Aus ML2R wurde das Lamarr-Institut – alle aktuellen Publikationen finden Sie hier!
Publikationen mit Bezug zum Kompetenzzentrum Maschinelles Lernen Rhein-Ruhr (ML2R)
2022
T. H. Schulz, P. Welke, S. Wrobel: Graph Filtration Kernels. AAAI, 2022.
H. Tan, H. Kotthaus: Surrogate Model-Based Explainability Methods for Point Cloud NNs. WACV, 2022.
N. Andrienko, G. Andrienko, L. Adilova, S. Wrobel: Visual Analytics for Human-Centered Machine Learning. In: IEEE Computer Graphics and Applications 42(1), 2022, 123-133.
D. Antweiler, M. Marmening, N. Marheineke, A. Schmeißer, R. Wegener, P. Welke: Graph-Based Tensile Strength Approximation of Random Nonwoven Materials by Interpretable Regression. In: Machine Learning with Applications 8, 2022.
H.-J. Jin, T. Dong, L. Hou, J. Li, et al: How Can Cross-lingual Knowledge Contribute Better to Fine-Grained Entity Typing?. ACL, 2022.
C. Bauckhage, R. Sifa: Gradient Flows for Linear Discriminant Analysis. LION, 2022.
M. Amir, C. Bauckhage, A. Chircu, C. Czarnecki, C. Knopf, N. Piatkowski, E. Sultanow: What Can We Expect from (Quantum) Digital Twins?. Wirtschaftsinformatik, 2022.
E. Sultanoow, C. Bauckhage, C. Knopf, N. Piatkowski: Sicherheit von Quantum Machine Learning. In: Wirtschaftsinformatik & Management 14, 2022, 144-152.
C. Bauckhage, T. Gerlach, N. Piatkowski: QUBOs for Sorting Lists and Building Trees. arXiv preprint, 2022.
L. Hillebrand, T. Deusser, C. Bauckhage, R. Sifa: KPI-BERT: A Joint Named Entity Recognition and Relation Extraction for Financial Reports. ICPR, 2022.
T. H. Schulz, P. Welke, T. Horvath, S. Wrobel: A Generalized Weisfeiler-Lehman Graph Kernel. In: Machine Learning 111, 2022, 2601-2629.
D. Biesner, R. Ramamurthy, R. Stenzl, M. Luebbering, L. Hillebrand, A. Ladi, M. Pielka, R. Loitz, C. Bauckhage, R. Sifa: Anonymization of German Financial Documents Using Neural Network- Based Language Models with Contextual Word Representations. In: International Journal of Data Science and Analytics 13, 2022, 151-161.
D. Biesner, R. Sifa, C. Bauckhage, B. Kliem: Solving Subset Sum Problems using Binary Optimization with Applications in Auditing and Financial Data Analysis. In: TechRxiv preprint, 2022.
K. Cvejoski, R. Sánchez, C. Bauckhage, C. Ojeda: Dynamic Review-based Recommenders. Data Science – Analytics and Applications, 2022.
K. Beckh, S. Müller, S. Rüping: A Quantitative Human-Grounded Evaluation Process for Explainable ML. HCXAI Workshop at CHI, 2022.
A. Saadallah, M. Jakobs, K. Morik: Explainable Online Ensemble of Deep Neural Network Pruning for Time Series Forecasting. In: Machine Learning, 2022.
H. Liu, M. Brehler, M. Ravishankar, N. Vasilache, B. Vanik, S. Laurenzo: TinyIREE: An ML Execution Environment for Embedded Systems from Compilation to Deployment. In: IEEE Micro, 2022.
R. L. Wilking, M. Jakobs, K. Morik: Fooling Perturbation-Based Explainability Methods. Trustworthy Artificial Intelligence Workshop at ECML PKDD, 2022.
R. Fischer, M. Jakobs, S. Mücke, K. Morik: A Unified Framework for Assessing Energy Efficiency of Machine Learning. Data Science for Social Good Workshop at ECML PKDD, 2022.
K. Morik, H. Kotthaus, L. Heppe, D. Heinrich, R. Fischer, S. Mücke, A. Pauly, M. Jakobs, N. Piatkowski: Yes We Care! – Certification for Machine Learning Methods through the Care Label Framework. In: Frontiers in Artificial Intelligence, 2022.
M. Jakobs, H. Kotthaus, I. Röder, M. Baritz: SancScreen: Towards a real-world dataset for evaluating explainability methods. LWDA, 2022.
L. Pucknat, M. Pielka, R. Sifa: Towards Informed Pre-Training for Critical Error Detection in English-German. LWDA, 2022.
D. Biesner, K. Cvejoski, R. Sifa: Combining Variational Autoencoders and Transformer Language Models for Improved Password Generation. ARES, 2022.
C. L. Chapman, L. Hillebrand, M. R. Stenzel, T. Deusser, D. Biesner, C. Bauckhage, R. Sifa: Towards Generating Financial Reports from Tabular Data Using Transformers. CD-MAKE, 2022.
C. Bauckhage, H. Schneider, B. Wulff, R. Sifa: Gradient Flows for L2 Support Vector Machine Training. ICML, 2022.
A. Gouda, A. Ghanem, C. Reining: DoPose-6D dataset for object segmentation and 6D pose estimation. ICMLA, 2022.
J. Rutinowski, C. Pionzewski, T. Chilla, C. Reining, M. ten Hompel: Computer Vision Based Re-Identification of Wooden Euro-pallets. ICMLA, 2022.
E. Brito, V. Gupta, E. Hahn, S. Giesselbach: Assessing the Performance Gain on Retail Article Categorization at the Expense of Explainability and Resource Efficiency. KI, 2022.
T. Deußer, S. M. Ali, L. Hillebrand, D. Nurchalifah, B. Jacob, C. Bauckhage, R. Sifa: KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents. ICMLA, 2022.
L. Hillebrand, T. Deußer, T. Dilmaghani, B. Kliem, R. Loitz, C. Bauckhage, R. Sifa: Towards automating Numerical Consistency Checks in Financial Reports. BigData, 2022.
