingend erforderlich, um zu vermeiden, dass maschinelle Lernwerkzeuge, die im Gesundheitswesen eingesetzt werden, Verzerrungen enthalten oder verstärken.

Es wurden bereits ethische Rahmenwerke vorgeschlagen (z. B. Floridi & Strait, 2020; Saltz & Dewar, 2019), doch Char und Kollegen (2020) haben ein Rahmenwerk entwickelt, das sorgfältig und transparent auf der Grundlage bereits vorhandener Literatur aufgebaut ist, um systematisch ethische Überlegungen zu identifizieren, die für ML-HCAs spezifisch sind. Während einige für einen "Ethiker als Designer" plädieren, der den Entwicklungsprozess von Werkzeugen des maschinellen Lernens prüft (van Wynsberghe & Robbins, 2014), hat die Implementierung eines solchen ethischen Identifikationsrahmens in einem Forschungsteam einen größeren Nutzen. Wie bereits an anderer Stelle vorgeschlagen wurde (z. B. Armstrong, 2017; Blay et al., 2012), sollte die Entwicklung von KI in der Medizin interdisziplinär und/oder durch Co-Design erfolgen. Daher bietet die Umsetzung des Rahmens von Char und Kollegen (2020) in einem Forschungsteam den Vorteil der Überprüfung (z. B. van Wynsberghe & Robbins, 2014) durch die Forscher dieser Studie und fördert gleichzeitig die Identifizierung und das Management ethischer Überlegungen vor Ort in der Forschungsgruppe. Eine solche Umsetzung würde die ethische Entwicklung von ML-HCAs fördern. Der vorgeschlagene Rahmen muss jedoch noch unabhängig evaluiert werden. Daher wollen wir den Pipeline-Rahmen von Char und Kollegen (2020) im Kontext einer Forschungsgruppe bewerten, die maschinelle Lerntechniken zur Identifizierung von Biomarkern bei Brustkrebspatientinnen entwickeln will, um den Erfolg der Chemotherapie vorherzusagen.

Quellenangaben:

[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

Die akute lymphoblastische Leukämie der B-Generationen (B-ALL, engl.: B-progenitor acute lymphoblastic leukemia) ist die häufigste pädiatrische Malignität. Next Generation Sequencing (NGS)-Technologien haben Einzug in die Routinediagnostik gehalten. Unter ihnen ist die kosteneffektive gezielte RNA-Sequenzierung besonders attraktiv. Wir analysierten die gezielte RNA-Sequenzierung von ~1.500 pädiatrischen ALL-Patienten aus den deutschen pädiatrischen ALL-Studiengruppen.  Wir kombinieren UMAP (Uniform Manifold Approximation and Projection) und überwachte Algorithmen des maschinellen Lernens, um ein interaktives Tool zur Visualisierung und Vorhersage von diagnostischen Untergruppen zu entwickeln. Wir erforschen eine Vielzahl von maschinellen Lerntechniken, einschließlich über Gennetzwerke informierte neuronale Netze, um unser Vorhersagemodell zu erstellen. Das Tool hilft bei der Stratifizierung von Patienten ohne aberrante Fusion oder Aneudiploidie, bei der Validierung konventioneller diagnostischer Methoden und bei der Entdeckung neuer Untergruppen. Für die Zukunft planen wir, ein solches KI-gestütztes Diagnosewerkzeug auf weitere klinische, transkriptomische und epigenetische Daten auszuweiten. Der vorgeschlagene Arbeitsablauf wird die derzeitige diagnostische Routine erheblich ergänzen, den Patienten bessere Behandlungsmöglichkeiten bieten und den Weg für die personalisierte Onkologie ebnen.Accordion Sample Description

Man with Parkinsons disease

Team: Soumyadeep Roy, Salomon Kabongo Kabenamualu, Prof. Niloy Ganguly, Prof. Dr. Helge Frieling, Dr. Stefanie Mücke, Dominik Wolff

Im Projekt "Big Data in Psychiatric Disorders" arbeitet Prof. Dr. Helge Frieling von der Klinik für Psychiatrie, Sozialpsychiatrie und Psychotherapie (MHH) gemeinsam mit dem Leibniz AI Lab an den Schwerpunkten Schizophrenie und neurodegenerative Erkrankungen. Im ersten Teilprojekt werden genetische Informationen von rund 50.000 Patienten mit der Diagnose Schizophrenie mittels künstlicher Intelligenz ausgewertet, um mögliche Subtypen zu identifizieren. Die Hypothese dabei ist, dass Schizophrenie als Phänotyp auf einer Vielzahl von Ursachen beruht, die eine differenzierte Diagnose und Therapie erfordern. Wir werden uns auf dieses Projekt konzentrieren und haben die Formalitäten der Datenanforderung abgeschlossen. Allerdings stehen die Daten vom NIMH noch aus.

