Publication list

2024

Harnessing Orthogonality to Train Low-Rank Neural Networks
Coquelin, D.; Flügel, K.; Weiel, M.; Kiefer, N.; Debus, C.; Streit, A.; Götz, M.
2024. ECAI 2024 : 27th European Conference on Artificial Intelligence 19–24 October 2024, Santiago de Compostela, Spain Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024 ; Proceedings. Ed.: U. Endriss, 2106–2113, IOS Press. doi:10.3233/FAIA240729

Taylor Expansion in Neural Networks: How Higher Orders Yield Better Predictions
Zwerschke, P.; Weyrauch, A.; Götz, M.; Debus, C.
2024. ECAI 2024 – 27th European Conference on Artificial Intelligence, 19–24 October 2024, Santiago de Compostela, Spain – Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024). Ed.: U. Endriss, 2983–2989, IOS Press. doi:10.3233/FAIA240838

ReCycle: Fast and Efficient Long Time Series Forecasting with Residual Cyclic Transformers
Weyrauch, A.; Steens, T.; Taubert, O.; Hanke, B.; Eqbal, A.; Götz, E.; Streit, A.; Götz, M.; Debus, C.
2024. 2024 IEEE Conference on Artificial Intelligence (CAI), Singapore, Singapore, 25-27 June 2024, 1187–1194, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/CAI59869.2024.00212

Feasibility of Forecasting Highly Resolved Power Grid Frequency Utilizing Temporal Fusion Transformers
Pütz, S.; El Ashhab, H.; Hertel, M.; Mikut, R.; Götz, M.; Hagenmeyer, V.; Schäfer, B.
2024. e-Energy ’24: Proceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems, 447–453, Association for Computing Machinery (ACM). doi:10.1145/3632775.3661963

iMagine, AI-supported imaging data and services for ocean and marine science
Sipos, G.; Schaap, D.; Lopez Garcia, A.; Kozlov, V.
2024. International Conference on Marine Data and Information Systems - Proceedings Volume. International Conference on Marine Data and Information Systems - Proceedings Volume., International Conference on Marine Data and Information Systems (IMDIS 2024 2024), Bergen, Norwegen, 27.05.2024–29.05.2024, 303–305

AI4EOSC - D6.2 Intermediate status report about integration of pilot applications
Kozlov, V.; Sainz-Pardo, J.; Berberi, L.; Alibabaei, K.; Vollmer, E.; Błaszczak, M.; Krzyżanek, M.; Smok, J.; Bartok, J.; Sisan, P.; Papadopoulos, G.; Izquierdo, P.
2024. Zenodo. doi:10.5281/zenodo.10729327

Reconstructing Particle Decay Trees with Quantum Graph Neural Networks for High Energy Physics
Strobl, M.; Kuehn, E.; Fischer, M.; Streit, A.
2024. 21st International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2022), Bari, I, October 23-28, 2022

Model Fusion via Neuron Transplantation
Öz, M.; Kiefer, N.; Debus, C.; Hörter, J.; Streit, A.; Götz, M.
2024. Machine Learning and Knowledge Discovery in Databases. Research Track : European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part IV. Ed. by Albert Bifet, Jesse Davis, Tomas Krilavičius, Meelis Kull, Eirini Ntoutsi, Indrė Žliobaitė, 3–19, Springer Nature Switzerland. doi:10.1007/978-3-031-70359-1_1

Personalized Federated Learning for improving radar based precipitation nowcasting on heterogeneous areas
Díaz, J. S.-P.; Castrillo, M.; Bartok, J.; Cachá, I. H.; Ondík, I. M.; Martynovskyi, I.; Alibabaei, K.; Berberi, L.; Kozlov, V.; García, Á. L.
2024. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000173713

(Semi-) Automatic Review Process for Common Compound Characterization Data in Organic Synthesis
Huang, Y.-C.; Tremouilhac, P.; Kuhn, S.; Huang, P.-C.; Lin, C.-L.; Schlörer, N.; Taubert, O.; Götz, M.; Jung, N.; Bräse, S.
2024. ChemRxiv. doi:10.26434/chemrxiv-2024-1r9tb

PETNet–Coincident Particle Event Detection using Spiking Neural Networks
Debus, J.; Debus, C.; Dissertori, G.; Götz, M.
2024. 2024 Neuro Inspired Computational Elements Conference (NICE), 9 S., Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/NICE61972.2024.10549584

2023

Understanding Scalable Perovskite Solar Cell Manufacturing with Explainable AI
Klein, L.; Ziegler, S.; Laufer, F.; Debus, C.; Götz, M.; Maier-Hein, K.; Paetzold, U.; Isensee, F.; Jaeger, P.
2023