D. Boiar, N. Killich, L. Schulte, V. H. Moreno, J. Deuse, T. Liebig: Forecasting Algae Growth in Photo-Bioreactors using Attention LSTMs. AI4EA Workshop at SEFM, 2022.
2021
L. von Rueden, T. Wirtz, F. Hueger, J. D. Schneider, N. Piatkowski, C. Bauckhage: Street-Map Based Validation of Semantic Segmentation in Autonomous Driving. ICPR, 2021.
P. Welke, F. Alkhoury, C. Bauckhage, S. Wrobel: Decision Snippet Features. ICPR, 2021.
C. Ojeda, R. Sanchez, K. Cvejoski, J. Schuecker, D. Biesner, C. Bauckhage, B. Georgiev: Auto Encoding Explanatory Examples with Stochastic Paths. ICPR, 2021.
C. Ojeda, B. Georgiev, K. Cvejoski, J. Schuecker, C. Bauckhage, R. Sanchez: Switching Dynamical Systems with Deep Neural Networks. ICPR, 2021.
C. Ojeda, K. Cvejoski, J. Schuecker, B. Georgiev, C. Bauckhage, R. Sanchez: An Adversarial Approach towards Queuing Systems Modeling. AAAI, 2021.
V. Olari, K. Cvejoski, Ø. Eide: Introduction to Machine Learning with Robots and Playful Learning. AAAI, 2021.
J. Kalofolias, P. Welke, J. Vreeken: SUSAN: The Structural Similarity Random Walk Kernel. SIAM Data Mining, 2021.
M. Pielka, R. Sifa, L. P. Hillebrand, D. Biesner, R. Ramamurthy, A.Ladi, C. Bauckhage: Tackling Contradiction Detection in German Using Machine Translation and End-to-End Recurrent Neural Networks. ICPR, 2021.
S. Hänold, N. Schlee, D. Antweiler, K. Beckh: Die Nachvollziehbarkeit von KI-Anwendungen in der Medizin – eine Betrachtung aus juristischer Perspektive mit Beispielszenarien. In: Medizinrecht 39, 2021, 516-523.
B. Georgiev, L. Franken, M. Mukherjee: Heating up Decision Boundaries: Isocapacitory Saturation, Adversarial Scenarios and Generalization Bounds. ICLR, 2021.
V. Gupta, K. Beckh, S. Giesselbach, D. Wegener, T. Wirtz: Supporting Verification of News Articles with Automated Search for Semantically Similar Articles. ROMCIR Workshop at ECIR, 2021.
L. Hillebrand, D. Biesner, C. Bauckhage, R. Sifa: Interpretable Topic Extraction and Word Embedding Learning Using Non-Negative Tensor DEDICOM. In: Machine Learning and Knowledge Extraction 3(19), 2021, 123-167.
A. Saadallah, M. Tavakol, K. Morik: An Actor-Critic Ensemble Aggregation Model for Time-Series Forecasting. ICDE, 2021.
A. Saadallah, K. Morik: Meta-Adversarial Training of Neural Networks for Binary Classification. IJCNN, 2021.
Z. Yao, C. Li, T. Dong , X. Lv, J. Yu, L. Hou, J. Li, Y. Zhang, Z. Dai: Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making. ACL IJCNLP, 2021.
J. Rosenzweig, J. Sicking, S. Houben, M. Mock, M. Akila: Patch Shortcuts: Interpretable Proxy Models Efficiently FindBlack-Box Vulnerabilities. SAIAD Workshop at CVPR, 2021.
L. Adilova, E. Schulz, M. Akila, S. Houben, J. D. Schneider, F. Hüger, T. Wirtz: Plants Don’t Walk on the Street: Common-Sense Reasoning for Reliable Semantic Segmentation. SAIAD Workshop at CVPR, 2021.
C. Ojeda, K. Cvejoski, B. Georgiev, C. Bauckhage, J. Schuecker, R. J. Sanchez: Learning Deep Generative Models for Queueing Systems. AAAI, 2021.
D. Biesner, K. Cvejoski, B. Georgiev, R. Sifa, E. Krupicka: Advances in Password Recovery using Generative Deep Learning Techniques. ICANN, 2021.
A. Saadallah, M. Jakobs, K. Morik: Explainable Online Deep Neural Network Selection using Adaptive Saliency Maps for Time Series Forecasting. ECML PKDD, 2021.
S. Houben, S. Abrecht, M. Akila, A. Bär, F. Brockherde, P. Feifel, T. Fingscheidt, S. S. Gannamanen, S. E. Ghobadi, A. Hammam, A. Haselhoff, F. Hauser, C. Heinzemann, M. Hoffmann, N. Kapoor, F. Kappel, M. Klingner, J. Kronenberger, F. Küppers, J. Löhdefink, M. Mlynarski, M. Mock, F. Mualla, S. Pavlitskaya, M. Poretschkin, A. Pohl, V. Ravi-Kumar, J. Rosenzweig, M. Rottmann, S. Rüping, T. Sämann, J. D. Schneider, E. Schulz, G. Schwalbe, J. Sicking, T. Srivastava, S. Varghese, M. Weber, S. Wirkert, T. Wirtz, M. Woehrle: Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety. In: Deep Neural Networks and Data for Automated Driving, 2021, 3-78.
K. Morik, S. Buschjäger, P. Honysz, L. Pfahler: Very Fast Streaming Submodular Function Maximization. ECML PKDD, 2021.
M. Bunse, K. Morik: Certification of Model Robustness in Active Class Selection. ECML PKDD, 2021.
D. Weichert, F. Horchler, A. Kister, M. Trost, J. Hartung, S. Risse: Monte Carlo Tree Search for High Precision Manufacturing. RL4RL Workshop at ICML, 2021.
M. Bunse, K. Morik: Active Class Selection with Uncertain Deployment Class Proportions. IAL Workshop at ECML PKDD, 2021.
S. Chen, N. V. Andrienko, G. L. Andrienko, J. Lie, X. Yuan: Co-Bridges: Pair-wise Visual Connection and Comparison for Multi-item Data Streams. In: IEEE Trans. Vis. Comput. Graph 27(2), 2021, 1612-1622.