Daher arbeiten wir an der Subtypisierung von Patienten mit der Parkinson-Krankheit, einer neurodegenerativen Erkrankung, unter Verwendung klinischer und genetischer Daten. Die meisten Arbeiten befassen sich mit der Subtypisierung von Parkinson-Patienten anhand der motorischen Symptome und berücksichtigen in der Regel die ältere Bevölkerung (über 60 Jahre). In jüngster Zeit beziehen Forscher auch nicht-motorische Symptome in die Definition von Patientensubtypen ein, da nicht-motorische Symptome häufig der Entwicklung klassischer motorischer Anzeichen vorausgehen und wesentlich zur Gesamtprognose beitragen. Konkret planen wir, bei jüngeren Morbus-Parkinson-Patienten (unter 60 Jahren) anhand von klinischen und genetischen Daten Patientensubtypen zu identifizieren. Wir sind auch an Patienten mit Begleiterkrankungen wie Schizophrenie und schweren Depressionen interessiert. Wir haben ein binäres Klassifikationsmodell entwickelt, mit dem wir vorhersagen können, ob ein Patient an Morbus Parkinson leidet oder nicht. Wir verwenden den trainierten Entscheidungsbaum (decision tree), um die Patientensubtypen zu bestimmen; dies ist der erste Ansatz, den wir verfolgen, um die Einschränkung zu überwinden, dass die Subtypkennzeichnungen der Patienten nicht verfügbar sind. Derzeit führen wir eine Studie zur Charakterisierung der Subtypen von Parkinson-Patienten anhand klinischer Daten durch. In Zukunft planen wir, diese klinischen Patientensubtypen anhand ihrer Genotypdaten weiter zu charakterisieren. In diesem Sinne erforschen wir derzeit einen zweiten Ansatz für die Subtypisierung von Patienten, bei dem wir die Patienten direkt anhand ihrer Genotypdaten (SNP-Daten) clustern.

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

Im Projekt "Anwendungsfall Pädiatrische Intensivstation (PICU, engl. Pediatric Intensive Care Unit)" haben Professorin Antje Wulff, PD Dr. Thomas Jack, PD. Dr. Henning Rathert, Marcel Mast und Prof. Michael Marschollek von der Medizinischen Hochschule Hannover gemeinsam mit dem Leibniz AI Lab an dem Ziel, Organdysfunktionen auf PICUs automatisch zu erkennen. Aufgrund der unmittelbaren Entscheidungsfindung mit hohem Risiko und hohem Stress für Kliniker auf Intensivstationen, einer datenintensiven Umgebung, ist es unerlässlich, automatische Entscheidungsfindungsmodelle mit dem neuesten Stand des maschinellen Lernens und der Deep-Learning-Topologien zu entwickeln; dadurch wird die Entwicklung von Echtzeitmodellen für die Entscheidungsfindung gefördert und der Druck auf die Kliniker gemindert. Darüber hinaus gibt es bei der Entscheidungsfindung in der PICU mehrere Schwierigkeiten: i) Verschiedene Krankheiten dominieren bestimmte Altersgruppen von 0 bis 18 Jahren, und ii) normative Werte sind in verschiedenen Altersgruppen sehr unterschiedlich. Es gibt jedoch nur wenige Forschungsstudien, die sich mit der Analyse der auf PICU-Stationen erhobenen Daten befassen. In diesem Zusammenhang konzentriert sich das Projekt PICU Use Case auf die Vorhersage von Organdysfunktionen auf der Grundlage von PICU-Daten. Es gibt zwei Hauptbereiche, die in diesem Projekt geplant sind. Im Folgenden werden die beiden Zweige vorgestellt.

i) Wir werden uns auf die Verarbeitung der klinischen Daten konzentrieren, die hauptsächlich Vitalparameter (z. B. Atemfrequenz, Herzfrequenz usw.), Laborparameter (z. B. Leukozyten) und Patientendaten (z. B. Größe, Gewicht usw.) enthalten.

ii) Es wird eine neue Datenbank mit Kurvenformdaten (z. B. Elektrokardiogramm) von den bettseitigen Monitoren erfasst. Der Benchmark wird eingerichtet, wenn die Daten gesammelt und vorverarbeitet sind ( zum Beispiel Anonymisierung), und es werden eine Reihe von Ansätzen des maschinellen Lernens und des Deep Learning angewendet.