Precise Energy Consumption Measurements of Heterogeneous Artificial Intelligence Workloads
Caspart, R.; Ziegler, S.; Weyrauch, A.; Obermaier, H.; Raffeiner, S.; Schuhmacher, L. P.; Scholtyssek, J.; Trofimova, D.; Nolden, M.; Reinartz, I.; Isensee, F.; Götz, M.; Debus, C.
2023. High Performance Computing. ISC High Performance 2022 International Workshops – Hamburg, Germany, May 29 – June 2, 2022, Revised Selected Papers. Ed.: H. Anzt, 108–121, Springer International Publishing. doi:10.1007/978-3-031-23220-6_8

perun: Benchmarking Energy Consumption of High-Performance Computing Applications
Gutiérrez Hermosillo Muriedas, J. P.; Flügel, K.; Debus, C.; Obermaier, H.; Streit, A.; Götz, M.
2023. Euro-Par 2023: Parallel Processing. Ed.: J. Cano, 17–31, Springer Nature Switzerland. doi:10.1007/978-3-031-39698-4_2

D6.1 : Analysis of user applications, collection of requirements
Berberi, L.; Heredia, I.; Kozlov, V.; Vollmer, E.; Sainz-Pardo, J.; Bartok, J.; Fojud, A.; Blaszczak, M.; Rausch, A.; Alibabaei, K.
2023. Zenodo. doi:10.5281/zenodo.7635453

D1.2 : AI4EOSC Data Management Plan
Aguilar, F.; Kozlov, V.; Heredia, I.
2023. Zenodo. doi:10.5281/zenodo.7649075

D3.1 : State of the art landscaping and initial platform requirements specification
Moltó, G.; Calatrava, A.; Nguyen, G.; Díaz, J. S.-P.; Berberi, L.; Kozlov, V.; Cruz, M. S.
2023. Zenodo. doi:10.5281/zenodo.7635430

iMagine D3.1 : Technical development roadmap for the AI image analysis use cases
Kozlov, V.
2023. Zenodo. doi:10.5281/zenodo.7760413

D3.2 : Initial High Level Architecture
García, Á. L.; Berberi, L.; Kozlov, V.; Antonacci, M.; Calatrava, A.; Langarita, S.; Moltó, G.
2023. Zenodo. doi:10.5281/zenodo.7728882

Massively Parallel Genetic Optimization Through Asynchronous Propagation of Populations
Taubert, O.; Weiel, M.; Coquelin, D.; Farshian, A.; Debus, C.; Schug, A.; Streit, A.; Götz, M.
2023. High Performance Computing – 38th International Conference, ISC High Performance 2023, Hamburg, Germany, May 21–25, 2023, Proceedings. Ed.: A. Bhatele, 106 – 124, Springer Nature Switzerland. doi:10.1007/978-3-031-32041-5_6

Training Parameterized Quantum Circuits with Triplet Loss
Wendenius, C.; Kuehn, E.; Streit, A.
2023. Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings. Part V. Ed.: M.-R. Amini, 515–530, Springer Nature Switzerland. doi:10.1007/978-3-031-26419-1_31

2022

A Computational Workflow for Interdisciplinary Deep Learning Projects utilizing bwHPC Infrastructure
Schilling, M.; Neumann, O.; Scherr, T.; Cui, H.; Popova, A. A.; Levkin, P. A.; Götz, M.; Reischl, M.
2022. Proceedings of the 7th bwHPC Symposium, 69–74, Kommunikations- und Informationszentrum (kiz). doi:10.18725/OPARU-46069

Hyde: The First Open-Source, Python-Based, Gpu-Accelerated Hyperspectral Denoising Package
Coquelin, D.; Rasti, B.; Gotz, M.; Ghamisi, P.; Gloaguen, R.; Streit, A.
2022. 2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Rome, Italy, 13-16 September 2022, 1–5, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/WHISPERS56178.2022.9955088

iMagine D4.1 : Best practices and guideline for developers and providers of AI-based image analytics services
Heredia, I.; Kozlov, V.
2022. Zenodo. doi:10.5281/zenodo.7372358

Prediction of Optimal Solvers for Sparse Linear Systems Using Deep Learning
Funk, Y.; Götz, M.; Anzt, H.
2022. Proceedings of the 2022 SIAM Conference on Parallel Processing for Scientific Computing (PP). Ed.: X. Li, 14–24, Society for Industrial and Applied Mathematics (SIAM). doi:10.1137/1.9781611977141.2

A survey of the European Open Science Cloud services for expanding the capacity and capabilities of multidisciplinary scientific applications
Calatrava, A.; Asorey, H.; Astalos, J.; Azevedo, A.; Benincasa, F.; Blanquer, I.; Bobak, M.; Brasileiro, F.; Codó, L.; Cano, L. del; Esteban, B.; Ferret, M.; Handl, J.; Kerzenmacher, T.; Kozlov, V.; et al.
2022. arxiv. doi:10.48550/ARXIV.2211.07738

A container-based workflow for distributed training of deep learning algorithms in HPC clusters
González-Abad, J.; López García, Á.; Kozlov, V. Y.
2022. doi:10.5445/IR/1000152892