M. Langer, D. Oster, T. Speith, H. Hermanns, L. Kästner, E. Schmidt, A. Sesing, K. Baum: What do we want from Explainable Artificial Intelligence (XAI)? – A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research. In: Artif. Intell. 296, 2021, 103473.
A. Amini, A. S. Periyasamy, S. Behnke: T6D-Direct: Transformers for Multi-Object 6D Pose Direct Regression. DAGM GCPR, 2021.
A. S. Periyasamy, M. Schwarz, S. Behnke: A Dataset for Dynamic Bin Picking Scene Understanding. DAGM GCPR, 2021.
K. Cvejoski, R. Sanchez, C. Bauckhage, C. Ojeda: Dynamic Review based Recommenders. iDSC, 2021.
F. Gonsior, S. Mücke, K. Morik: Conditional Structure Search for Normalizing Flows. FGKDML Workshop at LWDA, 2021.
N. Piatkowski, J. S. Mueller-Roemer, P. Hasse, A. Bachorek, T. Werner, P. Birnstill, A. Morgenstern, L. Stobbe: Generative Machine Learning for Resource-Aware 5G and IoT Systems. Workshop at ICC, 2021.
N. Piatkowski, P. N. Posch, M. Krause: How to Trust Generative Probabilistic Models for Time-Series Data?. LION, 2021.
R. Ramamurthy, M. Lübbering, T. Bell, M. Gebauer, B. Ulusay, D. Uedelhoven, T. D. Khameneh, R. Loitz, M. Pielka, C. Bauckhage, R. Sifa: Automatic Indexing of Financial Documents via Information Extraction. SSCI, 2021.
M. Lübbering, M. Gebauer, R. Ramamurthy, C. Bauckhage, R. Sifa: Decoupling Autoencoders for Robust One-vs-Rest Classification. DSAA, 2021.
R. Agombar, C. Bauckhage, M. Lübbering, R. Sifa: An Optimization for Convolutional Network Layers Using the Viola-Jones Framework and Ternary Weight Networks. In: International Conference on Learning and Intelligent Optimization, 2021, 1-6.
M. Lübbering, M. Gebauer, R. Ramamurthy, R. Sifa, C. Bauckhage: Supervised autoencoder variants for end to end anomaly detection. In: International Conference on Pattern Recognition, 2021, 566-581.
M. Lübbering, M. Pielka, K. Das, M. Gebauer, R. Ramamurthy, C. Bauckhage, R. Sifa: Toxicity Detection in Online Comments with Limited Data: A Comparative Analysis. ESANN, 2021.
M. Lübbering, M. Gebauer, R. Ramamurthy, M. Pielka, C. Bauckhage, R. Sifa: Utilizing representation learning for robust text classification under datasetshift. CEUR Workshop at LWDA, 2021.
D. Biesner, R. Ramamurthy, R. Stenzel, M. Lübbering, L. Hillebrand, A. Ladi, M. Pielka, R. Loitz, C. Bauckhage, R. Sifa: Anonymization of German Financial Documents Using Neural Network- based Language Models with Contextual Word Representations. In: Int J Data Sci Anal, 2021, 1-11.
M. Günder, N. Piatkowski, L. von Rüden, R. Sifa, C. Bauckhage: Towards Intelligent Food Waste Prevention: An Approach Using Scalable and Flexible Harvest Schedule Optimization With Evolutionary Algorithms. In: IEEE Access 9, 2021, 169044-169055.
M. Bulla, L. Hillebrand, M. Lübbering, R. Sifa: Knowledge Graph Based Question Answering System for Financial Securities. In: KI 2021: Advances in Artificial Intelligence, 2021, 44-50.
H. Kondratiuk, R. Sifa: Swords, Data and Balls: Extracting Extreme Behavioural Prototypes with Kernel Minimum Enclosing Balls. CoG, 2021.
L. Pucknat, M. Pielka, R. Sifa: Detecting Contradictions in German Text: A Comparative Study. SSCI, 2021.
R. Ramamurthy, M. Pielka, R. Stenzel, C. Bauckhage, R. Sifa, T. D. Khameneh, U. Warning, B. Kliem, R. Loitz: ALiBERT: Improved Automated List Inspection (ALI) with BERT. DocEng, 2021.
R. Sifa: Predicting Player Churn with Echo State Networks. CoG, 2021.
R. Sifa, A. Drachen, F. Block, S. Moon, A. Dubhashi, H. Xiao, Z. Li, D. Klabjan, S. Demediuk: Archetypal Analysis Based Anomaly Detection for Improved Storytelling in Multiplayer Online Battle Arena Games. ACSW, 2021.
A. Wahab, R. Sifa: DIBERT: Dependency Injected Bidirectional Encoder Representations from Transformers. SSCI, 2021.
C. Bauckhage, J. Gall, A. Schwing: Pattern Recognition. Springer, 2021.
T. Dong, A. Rettinger, J. Tang, B. Tversky, F. van Harmelen: Structure and Learning. In: Dagstuhl Reports 11(8), 2021, 11-34.
P. Honysz, S. Buschjäger, K. Morik: GPU-Accelerated Optimizer-Aware Evaluation of Submodular Exemplar Clustering. arxiv preprint, 2021.
P. Honysz, A. Schulze-Struchtrup, S. Buschjäger, K. Morik: Providing Meaningful Data Summarizations Using Examplar-based Clustering in Industry 4.0. arxiv preprint, 2021.
A. Gouda, A. Ghanem, P. Kaiser, M. ten Hompel: Object class-agnostic segmentation for practical CNN utilization in industry. ICMERR, 2021.
2020
K. Cvejoski, R. J. Sanchez, B. Georgiev, J.Schuecker, C. Bauckhage, C. Ojeda: Recurrent Point Processes for Dynamic Review Models. WICRS Workshop at AAAI, 2020.