Zusammenfassend lässt sich sagen, dass die Forschung im Rahmen dieses Projekts verwandte Forschungsstudien über die Anwendung von KI auf der Intensivstation erleichtern soll.

COVID-19, eine durch SARS-CoV2 verursachte Krankheit, kann viele verschiedene Formen annehmen, die in ihrem klinischen Schweregrad von leichten oder asymptomatischen Erkrankungen bis hin zu akuten Zuständen wie ARDS ("acute respiratory distress syndrome", d. h. akutes Atemnotsyndrom) und Tod reichen. Mehrere Studien haben bereits gezeigt, dass neben demografischen Faktoren und Vorerkrankungen auch eine genetische Veranlagung eine wichtige Rolle bei der Krankheitsentwicklung spielen kann. Um die Pathophysiologie und den Verlauf von COVID-19 besser zu verstehen, sammeln Kliniker und Forscher der Medizinischen Hochschule Hannover (MHH) seit Beginn der Pandemie Patientenproben und Daten in der vom Niedersächsischen Ministerium für Wissenschaft und Kultur (MWK) finanzierten COVID-19-Biobank.

An den gesammelten Bioproben wurden umfangreiche molekulare Charakterisierungen durchgeführt, insbesondere an Material von Patienten mit schweren Krankheitsverläufen, die intensivmedizinisch betreut und beatmet werden mussten. Zu diesen globalen Analysen gehören die Sequenzierung des Patientengenoms, die Genexpression und der Methylierungszustand bestimmter Basen im Genom (Epigenom). Ergänzt werden diese Daten durch hochauflösende optische Analysen struktureller DNA-Varianten, die mit einem erhöhten Krankheitsrisiko in Verbindung gebracht werden können. Darüber hinaus wurde von der Hannover Unified Biobank (HUB) in Zusammenarbeit mit der Abteilung Pneumologie der MHH ein breiter klinischer Datensatz zu allen Patienten erhoben, der Informationen über die Vorerkrankungen, den Schweregrad der Erkrankung, therapeutische Maßnahmen, Komplikationen und den Krankheitsverlauf der COVID-19-Patienten enthält.

Um diese umfangreiche Sammlung von molekularen und klinischen Daten, die im Rohzustand bereits über 14 TB umfasst, in einer integrativen Analyse zusammenzuführen, arbeitet die HUB mit Wissenschaftlern des L3S Future Laboratory und Prof. Yang Li vom Helmholtz-Zentrum für Infektionsforschung (HZI) zusammen. Ziel der integrativen Datenanalyse ist es, die verschiedenen Datenschichten zusammenzuführen und prognostische molekulare Marker oder frühe Krankheitsmuster zu identifizieren, die mit dem weiteren Krankheitsverlauf in Verbindung stehen.


Seminare



Publikationen


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

2024

  • Hirche, C. (2024)Quantum Doeblin coefficients: A simple upper bound on contraction coefficients. In preprint.
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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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  • 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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  • 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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  • 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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  • Rosenhahn, B., and Hirche, C. (2024)Quantum Normalizing Flows for Anomaly Detection, preprint.
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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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  • 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., and Rosenhahn, B. (2024)PARSAC: Accelerating Robust Multi-Model Fitting with Parallel Sample Consensus. In AAAI.
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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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  • Tang, M., Antić, Željko, Fardzadeh, P., Pietzsch, S., Schröder, C., Eberhardt, A., van Bömmel, A., Escherich, G., Hofmann, W., Horstmann, M. A., Illig, T., McCrary, J. M., Lentes, J., Metzler, M., Nejdl, W., Schlegelberger, B., Schrappe, M., Zimmermann, M., Miarka-Walczyk, K., Patsorczak, A., Cario, G., Renard, B. Y., Stanulla, M., and Bergmann, A. K. (2024)An artificial intelligence-assisted clinical framework to facilitate diagnostics and translational discovery in hematologic neoplasia, eBioMedicine, Elsevier BV 104, 105171.
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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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  • 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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  • 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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  • Hinrichs, R. (2024)Kompression der Erregungsmuster von Cochlea-Implantaten, VDI Verlag.
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  • Xuan, Q. L., Munderloh, M., and Ostermann, J. (2024)Self-supervised Domain Adaptation for Machinery Remaining Useful Life Prediction (accepted), Journal on Reliability Engineering and System Safety, Special Issue: RUL Prediction and System Reliability of Complex Systems.
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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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  • 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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  • 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).
    BibTeXEndNoteBibSonomy
  • 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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  • 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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2023

