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Latest publications

2020
  • Mihail Bogojeski, Leslie Vogt-Maranto, Mark E. Tuckerman, Klaus-Robert Müller, Kieron Burke:
    Quantum chemical accuracy from density functional approximations via machine learning
    Nature Communications 11:5223, 2020
    https://doi.org/10.1038/s41467-020-19093-1
  • Dong-Ok Won, Klaus-Robert Müller, Seong-Whan Lee:
    An adaptive deep reinforcement learning framework enables curling robots with human-like performance in real-world conditions
    Science Robotics Vol. 5, Issue 46, eabb9764. September 23, 2020
    DOI: 10.1126/scirobotics.abb9764
  • Philipp Leinen, Malte Esders, Kristof T. Schütt, Christian Wagner, Klaus-Robert Müller, F. Stefan Tautz:
    Autonomous robotic nanofabrication with reinforced learning
    Science Advances Vol. 6, No. 36, 2020
  • Anja Feldmann, Oliver Gasser, Franziska Lichtblau, Enric Pujol, Ingmar Poese, Christoph Dietzel, Daniel Wagner, Matthias Wichtlhuber, Juan Tapiador, Narseo Vallina-Rodriguez, Oliver Hohlfeld, and Georgios Smaragdakis:
    „The Lockdown Effect: Implications of the COVID-19 Pandemic on Internet Traffic“
    ACM IMC 2020
  • Said Jawad Saidi, Anna Maria Mandalari, Roman Kolcun, Hamed Haddadi, Daniel J. Dubois, David Choffnes, Georgios Smaragdakis, and Anja Feldmann:
    „A Haystack Full of Needles: Scalable Detection of IoT Devices in the Wild“
    ACM IMC 2020
  • John P. Rula, Philipp Richter, Georgios Smaragdakis, and Arthur Berger:
    „Who’s left behind? Measuring Adoption of Application Updates at Scale“
    ACM IMC 2020
  • Srdjan Matic, Costas Iordanou, Georgios Smaragdakis, and Nikolaos Laoutaris:
    „Identifying Sensitive URLs at Web-Scale“
    ACM IMC 2020
  • Le-Tuan Anh; Hayes, Conor; Hauswirth, Manfred; Le-Phuoc, Danh:
    „Pushing the Scalability of RDF Engines on IoT Edge Devices.“
    Sensors 2020, no. 10: 2788, 2020
  • Nguyen Duc-M., Le-Tuan A., Calbimonte JP., Hauswirth M., Le-Phuoc D.:
    „Autonomous RDF Stream Processing for IoT Edge Devices.“
    InSemantic Technology. JIST 2019. Lecture Notes in Computer Science, vol 12032. Springer, Cham.
  • Felix Sattler, Klaus-Robert Müller, and Wojciech Samek:
    „Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints“
    IEEE Transactions on Neural Networks and Learning Systems, 2020
  • Johanna Vielhaben, Markus Wenzel, Wojciech Samek, and Nils Strodthoff:
    „USMPep: Universal Sequence Models for Major Histocompatibility Complex Binding Affinity Prediction“
    BMC Bioinformatics, 21:279, 2020
  • Clemens Seibold, Wojciech Samek, Anna Hilsmann, and Peter Eisert:
    „Accurate and Robust Neural Networks for Face Morphing Attack Detection“
    Journal of Information Security and Applications, 53:102526, 2020
  • Patrick Wagner, Nils Strodthoff, Ralf-Dieter Bousseljot, Dieter Kreiseler, Fatima I. Lunze, Wojciech Samek, and Tobias Schaeffter:
    „PTB-XL, A Large Publicly Available Electrocardiography Dataset“
    Scientific Data, 7:154, 2020
  • Miriam Hägele, Philipp Seegerer, Sebastian Lapuschkin, Michael Bockmayr, Wojciech Samek, Frederick Klauschen, Klaus-Robert Müller, and Alexander Binder:
    „Resolving Challenges in Deep Learning-Based Analyses of Histopathological Images using Explanation Methods“
    Scientific Reports, 10:6423, 2020
  • Falk Schwendicke, Wojciech Samek, and Joachim Krois:
    „Artificial Intelligence in Dentistry: Chances and Challenges“
    Journal of Dental Research, 99(7):769-774, 2020
  • Felix Sattler, Thomas Wiegand and Wojciech Samek:
    „Trends and Advancements in Deep Neural Network Communication“
    ITU Journal: ICT Discoveries, 3(1), 2020
  • Luis Oala, Cosmas Heiß, Jan Macdonald, Maximilian März, Wojciech Samek, and Gitta Kutyniok:
    „Detecting Failure Modes in Image Reconstructions with Interval Neural Network Uncertainty“
    Proceedings of the ICML’20 Workshop on Uncertainty & Robustness in Deep Learning, 2020
  • Gary S. W. Goh, Sebastian Lapuschkin, Leander Weber, Wojciech Samek, and Alexander Binder:
    „Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution“
    Proceedings of the 25th International Conference on Pattern Recognition (ICPR), 2020
  • David Neumann, Felix Sattler, Heiner Kirchhoffer, Simon Wiedemann, Karsten Müller, Heiko Schwarz, Thomas Wiegand, Detlev Marpe, and Wojciech Samek:
    „DeepCABAC: Plug&Play Compression of Neural Network Weights and Weight Updates“
    Proceedings of the IEEE International Conference on Image Processing (ICIP), 2020
  • Paul Haase, Heiko Schwarz, Heiner Kirchhoffer, Simon Wiedemann, Talmaj Marinc, Arturo Marban, Karsten Müller, Wojciech Samek, Detlev Marpe, and Thomas Wiegand:
    „Dependent Scalar Quantization for Neural Network Compression“
    Proceedings of the IEEE International Conference on Image Processing (ICIP), 2020
  • Simon Wiedemann, Temesgen Mehari, Kevin Kepp, and Wojciech Samek:
    „Dithered backprop: A sparse and quantized backpropagation algorithm for more efficient deep neural network training“
    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 3096-3104, 2020
  • Arturo Marban, Daniel Becking, Simon Wiedemann, and Wojciech Samek:
    „Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T)“
    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 3105-3113, 2020
  • Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima, Alexander Binder, Wojciech Samek, and Sebastian Lapuschkin:
    „Towards best practice in explaining neural network decisions with LRP“
    Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), 2020
  • Vignesh Srinivasan, Klaus-Robert Müller, Wojciech Samek, and Shinichi Nakajima:
    „Benign Examples: Imperceptible Changes Can Enhance Image Translation Performance“
    Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020
  • Habib Mostafaei, Gabriele Lospoto, Roberto Di Lallo, Massimo Rimondini, Giuseppe Di Battista :
    „A framework for multi-provider virtual private
    networks in software-defined federated networks“

    International Journal of Network Management, 2020
    https://doi.org/10.1002/nem.2116
  • M. Imran, G. Gévay, V. Markl:
    „Distributed Graph Analytics with Datalog Queries in Flink“
    LSGDA 2020
  • M. Ghanbari, U. Ohler:
    „Deep neural networks for interpreting RNA-binding protein target preferences“
    Genome Research 30 (2), 214-226, 2020
  • Alexander Renz-Wieland, Rainer Gemulla, Steffen Zeuch, Volker Markl:
    „Dynamic Parameter Allocation in Parameter Servers“
    PVLDB Vol. 13 (11), 2020
  • Ç. Yapar, R. Levie, G. Kutyniok, G. Caire:
    Real-time Localization Using Radio Maps
    arXiv:2006.05397, 2020
  • R. Levie, Ç. Yapar, G. Kutyniok, G. Caire:
    „Pathloss Prediction using Deep Learning with Applications to Cellular Optimization and Efficient D2D Link Scheduling“
    IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
  • Julius Hülsmann, Jonas Traub, Volker Markl:
    „Demand-based Sensor Data Gathering with Multi-Query Optimization“
    Proceedings of the VLDB Endowment, Vol. 13 No. 12, 2020
  • Niklas Semmler, Matthias Rost, Georgios Smaragdakis, and Anja Feldmann:
    „Edge Replication Strategies for Wide-Area Distributed Processing“
    ACM EdgeSys, 2020
  • Apoorv Shukla, Kevin Nico Hudemann, Zsolt Vági, Lily Hügerich, Georgios Smaragdakis, Stefan Schmid, Artur Hecker, Anja Feldmann:
    „Towards Runtime Verification of Programmable Switches“
    CoRR abs/2004.10887, 2020
  • Ke Yang, Biao Huang, Julia Stoyanovich, Sebastian Schelter:
    „Fairness-Aware Instrumentation of Preprocessing Pipelines for Machine Learning“
    Human-In-the-Loop Data Analytics workshop at ACM SIGMOD, 2020
  • Sergey Redyuk, Volker Markl, Sebastian Schelter:
    „Towards Unsupervised Data Quality Validation on Dynamic Data.“
    Workshop on Explainability for Trustworthy ML Pipelines at EDBT, 2020
  • Tammo Rukat, Dustin Lange, Sebastian Schelter, Felix Biessmann:
    „Towards Automated ML Model Monitoring: Measure, Improve and Quantify Data Quality“
    ML Ops workshop at the Conference on Machine Learning and Systems (MLSys), 2020
  • Philipp Benner, Martin Vingron:
    „ModHMM: A modular supra-Bayesian genome segmentation method.“
    Journal of Computational Biology 27.4.: 442-457, 2020
  • Sebastian Schelter, Yuxuan He, Jatin Khilnani, Julia Stoyanovich:
    „FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventions.“
    International Conference on Extending Database Technology (EDBT), 2020
  • Sebastian Schelter:
    „‚Amnesia‘ – A Selection of Machine Learning Models That Can Forget User Data Very Fast“
    Conference on Innovative Data Systems Research (CIDR), 2020.