D. Biesner, R. Ramamurthy, M. Lübbering, B. Fürst, H. Ismail, L. Hillebrand, A. Ladi, M. Pielka, R. Stenzel, T. Khameneh, V. Krapp, I. Huseynov, J. Schlums, U. Stoll, U. Warning, B. Kliem, C. Bauckhage, R. Sifa: Leveraging Contextual Text Representations for Anonymizing German Financial Documents. AAAI Workshop on Knowledge Discovery from Unstructured Data in Financial Services at KDF, 2020.
O. Urbann, S. Camphausen, A. Moos, I. Schwarz, S. Kerner, M. Otten: A C Code Generator for Fast Inference and Simple Deployment of Convolutional Neural Networks on Resource Constrained Systems. C4ML Workshop at CGO, 2020.
M. Nanni, A. Gennady, A.-L. Barabasi, C. A. Boldrini, F. Bonchi, C. Cattuto, F. Chiaromonte, G. Commande, M. Conti, M. Cote, F. Dignum, V. Dignum, J. Domingo-Ferrer, P. Ferragina, F. Giannotti, R. Guidotti, D. Helbing, K. Kaski, J. Kertesz, S. Lehmann, B. Lepri, P. Lukowicz, S. Matwin, J. Megias, D. Megias, A. Monreale, K. Morik, N. Oliver, A. Passarella, A. Passerini, D. Pedreschi, A. Pentland, F. Pianesi, F. Pratesi, S. Rinzivillo, S. Ruggieri, A. Siebes, V. Torra, R. Trasarti, J. van der Hoven, A. Vespignani: Give More Data, Awareness and Control to Individual Citizens, and They Will Help COVID-19 Containment. In: Trans. Data Priv. 13, 2020, 61-66.
L. von Rueden, S. Mayer, R. Sifa, C. Bauckhage, J. Garcke: Combining Machine Learning and Simulation to a Hybrid Modelling Approach: Current and Future Directions. IDA, 2020.
C. Bauckhage, R. Sifa, S. Wrobel: Adiabatic Quantum Computing for Max-Sum Diversification. SDM, 2020.
R. Sifa: DESICOM as Metaheuristic Search. LION, 2020.
R. Sifa, C. Bauckhage: Novelty Discovery with Kernel Minimum Enclosing Balls. LION, 2020.
M. Cekic, B. Georgiev, M. Mukherjee: Polyhedral Billiards, Eigenfunction Concentration and Almost Periodic Control. In: Commun. in Math. Phys. 377, 2020, 2451-2487.
C. Bauckhage, R. Sanchez, R. Sifa: Problem Solving with Hopfield Networks and Adiabatic Quantum Computing. IJCNN, 2020.
K. Cvejoski, C. Ojeda, B. Georgiev, C. Bauckhage, R. J. Sanchez: Recurrent Point Review Models. IJCNN, 2020.
A. Kiwan, S. Giesselbach, S. Rüping: Incorporating Knowledge Bases into SciBERT and BioBERT Pre-Trained Language Models. SciNLP Workshop at AKBC, 2020.
B. Sliwa, N. Piatkowski, C. Wietfeld: LIMITS: Lightweight Machine Learning for IoT Systems with Resource Limitations. ICC, 2020.
B. Sliwa, N. Piatkowski, C. Wietfeld: The Channel as a Traffic Sensor: Vehicle Detection and Classification Based on Radio Fingerprinting. In: IEEE Internet of Things Journal 7, 2020, 7392-7406.
F. Finkeldey, A. Saadallah, P. Widerkehr, K. Morik: Real-Time Prediction of Process Forces in Milling Operations Using Synchronized Data Fusion of Simulation and Sensor Data. In: Eng. Appl. Artif. Intell. 94, 2020.
L. von Rueden, T. Wirtz, F. Hueger, J. D. Schneider, C. Bauckhage: Towards Map-Based Validation of Semantic Segmentation Masks. AIAD Workshop at ICML, 2020.
K. Y. Chai, J. Stenzel, J. Jost: Generation, Classification and Segmentation of Point Clouds in Logistic Context with PointNet++ and DGCNN. IRCE, 2020.
L. Hillebrand, D. Biesner, C. Bauckhage, R. Sifa: Interpretable Topic Extraction and Word Embedding Learning Using Row-Stochastic DEDICOM. CD-MAKE, 2020.
L. Pfahler, K. Morik: Semantic Search in Millions of Equations. KDD, 2020.
P. Welke, F. Seiffarth, M. Kamp, S. Wrobel: HOPS: Probabilistic Subtree Mining for Small and Large Graphs. KDD, 2020.
B. Kirsch, S. Giesselbach, T. Schmude, M. Völkening, F. Rostalsko, S.Rüping: Probabilistic Soft Logic to Improve Information Extraction in the Legal Domain. LWDA, 2020.
R. Fischer, M. Jakobs, S. Mücke, K. Morik: Solving Abstract Reasoning Tasks with Grammatical Evolution. LWDA, 2020.
J. Kindermann, K. Beckh: Fusing Multi-label Classification and Semantic Tagging. KDML Workshop at LWDA, 2020.
S. Buschjäger, L. Pfahler, J. Buss, K. Morik, W. Rhode: On-Site Gamma-Hadron Separation with Deep Learning on FPGAs. ECML PKDD, 2020.
L. Heppe, M. Kamp, L. Adilova, N. Piatkowski, D. Heinrich, K. Morik: Resource-Constrained On-Device Learning by Dynamic Averaging. PDFL Workshop at ECML PKDD, 2020.
F. Seiffarth, T. Horvath, S. Wrobel: Maximum Margin Separations in Finite Closure Systems. ECML PKDD, 2020.
B. Georgiev, G. Angelov: On Learning a Control System without Continuous Feedback. ESANN, 2020.
B. Georgiev, L. Franken: Explorations in Quantum Neural Networks with Intermediate Measurements. ESANN, 2020.
R. Fischer, N. Piatkowski, C. Pelletier, G. Webb, F. Petitjean, K. Morik: No Cloud on the Horizon: Probabilistic Gap Filling in Satellite Image Series. DSAA, 2020.
