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Latest publications

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“
    to appear in Proceedings of the VLDB Endowment, 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“
    arXiv:2003.05431, 2020
  • 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
  • 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