  • Sebastian Schelter, Tammo Rukat, Felix Biessmann:
    „Learning to Validate the Predictions of Black box Classfiers on Unseen Data.“
    ACM SIGMOD, 2020
  • O Eberle, J Büttner, F Kräutli, KR Müller, M Valleriani, G Montavon:
    „Building and Interpreting Deep Similarity Models“
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020
    DOI: 10.1109/TPAMI.2020.3020738
  • D. A. Awan, R.L.G. Cavalcante, M. Yukawa and S. Stanczak:
    „Adaptive Learning for Symbol Detection. Machine Learning for Future Wireless Communications.“
    Wiley & IEEE Press, 15., 2020
  • Valleriani, Matteo (ed.):
    „De sphaera of Johannes de Sacrobosco in the Early Modern Period: The Authors of the Commentaries.“
    Springer Nature, 2020
    https://doi.org/10.1007/978-3-030-30833-9
  • D. A. Awan, Renato L.G. Cavalcante and Slawomir Stanczak:
    „Robust Cell-Load Learning with a Small Sample Set.“
    IEEE Transactions on Signal Processing (TSP), 68:270-283., 2020
  • Cheng-Xiang Wang, Marco Di Renzo, Slawomir Stanczak, Sen Wang, Erik G. Larsson:
    „Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges.“
    IEEE Wireless Communications Magazine, February 2020/Intelligent Radio: When Artificial Intelligence Meets Radio Network, 2020
  • M Frey, I Bjelaković, S Stańczak:
    „Securing Distributed Function Approximation via Coding for Continuous Compound Channels“
    arXiv:2001.03174, 2020
  • F. Ganji, S. Amir, S. Tajik, D. Forte, J.-P. Seifert:
    Machine Learning Based Adversary Modeling for Composed Hardware
    To appear in Design, Automation and Test (DATE), 2020
  • F. Ganji, S. Tajik, P. Stauss, J.-P. Seifert, D. Forte, M. Tehranipoor:
    „Rock’n’roll PUFs: Crafting Provably Secure PUFs from Less Secure Ones“
    To appear in Journal of Cryptographic Engineering, 2020
  • N. Wisiol, N. Pirnay:
    „XOR Arbiter PUFs have Systematic Response Bias“
    To appear at Financial Cryptography 2020
  • N. Wisiol, C. Mühl, N. Pirnay, P. H. Nguyen, M. Margraf, J.-P. Seifert, M. van Dijk, U. Rührmair:
    „Splitting the Interpose PUF: A Novel Modeling Attack Strategy“
    To appear in TCHES 2020, Issue 3
  • Erwin Quiring, David Klein, Daniel Arp, Martin Johns, Konrad Rieck:
    „Adversarial Preprocessing: Understanding and Preventing Image-Scaling Attacks in Machine Learning“
    Proc. of the 29th USENIX Security Symposium, to appear August 2020
  • Alexander Warnecke, Daniel Arp, Christian Wressnegger, Konrad Rieck:
    „Evaluating Explanation Methods for Deep Learning in Computer Security“
    Proc. of the 5th IEEE European Symposium on Security and Privacy (EuroS&P), to appear June 2020.