C. Bauckhage, M. Bortz, R. Sifa: Shells within Minimum Enclosing Balls. DSAA, 2020.
P. Welke: Efficient Frequent Subgraph Mining in Transactional Databases. DSAA, 2020.
A. Saadallah, K. Morik: Active Sampling for Learning Interpretable Surrogate Machine Learning Models. DSAA, 2020.
A. Mehler, W. Hemati, P. Welke, M. Konca, T. Uslu: Multiple Texts as a Limiting Factor in Online Learning: Quantifying (Dis-)similarities of Knowledge Networks across Languages. In: Frontiers in Education, 2020.
M. Bunse, D. Weichert, A. Kister, K. Morik: Optimal Probabilistic Classification in Active Class Selection. ICDM, 2020.
D. Antweiler, P. Welke: Temporal Graph Analysis for Outbreak Pattern Detection in COVID-19 Contact Tracing Networks. MLPH Workshop at NeurIPS, 2020.
T. Dong: A Geometric Approach to The Unification of Symbolic Structures and Neural Networks. Springer, 2020.
C. Bauckhage, R. Ramamurthy, R. Sifa: Hopfield Networks for Vector Quantization. ICANN, 2020.
R. Ramamurthy, R. Sifa, M. Lübbering, C. Bauckhage: Guided Reinforcement Learning via Sequence Learning. ICANN, 2020.
M. Lübbering, R. Ramamurthy, M. Gebauer, T. Bell, R. Sifa, C. Bauckhage: From Imbalanced Classification to Supervised Outlier Detection Problems: Adversarially Trained Auto Encoders. ICANN, 2020.
M. Masoudinejad: Open-Loop Dynamic Modeling of Low-Budget Batteries with Low-Power Loads. In: MDPI Batteries 6(4), 2020, 50.
S. Buschjäger, P. J. Honysz, K. Morik: Randomized Outlier Detection with Trees. In: Int. J. Data Sci. Anal. 13, 2020, 91-204.
L. Franken, B. Georgiev, S. Muecke, M. Wolter, N. Piatkowski, C. Bauckhage: Gradient-Free Quantum Optimization on NISQ Devices. arxiv preprint, 2020.
M. Elahi, R. Hosseini, M. H. Rimaz, F. B. Moghaddam, C. Trattner: Visually-Aware Video Recommendation in the Cold Start. HT, 2020.
X. Han, T. Grubenmann, R. Cheng, S. C. Wong, X. Li, W. Sun: Traffic Incident Detection: A Trajectory-Based Approach. ICDE, 2020.
L. Reimann, G. Kniesel-Wünsche: Achieving Guidance in Applied Machine Learning through Software Engineering Techniques. Programming, 2020.
A. Sadeghi, D. Graux, H. S. Yazdi, J. Lehmann: MDE: Multiple Distance Embeddings for Link Prediction in Knowledge Graphs. ECAI, 2020.
H. Zafar, M. Tavakol, J. Lehmann: Distantly Supervised Question Parsing. ECAI, 2020.
F. A. Musyaffa, M. Vidal, F. Orlandi, J. Lehmann, H. Jabeen: IOTA: Interlinking of Heterogeneous Multilingual Open Fiscal DaTA. In: Expert Syst. Appl. 147(4), 2020.
M. Cremaschi, F. De Paoli, A. Rula, B. Spahiu: A Fully Automated Approach to a Complete Semantic Table Interpretation. In: Future Gener. Comput. Syst. 112, 2020, 478 – 500.
D. Tomaszuk, R. Angles, H. Thakkar: PGO: Describing Property Graphs in RDF. In: IEEE Access 8, 2020, 118355 – 118369.
S. Payrosangari, A. Sadeghi, D. Graux, J. Lehmann: Meta-hyperband: Hyperparameter Optimization with Meta-Learning and Coarse-to-Fine. IDEAL, 2020.
M. Nayyeri, X. Zhou, S. Vahdati, R. Izanloo, H. S. Yazdi, J. Lehmann: Let the Margin SlidE for Knowledge Graph Embeddings via a Correntropy Objective Function. IJCNN, 2020.
H. Jabeen, D. Graux, G. Sejdiu: Scalable Knowledge Graph Processing Using SANSA. In: Knowledge Graphs and Big Data Processing 12072, 2020, 105 – 121.
R. Nedelchev, R. Usbeck, J. Lehmann: Treating Dialogue Quality Evaluation as an Anomaly Detection Problem. LREC, 2020.
M. T. Chau, D. Esteves, J. Lehmann: A Neural-Based Model to Predict the Future Natural Gas Market Price through Open-domain Event Extraction. ESWC, 2020.
S. R. Bader, I. Grangel-González, P. Nanjappa, M. Vidal, M. Maleshkova: A Knowledge Graph for Industry 4.0. ESWC, 2020.
E. Kacupaj, H. Zafar, J. Lehmann, M. Maleshkova: VQuAnDa: Verbalization QUestion ANswering DAtaset. ESWC, 2020.
M. Nayyeri, S. Vahdati, X. Zhou, H. S. Yazdi, J. Lehmann: Embedding-Based Recommendations on Scholarly Knowledge Graphs. ESWC, 2020.
M. Nayyeri, C. Xu, S. Vahdati, N. Vassilyeva, E. Sallinger, H. S. Yazdi, J. Lehmann: Fantastic Knowledge Graph Embeddings and How to Find the Right Space for Them. ISWC, 2020.
C. Xu, M. Nayyeri, F. Alkhoury, H. S. Yazdi, J. Lehmann: Temporal Knowledge Graph Completion Based on Time Series Gaussian Embedding. ISWC, 2020.
T. Grubenmann, R. C. K. Cheng, L. V. S. Lakshmanan: TSA: A Truthful Mechanism for Social Advertising. WSDM, 2020.
I. O. Mulang, K. Singh, A. Vyas, S. Shekarpour, M. Vidal, S. Auer, J. Lehmann: Encoding Knowledge Graph Entity Aliases in Attentive Neural Network for Wikidata Entity Linking. WISE, 2020.