2021

Evolutionary Optimization of Neural Architectures in Remote Sensing Classification Problems
Coquelin, D.; Sedona, R.; Riedel, M.; Götz, M.
2021. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 12-16 July 2021, 1587–1590, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/IGARSS47720.2021.9554309

Accelerating Neural Network Training with Distributed Asynchronous and Selective Optimization (DASO)
Coquelin, D.; Debus, C.; Götz, M.; Lehr, F. von der; Kahn, J.; Siggel, M.; Streit, A.
2021. Springer. doi:10.21203/rs.3.rs-832355/v1

2020

HeAT – a Distributed and GPU-accelerated Tensor Framework for Data Analytics
Götz, M.; Coquelin, D.; Debus, C.; Krajsek, K.; Comito, C.; Knechtges, P.; Hagemeier, B.; Tarnawa, M.; Hanselmann, S.; Siggel, M.; Basermann, A.; Streit, A.
2020. doi:10.5445/IR/1000123473

High-performance data analytics of hybrid rocket fuel combustion data using different machine learning approaches
Debus, C.; Ruettgers, A.; Petrarolo, A.; Kobald, M.; Siggel, M.
2020. AIAA Scitech 2020 Forum, American Institute of Aeronautics and Astronautics. doi:10.2514/6.2020-1161

HeAT - A Distributed and GPU-accelerated Tensor Framework for Data Analytics
Götz, M.; Debus, C.; Coquelin, D.; Krajsek, K.; Comito, C.; Knechtges, P.; Hagemeier, B.; Tarnawa, M.; Hanselmann, S.; Siggel, M.; Basermann, A.; Streit, A.
2020. 2020 IEEE International Conference on Big Data (Big Data): 10-13 December 2020, online, 276–287, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/BigData50022.2020.9378050

Loss Scheduling for Class-Imbalanced Segmentation Problems
Taubert, O.; Götz, M.; Schug, A.; Streit, A.
2020. 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 422–427, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICMLA51294.2020.00073

HeAT – a Distributed and GPU-accelerated TensorFramework for Data Analytics
Götz, M.; Debus, C.; Coquelin, D.; Krajsek, K.; Comito, C.; Knechtges, P.; Hagemeier, B.; Tarnawa, M.; Hanselmann, S.; Siggel, M.; Basermann, A.; Streit, A.
2020. 2020 IEEE International Conference on Big Data (Big Data), 276–287, Institute of Electrical and Electronics Engineers (IEEE)

2019

Benchmarking Deep Learning Infrastructures by Means of TensorFlow and Containers
Grupp, A.; Kozlov, V.; Campos, I.; David, M.; Gomes, J.; López García, Á.
2019. High Performance Computing : ISC High Performance 2019 International Workshops, Frankfurt, Germany, June 16-20, 2019. Ed.: M. Weiland, 478–489, Springer International Publishing. doi:10.1007/978-3-030-34356-9_36

Machine learning-aided numerical linear Algebra: Convolutional neural networks for the efficient preconditioner generation
Götz, M.; Anzt, H.
2019. Proceedings of ScalA 2018: 9th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, 49–56, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ScalA.2018.00010

Remote Sensing Data Analytics with the Udocker Container Tool using Multi-GPU Deep Learning Systems
Cavallaro, G.; Kozlov, V.; Götz, M.; Riedel, M.
2019. Proceedings of 2019 Big Data from Space (BiDS’19). Ed.: S. Pierre, 177–180. doi:10.2760/848593

2018

The Helmholtz Analytics Toolkit (HEAT): A scientific Big Data Library for HPC
Krajsek, K.; Comito, C.; Götz, M.; Hagemeier, B.; Knechtges, P.; Siggel, M.
2018. Proceedings of the Extreme Data Workshop 2018

2016

Online Distance Measurement for Tree Data Event Streams
Kuehn, E.; Streit, A.
2016. 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech), Auckland, New Zealand, 8–12 August 2016. Ed. : Q. Jin, 681–688, IEEE Computer Society. doi:10.1109/DASC-PICom-DataCom-CyberSciTec.2016.122

Data locality via coordinated caching for distributed processing
Fischer, M.; Kuehn, E.
2016. 7th International Conference on Cloud Computing, GRIDs, and Virtualization, Roma, I, March 20-24, 2016. Ed.: C. Becker Westphall, 113–118, International Academy, Research, and Industry Association (IARIA)

2015

Clustering evolving batch system jobs for online anomaly detection
Kuehn, E.
2015. 15th IEEE International Conference on Data Mining, Atlantic City, NJ, November 14-17, 2015. Ed.: P. Cui, 1534–1535, IEEE Computer Society. doi:10.1109/ICDMW.2015.219

2014

Monitoring data streams at process level in scientific big data batch clusters
Kuehn, E.; Fischer, M.; Jung, C.; Petzold, A.; Streit, A.
2014. Proceedings of the International Symposium on Big Data Computing (BDC 2014), London, UK, December 8-9, 2014, 90–952, IEEE Computer Society. doi:10.1109/BDC.2014.21