  • A. Kaitoua, T. Rabl, V. Markl:
    „A distributed data exchange engine for polystores“
    it – Information Technology Vol. 1, 2020
  • Makait, H.:
    „Rethinking Message Brokers on RDMA and NVM.Proceedings of the 2020 International Conference on Management of Data.“
    ACM, Portland, OR, USA, 2020
  • Dreseler, M., Boissier, M., Rabl, T., Uflacker, M.:
    „Quantifying TPCH Choke Points and Their Optimizations [Experiments and Analyses]“
    Proceedings of the VLDB Endowment, 2020
  • Pedro Silva, Wang Yue, Tilmann Rabl:
    „Incremental Stream Query Analytics“
    DEBS, 2020
  • Julia Markowski, Rieke Kempfer, Alexander Kukalev, Ibai Irastorza-Azcarate, Gesa Loof, Ana Pombo, Roland F. Schwarz:
    „GAMIBHEAR: whole-genome haplotype reconstruction from Genome Architecture Mapping data“
    bioRxiv, 2020
    DOI: 10.1101/2020.01.30.927061
  • S. López E.L. Lim S. Horswell K. Haase A. Huebner M. Dietzen T.P. Mourikis T.B.K. Watkins A. Rowan S.M. Dewhurst N.J. Birkbak G.A. Wilson P. Van Loo M. Jamal-Hanjani C. Swanton N. McGranahan:
    „Interplay between whole-genome doubling and the accumulation of deleterious alterations in cancer evolution“
    Nat Genet 52 (3): 283-293, 2020
  • Richard P. Koche, Roland F. Schwarz et al.:
    „Extrachromosomal circular DNA drives oncogenic genome remodeling in neuroblastoma“
    Nature Genetics volume 52, pages29–34, 2020
  • C. Calabrese, Roland F. Schwarz et al.:
    „Genomic basis for RNA alterations in cancer“
    Nature 578 (7793): 129-136, 2020
  • Peter J. Campbell, Roland F. Schwarz et al.:
    „Pan-cancer analysis of whole genomes“
    Nature 578 (7793): 82-93, 2020
  • Alt, Christoph; Gabryszak, Aleksandra; Hennig, Leonhard:
    „TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task“
    in Proceedings of ACL, 2020
  • Alt, Christoph; Gabryszak, Aleksandra; Hennig, Leonhard:
    „Probing Linguistic Features of Sentence-level Representations in Neural Relation Extraction“
    in Proceedings of ACL, 2020
  • Ulrich Meyer, Ziawasch Abedjan:
    „Algorithms for Big Data“
    In: it – Information Technology, Vol. 1, 2020
  • L. Visengeriyeva, Z. Abedjan:
    „Anatomy of metadata for data curation“
    Journal of Data and Information Quality (JDIQ), 2020
  • Ziawasch Abedjan, Hagen Anuth, Mahdi Esmailoghli, Mohammad Mahdavi, Felix Neutatz, Binger Chen:
    „Data Science für alle: Grundlagen der Datenprogrammierung“
    Informatik Spektrum, pp. 1-8, 2020
  • Jürgen Renn:
    The evolution of knowledge: rethinking science for the Anthropocene
    Princeton: Princeton University Press, 2020
  • Lalli, R., Howey, R. T., & Wintergrün, D.:
    „The dynamics of collaboration networks and the history of general relativity, 1925–1970“
    Scientometrics, 122(2), 1129-1170, 2020
    DOI: 10.1007/s11192-019-03327-1.