Z. Say, S. Fathalla, S. Vahdati, J. Lehmann, S. Auer: Ontology Design for Pharmaceutical Research Outcomes. TPDL, 2020.
M. Elias, M. R. Tavakoli, S. Lohmann, G. Kismihók, S. Auer: An OER Recommender System Supporting Accessibility Requirements. ASSETS, 2020.
H. Jabeen, E. Haziiev, G. Sejdiu, J. Lehmann: DISE: A Distributed in-Memory SPARQL Processing Engine over Tensor Data. ICSC, 2020.
H. Thakkar, R. Angles, M. Rodriguez, S. Mallette, J. Lehmann: Let’s Build Bridges, Not Walls: SPARQL Querying of TinkerPop Graph Databases with Sparql-Gremlin. ICSC, 2020.
R. Angles, H. Thakkar, D. Tomaszuk: Mapping RDF Databases to Property Graph Databases. In: IEEE Access 8, 2020, 86091 – 86110.
H. Jabeen, J. Weinz, J. Lehmann: AutoChef: Automated Generation of Cooking Recipes. CEC, 2020.
H. Jabeen: Big Data Outlook, Tools, and Architectures. In: Knowledge Graphs and Big Data Processing, 2020, 35 – 55.
M. Tasnim, D. Collarana, D. Graux, M. Vidal: Context-Based Entity Matching for Big Data. In: Knowledge Graphs and Big Data Processing, 2020, 122 – 146.
J. Rose, J. Lehmann: CASQAD – A New Dataset for Context-Aware Spatial Question Answering. ISWC, 2020.
I. O. Mulang‘, K. Singh, C. Prabhu, A. Nadgeri, J. Hoffart, J. Lehmann: Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models. CIKM, 2020.
J. Armitage, E. Kacupaj, G. Tahmasebzadeh, Swati, M. Maleshkova, R. Ewerth, J. Lehmann: MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities. CIKM, 2020.
M. Nayyeri, M. M. Alam, J. Lehmann, S. Vahdati: 3D Learning and Reasoning in Link Prediction Over Knowledge Graphs. In: IEEE Access 8, 2020.
A. Say, S. Fathalla, S. Vahdati, J. Lehmann S. Auer: Semantic Representation of Physics Research Data. IC3K, 2020.
J. Armitage, S. Thakur, R. Tripathi, J. Lehmann, M. Maleshkova: Training Multimodal Systems for Classification with Multiple Objectives. CLEOPATRA Workshop at ESWC, 2020.
M. Galkin, P. Trivedi, G. Maheshwari, R. Usbeck, J. Lehmann: Message Passing for Hyper-Relational Knowledge Graphs. EMNLP, 2020.
C. Xu, M. Nayyeri, Y. Chen, J. Lehmann: Knowledge Graph Embeddings in Geometric Algebras. COLING, 2020.
C. Xu, M. Nayyeri, F. Alkhoury, H. S. Yazdi, J. Lehmann: TeRo: A Time-Aware Knowledge Graph Embedding via Temporal Rotation. COLING, 2020.
R. Nedelchev, J. Lehmann, R. Usbeck: Language Model Transformers as Evaluators for Open-Domain Dialogues. COLING, 2020.
E. Iglesias, S. Jozashoori, D. Chaves-Fraga, D. Collarana, M. Vidal: SDM-RDFizer: An RML Interpreter for the Efficient Creation of RDF Knowledge Graphs. CIKM, 2020.
A. Rivas, I. Grangel-González, D. Collarana, J. Lehmann, M. Vidal: Unveiling Relations in the Industry 4.0 Standards Landscape Based on Knowledge Graph Embeddings. DEXA, 2020.
D. Banerjee, D. Chaudhuri, M. Dubey, J. Lehmann: PNEL: Pointer Network Based End-To-End Entity Linking over Knowledge Graphs. ISWC, 2020.
C. Ojeda, R. Sanchez, K. Cvejoski, J. Schuecker, D. Biesner, C. Bauckhage, B. Georgiev: Auto Encoding Explanatory Examples with Stochastic Paths. ICPR, 2020.
C. Ojeda, B. Georgiev, K. Cvejoski, J. Schuecker, C. Bauckhage, R. Sanchez: Switching Dynamical Systems with Deep Neural Networks. ICPR, 2020.
L. v. Rüden, S. Mayer, K. Beckh, B. Georgiev, S. Giesselbach, R. Heese, B. Kirsch, J. Pfrommer, A. Pick, R. Ramamurthy, M. Walczak, J. Garcke, C. Bauckhage, J. Schuecker: Informed Machine Learning – A Taxonomy and Survey of Integrating Knowledge into Learning Systems. In: IEEE Trans. Knowl. Data Eng., 2020.
P. H. Nguyen, R. Henkin, S. Chen, N. V. Andrienko, G. L. Andrienko, O. Thonnard, C. Turkay: VASABI: Hierarchical User Profiles for Interactive Visual User Behaviour Analytics. In: IEEE Trans. Vis. Comput. Graph 26(1), 2020, 77 – 86.
S. Chen, N. V. Andrienko, G. L. Andrienko, L. Adilova, J. Barlet, J. Kindermann, P. H: Nguyen, O. Thonnard, C. Turkay: LDA Ensembles for Interactive Exploration and Categorization of Behaviors. In: IEEE Trans. Vis. Comput. Graph 26(9), 2020, 2775 – 2792.
F. Seiffarth, T. Horvath, S. Wrobel: Maximal Closed Set and Half-Space Separations in Finite Closure Systems. ECML PKDD, 2020.
2019
T. Dong, Z. Wang, J. Li, C. Bauckhage, and A. B. Cremers: Triple Classification Using Regions and Fine-Grained Entity Typing. AAAI, 2019.
S. Hess, W. Duivesteijn, K.Morik, P.-J. Honysz: The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering. AAAI, 2019.