  • 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. In Frontiers in Artificial Intelligence and Applications, pp. 2002–2009.
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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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • Rosenhahn, B., and Osborne, T. (2023)Monte Carlo Graph Search for Quantum Circuit Optimization (Accepted), Physical Review A.
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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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • Xu, L., Dockhorn, A., and Perez-Liebana, D. (2023)Elastic Monte Carlo Tree Search, IEEE Transactions on Games.
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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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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2022

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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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  • Xu, L., Hurtado-Grueso, J., Jeurissen, D., Liebana, D. P., and Dockhorn, A. (2022)Elastic Monte Carlo Tree Search State Abstraction for Strategy Game Playing. In 2022 IEEE Conference on Games (CoG).
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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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  • 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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  • 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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  • 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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  • 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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  • 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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  • Mohan, A., Ruhkopf, T., and Lindauer, M. (2022)Towards Meta-learned Algorithm Selection using Implicit Fidelity Information. In ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML), arXiv.
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  • Deng, D., Karl, F., Hutter, F., Bischl, B., and Lindauer, M. (2022)Efficient Automated Deep Learning for Time Series Forecasting. In Proceedings of the European Conference on Machine Learning (ECML), arXiv.
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  • Kellermann, C., Selmi, A., Brown, D., and Ostermann, J. (2022)Fault Detection in Multi-stage Manufacturing to Improve Process Quality. In International Conference on Control, Automation and Diagnosis (ICCAD), pp. 1–6.
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  • Benjamins, C., Raponi, E., Jankovic, A., van der Blom, K., Santoni, M. L., Lindauer, M., and Doerr, C. (2022)PI is back! Switching Acquisition Functions in Bayesian Optimization. In 2022 NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems, arXiv.
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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 16259.
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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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  • 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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  • 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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  • Pestel-Schiller, U., Yang, Y., and Ostermann, J. (2022)Semantic Segmentation of Natural and Man-Made Fruits Using a Spatial-Spectral Two-Channel-CNN for Sparse Data. In 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS).
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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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  • 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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  • 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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  • 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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  • 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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  • Ostermann, J., and Hinrichs, R. (2020)Links und rechts verbinden, Unimagazin.
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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • Pestel-Schiller, U., and Ostermann, J. (2020)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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  • 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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  • 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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  • 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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  • 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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  • Dockhorn, A. (2020)Dissertation: Prediction-based Search for Autonomous Game-Playing, Otto von Guericke University Magdeburg 1–231.
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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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  • 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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  • 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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  • {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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  • 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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  • 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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  • 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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  • 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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  • 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.
    URLBibTeXEndNoteBibSonomy
  • 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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  • Rudolph, M., Wandt, B., and Rosenhahn, B. (2020, August)Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows..
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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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  • Awiszus, M., Schubert, F., and Rosenhahn, B. (2020, October)TOAD-GAN: Coherent Style Level Generation from a Single Example.
    AbstractURLBibTeXEndNoteBibSonomy
  • 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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  • 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.
    URLBibTeXEndNoteBibSonomy
  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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.
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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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2019

  • 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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  • 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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  • 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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  • 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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  • 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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  • Dockhorn, A., and Mostaghim, S. (2019)Introducing the Hearthstone-AI Competition, arXiv:1906.04238 1–4.
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2018

  • 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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  • 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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  • 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., 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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  • 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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  • 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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  • 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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2017

  • 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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  • 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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2016

  • 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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  • 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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2015

  • 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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  • 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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