  • Lalli, R., Howey, R. T., & Wintergrün, D.:
    „The socio-epistemic networks of general relativity, 1925–1970“
    In: Revisiting the renaissance of General Relativity: Social and epistemic perspectives, ed. A. Blum, R. Lalli, J. Renn, Boston: Birkhäuser, in press, 2020
  • Lauritz Thamsen, Jossekin Beilharz, Vinh Thuy Tran, Sasho Nedelkoski, Odej Kao:
    „Mary, Hugo, and Hugo*: Learning to Schedule Distributed Data-Parallel Processing Jobs on Shared Clusters“
    Concurrency and Computation: Practice and Experience (to appear). Wiley, 2020
  • Nedelkoski Sasho, Bogojeski Mihail, Odej Kao:
    „Learning More Expressive Joint Distributions in Multimodal Cariational Methods“
    To appear in the Proceedings of the Annual Conference on Machine Learning, Optimization, and Data Science (LOD). Springer, 2020
  • Dimitrios Giouroukis, Alexander Dadiani, Jonas Traub, Steffen Zeuch, Volker Markl:
    „A Survey of Adaptive Sampling and Filtering Algorithms for the Internet of Things“
    accepted at the 14th ACM International Conference on Distributed and Event-Based Systems (DEBS), 2020
  • Sebastian Kruse, Zoi Kaoudi, Sanjay Chawla, Felix Naumann, Bertty Contreras-Rojas, Jorge-Arnulfo Quiané-Ruiz:
    „RHEEMix in the Data Jungle: A Cost-based Optimizer for Cross-Platform Systems“
    to appear in The VLDB Journal (VLDBJ), 2020
  • Zoi Kaoudi, Jorge-Arnulfo Quiané-Ruiz, Bertty Contreras-Rojas, Rodrigo Pardo-Meza, Anis Troudi, Sanjay Chawla:
    „ML-based Cross-Platform Query Optimization“
    IEEE 36th International Conference on Data Engineering (ICDE), 2020
  • Schütt, K.T.; Chmiela, S.; von Lilienfeld, O.A.; Tkatchenko, A.; Tsuda, K.; Müller, K.-R. (Eds.):
    „Machine Learning Meets Quantum Physics“
    Lecture Notes in Physics, Springer, 2020
  • Del Monte, Bonaventura; Zeuch, Steffen; Rabl, Tilmann; Markl, Volker:
    „Rhino: Efficient Management of Very Large Distributed State for Stream Processing Engines“
    accepted at ACM SIGMOD/PODS International Conference on the Management of Data, 2020
  • Derakhshan, Behrouz; Mahdiraji, Alireza Rezaei; Abedjan, Ziawasch; Rabl, Tilmann; Markl, Volker:
    „Optimizing Machine Learning Workloads in Collaborative Environments“
    accepted at ACM SIGMOD/PODS International Conference on the Management of Data, 2020
  • Grulich, Philipp M.; Breß, Sebastian; Zeuch, Steffen; Traub, Jonas; von Bleichert, Janis; Chen, Zongxiong; Rabl, Tilmann; Markl, Volker:
    „Grizzly: Efficient Stream Processing Through Adaptive Query Compilation“
    accepted at ACM SIGMOD/PODS International Conference on the Management of Data, 2020
  • Lutz, Clemens; Breß, Sebastian; Zeuch, Steffen; Rabl, Tilman; Markl, Volker:
    „Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects“
    accepted at ACM SIGMOD/PODS International Conference on the Management of Data, 2020
  • Wiedemann, Simon; Kirchhoffer, Heiner; Matlage, Stefan; Haase, Paul; Marban, Arturo; Marinc, Talmaj; Neumann, David; Nguyen, Tung; Osman, Ahmed; Schwarz, Heiko; Marpe, Detlev; Wiegand, Thomas; Samek, Wojciech:
    „DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks“
    IEEE Journal of Selected Topics in Signal Processing, 14(3):1-15, 2020
  • Strodthoff, Nils; Wagner, Patrick; Wenzel, Markus; Samek, Wojciech:
    „Universal Deep Sequence Models for Protein Classification“
    Bioinformatics, btaa003, 2020
  • Samek, Wojciech:
    „Learning with Explainable Trees“
    Nature Machine Intelligence, 2:16-17, 2020
  • Sattler, Felix; Müller, Klaus-Robert; Wiegand, Thomas; Samek, Wojciech:
    „On the Byzantine Robustness of Clustered Federated Learning“
    Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
2019
  • Le-Tuan A., Hingu D., Hauswirth M., Le-Phuoc D.:
    „Incorporating Blockchain into RDF Store at the Lightweight Edge Devices.“
    Semantic Systems. The Power of AI and Knowledge Graphs. SEMANTiCS 2019. Lecture Notes in Computer Science, vol 11702. Springer, Cham, 2019
  • Y. Song, M. B. Khalilsarai, S. Haghighatshoar, G. Caire:
    „Machine Learning for Geometrically-Consistent Angular Spread Function Estimation in Massive MIMO“
    arXiv:1910.13795, 2020
  • Denise Thiel, Natasa Djurdjevac Conrad, Evgenia Ntini, Ria Peschutter, Heike Siebert, Annalisa Marsico:
    „Identifying lncRNA-mediated regulatory modules via ChIA-PET network analysis“
    BMC Bioinformatics, 20(1471-2105), 2019
  • Rabl, T., Brücke, C., Härtling, P., Stars, S., Palacios, R.E., Patel, H., Srivastava, S., Boden, C., Meiners, J., Schelter, S.:
    „ADABench – Towards an Industry Standard Benchmark for Advanced Anayltics.“
    2019
  • Konstantinos Poularakis, George Iosifidis, Georgios Smaragdakis, Leandros Tassiulas:
    „Optimizing Gradual SDN Upgrades in ISP Networks“
    IEEE/ACM Transactions on Networking, 27(1), 2019.