S. Buschjäger, T. Liebig, K. Morik: Gaussian Model Trees for Traffic Imputation. ICPRAM, 2019.
A. Saadallah, N. Piatkowski, F. Finkeldey, P. Wiederkehr, K. Morik: Learning Ensembles in the Presence of Imbalanced Classes. ICPRAM, 2019.
G. Meschke, B.-T. Cao, A. Egorov, A. Saadallah, S. Freitag, K. Morik: Big Data and Simulation – A New Approach for Real-Time TBM Steering. In: Tunnels and Underground Cities: Engineering and Innovation meet Archaeology, Architecture and Art, 2019, 2681-2690.
T. Dong, C. Bauckhage, H. Jin, J. Li, O. Cremers, D. Speicher, A.B. Cremers, J. Zimmermann: Imposing Category Trees onto Word-Embeddings Using a Geometric Construction. ICLR, 2019.
V. Gupta, S. Giesselbach, S. Rüping, C. Bauckhage: Improving Word Embeddings Using Kernel PCA. RepL4NLP Workshop at ACL, 2019.
A. Saadallah, A. Egorov, B.-T. Cao, S. Freitag, K. Morik, G. Meschke: Active Learning for Accurate Settlement Prediction Using Numerical Simulations in Mechanized Tunneling. CIRP, 2019.
P. Welke, T. Horvath, S. Wrobel: Probabilistic and Exact Frequent Subtree Mining in Graphs Beyond Forests. In: Machine Learning 108, 2019, 1137-1164.
D. Trabold, T. Horvath, S. Wrobel: Effective Approximation of Parametrized Closure Systems over Transactional Data Streams. In: Machine Learning 109, 2019, 1147-1177.
N. Piatkowski: Distributed Generative Modelling with Sub-Linear Communication Overhead. DMLE Workshop at ECML PKDD, 2019.
S. Mücke, N. Piatkowski, K. Morik: Hardware Accelerated Learning at the Edge. DMLE Workshop at ECML PKDD, 2019.
B. Kirsch, Z. Niyazova, S. Rüping, M. Mock: Noise Reduction in Distant Supervision for Relation Extraction Using Probabilistic Soft Logic. DINA Workshop at ECML PKDD, 2019.
N. Piatkowski: Hyper-Parameter-Free Generative Modelling with Deep Boltzmann Trees. ECML PKDD, 2019.
M. Tavakol, S. Mair, K. Morik: HyperUCB: Hyperparameter Optimization Using Contextual Bandits. International Workshop at ECML PKDD, 2019.
L. Pfahler, J. Schill, K. Morik: The Search for Equations – Learning to Identify Similarities Between Mathematical Expressions. ECML PKDD, 2019.
A. Saadallah, F. Priebe, K. Morik: A Drift-based Dynamic Ensemble Members Selection Using Clustering for Time Series Forecasting. ECML PKDD, 2019.
P. Welke, T. Schulz: On the Necessity of Graph Kernel Baselines. Graph Embedding and Mining Workhop at ECML PKDD, 2019.
F. Seiffarth, T. Horváth, S. Wrobel: Maximal Closed Set and Half-Space Separations in Finite Closure Systems. ECML PKDD, 2019.
C. Bauckhage, R. Sifa, T. Dong: Prototypes within Minimum Enclosing Balls. Workshop and Special Session at ICANN, 2019.
R. Ramamurthy, C. Bauckhage, R. Sifa, J. Schücker, S. Wrobel: Leveraging Domain Knowledge for Reinforcement Learning Using MMC Architectures. ICANN, 2019.
C. Bauckhage, N. Piatkowski, R. Sifa, D. Hecker, S. Wrobel: A QUBO Formulation of the k-Medoids Problem. LWDA, 2019.
R. Fischer, N. Piatkowski, K. Morik: Parameter Sharing for Spatio-Temporal Process Models. LWDA, 2019.
S. Mücke, N. Piatkowski, K. Morik: Learning Bit by Bit: Extracting the Essence of Machine Learning. LWDA, 2019.
J. HaiLong, H. Lei, L. Juanzi, T. Dong: Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks. EMNLP/IJCNLP, 2019.
R. Sifa, R. Yawar, R. Ramarmurthy, C. Bauckhage, K. Kersting: Matrix- and Tensor Factorization for Game Content Recommendation. In: KI – Künstliche Intelligenz 34, 2019, 57-67.
M. Masoudinejad: Data-Sets for Indoor Photovoltaic Behavior in Low Lighting Conditions. In: MPDI Data 5(2), 2019, 32.
S. Kerner, J. Leveling , M. Otten, O. Urbann, M. Vogel, L. Weickhmann: Anwendungsfelder von künstlicher Intelligenz in Industrie 4.0 Systemen. In: Handbuch Industrie 4.0, 2019, 1-24.
P. Tözün, H. Kotthaus: Scheduling Data-Intensive Tasks on Heterogeneous Many Cores. In: IEEE Data Eng. Bull., 2019.
H. Kotthaus, L. Schönberger, A. Lang, J. Chen, P. Marwedel: Can Flexible Multi-Core Scheduling Help to Execute Machine Learning Algorithms Resource-Efficiently? SCOPES, 2019.
S. Mücke, N. Piatkowski, K. Morik: Hardware Acceleration of Machine Learning Beyond Linear Algebra. International Workshop at ECML PKDD, 2019.
2018
R. Schiffers, K. Morik, A. Schulze Struchtrup, P.-J. Honysz, J. Wortberg: Anomaly Detection in Injection Molding Process Data Based on Unsupervised Learning. In: Journal of Plastics Technology, 2018, 301 – 347.
S. Hess, N. Piatkowski, K. Morik: The Trustworthy Pal: Controlling the False Discovery Rate in Boolean Matrix Factorization. SIAM SDM, 2018.
K. Morik, W. Kraemer (Hg.): Daten – wem gehören sie, wer speichert sie, wer darf auf sie zugreifen? 2018.
K. Morik: Daten – wem gehören sie, wer speichert sie, wer darf auf sie zugreifen? In: Daten – wem gehören sie, wer speichert sie, wer darf auf sie zugreifen? 2018, 15 – 47.