  • Niklas Semmler, Georgios Smaragdakis, and Anja Feldmann:
    „Online Replication Strategies for Distributed Data Stores“
    Very Large Internet of Things (VLIoT 2019), in conjunction with VLDB 2019
  • Niklas Semmler, Georgios Smaragdakis, Anja Feldmann:
    „Distributed Mega-Datasets: The Need for Novel Computing Primitives“
    IEEE ICDCS 2019
  • Lauritz Thamsen, Ilya Verbitskiy, Sasho Nedelkoski, Vinh Thuy Tran, Vinícius Meyer, Miguel G. Xavier, Odej Kao, César A. F. De Rose:
    „Hugo: A Cluster Scheduler that Efficiently Learns to Select Complementary Data-Parallel Job“
    To appear in the Proceedings of the Euro-Par 2019 Workshops. Springer, 2019
  • Tobias Zehnder, Philipp Benner, Martin Vingron:
    „Predicting enhancers in mammalian genomes using supervised hidden Markov models.“
    BMC Bioinformatics 20.1: 157, 2019
  • Ramisch, A.,Vingron, M. et al.:
    „CRUP: a comprehensive framework to predict condition-specific regulatory units.“
    Genome Biology, 20.1, 1-23, 2019
  • Valleriani, Matteo, Florian Kräutli, Maryam Zamani, Alejandro Tejedor, Christoph Sander, Malte Vogl, Sabine Bertram, Gesa Funke, and Holger Kantz:
    „The Emergence of Epistemic Communities in the Sphaera Corpus: Mechanisms of Knowledge Evolution.“
    Journal of Historical Network Research 3: 50–91., 2019
  • Rabl, T., Brücke, C., Härtling, P., Stars, S., Palacios, R.E., Patel, H., Srivastava, S., Boden, C., Meiners, J., Schelter, S.:
    „ADABench – Towards an Industry Standard Benchmark for Advanced Anayltics.“
    2019
  • F. Ganji, S. Tajik, P. Stauss, J.P. Seifert, D. Forte, M. Tehranipoor:
    „Rock’n’roll PUFs: Crafting Provably Secure PUFs from Less Secure Ones“
    In Security Proofs for Embedded Systems (PROOFS), 2019
  • F. Ganji, J.P. Seifert, D. Forte:
    „PUFmeter: A Property Testing Tool for Assessing the Robustness of Physically Unclonable Functions to Machine Learning Attacks“
    In IEEE Access, 2019
  • N. Wisiol, G.T. Becker, M. Margarf, T. Soroceanu, J. Tobisch, B. Zengin:
    „Breaking the Lightweight Secure PUF: Understanding the Relation of Input Transformation and Machine Learning Resistance“
    In Smart Card Research and Advanced Application Conference (CARDIS), 2019
  • Alt, Christoph; Hübner, Marc; Hennig, Leonhard:
    „Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction“
    in Proceedings of ACL, 2019
  • Sasho Nedelkoski, Lauritz Thamsen, Ilya Verbitskiy, Odej Kao:
    „Multilayer Active Learning for Efficient Learning and Resource Usage in Distributed IoT Architectures“
    In Proceedings of the IEEE International Conference on Edge Computing (EDGE 2019). IEEE, 2019
  • Nedelkoski, Sasho, Jorge Cardoso, Odej Kao:
    „Anomaly Detection from System Tracing Data using Multimodal Deep Learning“
    In the Proceedings of the IEEE International Conference on Cloud Computing (CLOUD). IEEE, 2019
  • Morgan K. Geldenhuys, Lauritz Thamsen, Kain Kordian Gontarska, Felix Lorenz, Odej Kao:
    „Effectively Testing System Configurations of Critical IoT Analytics Pipelines“
    In the Proceedings of the IEEE International Conference on Big Data (IEEE BigData). IEEE, 2019
  • Lauritz Thamsen, Ilya Verbitskiy, Sasho Nedelkoski, Vinh Thuy Tran, Vinícius Meyer, Miguel G. Xavier, Odej Kao, César A. F. De Rose:
    „Hugo: A Cluster Scheduler that Efficiently Learns to Select Complementary Data-Parallel Job“
    To appear in the Proceedings of the Euro-Par 2019 Workshops. Springer, 2019
  • Redyuk, S.; Schelter, S.; Rukat, T.; Markl, V.; F. Biessmann, F.:
    „Learning to Validate the Predictions of Black Box Machine Learning Models on Unseen Data“
    HILDA’19, Amsterdam, Netherlands, 2019
  • Redyuk, S.:
    „Automated Documentation of End-to-End Experiments in Data Science“
    In Ph.D. Symposium track, IEEE 35th International Conference on Data Engineering (ICDE’19), 2019
  • Karimov, Jeyhun; Rabl, Tilmann; Markl, Volker:
    AJoin: Adhoc Stream Joins at Scale
    Proceedings of the VLDB Endowment, Vol. 13, No. 4, 2019
    https://doi.org/10.14778/3372716.3372718
  • Behnke, Ilja; Thamsen, Lauritz; Kao, Odej Héctor:
    A Framework for Testing IoT Applications Across Heterogeneous Edge and Cloud Testbeds
    Proceedings of the 12th {IEEE/ACM} International Conference on Utility and Cloud Computing, {UCC} 2019, pp. 15 – 20, 2019
    https://doi.org/10.1145/3368235.3368832
  • Nicoli, KA; Nakajima, S; Strodthoff, N; Samek, W; Kessel, P:
    Asymptotically Unbiased Generative Neural Sampling
    2019
    arXiv:1910.13496
  • Salem, Farouk; Schintke, Florian; Schütt, Thorsten; Reinefeld, Alexander:
    Scheduling data streams for low latency and high throughput on a Cray XC40 using Libfabric
    Concurrency and Computation Practice and Experience, pp. 1 – 14, 2019
    https://doi.org/10.1002/cpe.5563
  • Semmler, Niklas; Smaragdakis, Georgios; Feldmann, Anja:
    Distributed Mega-Datasets: The Need for Novel Computing Primitives
    39th {IEEE} International Conference on Distributed Computing Systems, 2019
    htps://doi.org/10.1109/ICDCS.2019.00167
  • Valeriani, M; Kräutli, F; Zamani, M; Tejedor, A; Sander, C; Vogl, M; Bertram, S; Funke, G; Kantz, H:
    The Emergence of Epistemic Communities in the Sphaera Corpus
    Journal of Historical Network Research, 3 (1), pp. 50-91, 2019
    https://doi.org/10.25517/jhnr.v3i1.63
  • Vidaurre C; Nolte, G; de Vries, IEJ; Gómez, M; Boonstra, TW; Müller, KR; Villringer, A; Nikulin, VV:
    Canonical maximization of coherence: A novel tool for investigation of neuronal interactions between two datasets
    NeuroImage, 201, 2019
    https://doi.org/10.1016/j.neuroimage.2019.116009
  • von Lühmann, A; Boukouvalas, Z; Müller, KR; Adalı, T:
    A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared Spectroscopy
    NeuroImage, 200, pp. 72-88, 2019
    https://doi.org/10.1016/j.neuroimage.2019.06.021
  • Mahdavi, Mohammad; Neutatz, Felix; Visengeriyeva, Larysa; Abedjan, Ziawasch:
    Towards Automated Data Cleaning Workflows
    Proceedings of the Conference on „Lernen, Wissen, Daten, Analysen“ (LWDA2019), pp. 10-19, 2019
    http://ceur-ws.org/Vol-2454/paper_8.pdf
  • Vidaurre C; Murguialday, AR; Haufe, S; Gómez, M; Müller, KR; Nikulin, VV:
    Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation
    NeuroImage, 199, pp. 375-386, 2019
    https://doi.org/10.1016/j.neuroimage.2019.05.074
  • Sauceda, HE; Chmiela, S; Poltavsky, I; Müller, KR; Tkatchenko, A:
    Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights
    2019
    arXiv:1909.08565
  • Schütt, KT; Gastegger, M; Tkatchenko, A; Müller, KR:
    Quantum-Chemical Insights from Interpretable Atomistic Neural Networks
    Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, p. 311-330, Springer International Publishing, 2019,
    https://doi.org/10.1007/978-3-030-28954-6_17
  • Samek, W; Müller, KR:
    Towards Explainable Artificial Intelligence
    Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, pp. 5-22, Springer International Publishing, 2019
    https://doi.org/10.1007/978-3-030-28954-6_1
  • Iravani, S; Conrad, TOF:
    Deep Learning for Proteomics Data for Feature Selection and Classification
    Machine Learning and Knowledge Extraction, pp. 301-316, Springer International Publishing, 2019
    https://doi.org/10.1007/978-3-030-29726-8_19
  • Hägele, M; Seegerer, P; Lapuschkin, S; Bockmayr, M; Samek, W; Klauschen, F; Müller, KR; Binder, A:
    Resolving challenges in deep learning-basedanalyses of histopathological images usingexplanation methods
    2019
    https://arxiv.org/abs/1908.06943
  • Alber, M; Lapuschkin, S; Seegerer, P; Hägele, M; Schütt, KT; Montavon, G; Samek, W; Müller, KR; Dähne, S; Kindermans, PJ:
    iNNvestigate neural networks!
    2019
    https://arxiv.org/abs/1808.04260
  • Esmailoghli, Mahdi; Redyuk, Sergey; Martinez, Ricardo; Abedjan, Ziawasch; Ziehn, Ariane; Rabl, Tilmann; Markl, Volker:
    Particulate Matter Matters—The Data Science Challenge @ BTW 2019
    Datenbank-Spektrum, Vol. 19, Issue 3, pp. 165-182, 2019 https://doi.org/10.1007/s13222-019-00322-x
  • Kunft, Andreas; Katsifodimos, Asterios; Schelter, Sebastian; Breß, Sebastian; Rabl, Tilmann; Markl, Volker:
    An Intermediate Representation for Optimizing Machine Learning Pipelines
    Proceedings of the VLDB Endowment, Vol. 12, No. 11, 2019
    http://www.vldb.org/pvldb/vol12/p1553-kunft.pdf
  • Bosse, S; Becker, S; Müller, KR; Samek, W; Wiegand, T:
    Estimation of distortion sensitivity for visual quality prediction using a convolutional neural network
    Digital Signal Processing, 91, pp. 54-65, 2019.
    https://doi.org/10.1016/j.dsp.2018.12.005
  • Karimov, Jeyhun; Rabl, Tilmann; Markl, Volker:
    AStream: Ad-hoc Shared Stream Processing
    SIGMOD ’19: Proceedings of the 2019 International Conference on Management of Data, pp. 607–622, 2019
    https://doi.org/10.1145/3299869.3319884
  • Traub, Jonas; Grulich, Philipp; Cuéllar, Alejandro Rodríguez; Breß, Sebastian; Katsifodimos, Asterios; Rabl, Tilmann; Markl, Volker:
    Efficient Window Aggregation with General Stream Slicing
    22th International Conference on Extending Database Technology (EDBT), 2019
    https://dx.doi.org/10.5441/002/edbt.2019.10
  • Esmailoghli, Mahdi; Redyuk, Sergey; Martinez, Ricardo; Abedjan, Ziawasch; Rabl, Tilmann; Markl, Volker:
    Explanation of Air Pollution Using External Data Sources
    Datenbanksysteme für Business, Technologie und Web (BTW 2019), pp. 297-300, 2019
    https://dl.gi.de/handle/20.500.12116/21820
  • Giouroukis, Dimitrios; Hülsmann, Julius; Bleichert, Janis von; Geldenhuys, Morgan; Stullich, Tim; Gutierrez, Felipe Oliveira; Traub, Jonas; Beedkar, Kaustubh; Markl, Volker:
    Resense: Transparent Record and Replay of Sensor Data in the Internet of Things
    Proceedings of the 21st International Conference on Extending Database Technology (EDBT), 2019
    https://doi.org/10.5441/002/edbt.2019.63