L. Adilova, S. Giesselbach, S. Rüping: Making Efficient Use of a Domain Expert’s Time in Relation Extraction. DMNLP Workshop at ECML PKDD 2017. arXiv:1807.04687 [cs.LG], 2018.
S. Hao, X. Ma, T. Dong, A. B. Cremers, C. Chun: An Assertion Framework for Mobile Robotic Programming with Spatial Reasoning. COMPSAC, 2018.
J. HaiLong, L. Hou, J. Li, T. Dong: Attributed and Predictive Entity Embedding for Fine-Grained Entity Typing in Knowledge Bases. COLING, 2018.
C. Bauckhage, C. Ojeda, R. Sifa, S. Wrobel: Adiabatic Quantum Computing for Kernel k=2 Means Clustering. LWDA, 2018.
C. Bauckhage, C. Ojeda, J. Schücker, R. Sifa, S. Wrobel: Informed Machine Learning Through Functional Composition. LWDA, 2018.
M. Bunse, N. Piatkowski, K. Morik: Towards a Unifying View on Deconvolution in Cherenkov Astronomy. LWDA, 2018.
E. Schubert, S. Hess, K. Morik: The Relationship of DBSCAN to Matrix Factorization and Spectral Clustering. LWDA, 2018.
R. Ramamurthy, C. Bauckhage, R. Sifa, S. Wrobel: Policy Learning Using SPSA. ICANN, 2018.
R. Sifa, D. Paurat, D. Trabold, C. Bauckhage: Simple Recurrent Neural Networks for Support Vector Machine Training. ICANN, 2018.
B. Wulff, J. Schücker, C. Bauckhage: SPSA for Layer-Wise Training of Deep Networks. ICANN, 2018.
Y. Cao, L. Hou, J. Li, Z. Liu, C. Li, X. Chen, T. Dong: Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision. EMNLP, 2018.
S. Buschjäger, K. Morik: Decision Tree and Random Forest Implementations for Fast Filtering of Sensor Data. In: IEEE Trans. on Circuits and Systems 65(1), 2018, 209-222.
P. Welke, T. Horvath, S. Wrobel: Probabilistic Frequent Subtrees for Efficient Graph Classification and Retrieval. In: Machine Learning 107(11), 2018, 1847 – 1873.
S. Giesselbach, K. Ullrich, M. Kamp, D. Paurat, T. Gärtner: Corresponding Projections for Orphan Screening. ML4H Workshop at NeurIPS, 2018.
M. Kamp, L. Adilova, J. Sicking, F. Hüger, P. Schlicht, T. Wirtz, S. Wrobel: Efficient Decentralized Deep Learning by Dynamic Model Averaging. ECML PKDD, 2018.
B. Kirsch, S. Giesselbach, D. Knodt, S. Rüping: Robust End-User-Driven Social Media Monitoring for Law Enforcement and Emergency Monitoring. In: Community-Oriented Policing and Technological Innovations, 2018, 29 – 36.
K. Morik, C. Bockermann, S. Buschjäger: Big Data Science. In: KI- Künstliche Intelligenz 32(1), 2018, 27 – 36.
H. Kotthaus: Methods for Efficient Resource Utilization in Statistical Machine Learning Algorithms. TUD, 2018.
C. Bauckhage, E. Brito, K. Cvejoski, C. Ojeda, J. Schücker, R. Sifa: Towards Shortest Paths via Adiabatic Quantum Computing. MLG, 2018.
2017
T. Liebig, N. Piatkowski, C. Bockermann, K. Morik: Dynamic route planning with real-time traffic predictions. In: Information Systems 64, 2017.
S. Hess, K. Morik, N. Piatkowski: The PRIMPING routine – Tiling through proximal alternating linearized minimization. In: Data Mining and Knowledge Discovery 31(4), 2017.
C. Bauckhage: A Neural Network Implementation of Frank-Wolfe Optimization. ICANN, 2017.
S. Hess, K. Morik: C-SALT: Mining Class-Specific ALTerations in Boolean Matrix Factorization. ECML PKDD, 2017.
K. Ullrich, M. Kamp, T. Gärtner, M. Vogt, S. Wrobel: Co-Regularised Support Vector Regression. ECML PKDD, 2017.
L. Pfahler, K. Morik, F. Elwert, S. Tabti, V. Krech: Learning Low-Rank Document Embeddings with Weighted Nuclear Norm Regularization. IEEE DSAA, 2017.
R. Sifa, C. Bauckhage: Online k-Maxoids Clustering. IEEE DSAA, 2017.
C. Bauckhage, E. Brito, K. Cvejoski, C. Ojeda, R. Sifa, S. Wrobel: Ising Models for Binary Clustering via Adiabatic Quantum Computing. EMMCVPR, 2017.
H. Kotthaus, J. Richter, A. Lang, J. Thomas, B. Bischl, P. Marwedel, J. Rahnenführer, M. Lang: RAMBO: Resource-Aware Model-Based Optimization with Scheduling for Heterogeneous Runtimes and a Comparison with Asynchronous Model-Based Optimization. LION, 2017.
2016
M. Neumann, R. Garnett, C. Bauckhage, K. Kersting: Propagation kernels: efficient graph kernels from propagated information. In: Machine Learning 102(2), 2016.
C. Pölitz, W. Duivesteijn, K. Morik: Interpretable domain adaptation via optimization over the Stiefel manifold. In: Machine Learning 104(2-3), 2016.
N. Piatkowski, K. Morik: Stochastic Discrete Clenshaw-Curtis Quadrature. ICML, 2016.
N. Piatkowski, S. Lee, K. Morik: Integer undirected graphical models for resource-constrained systems. In: Neurocomputing 173, 2016.
R. Sifa, S. Srikanth, A. Drachen, C. Ojeda, C. Bauckhage: Predicting Retention in Sandbox Games with Tensor Factorization-based Representation Learning. IEEE CIG, 2016.
J. Richter, H. Kotthaus, B. Bischl, P. Marwedel, J. Rahnenführer, M. Lang: Faster Model-Based Optimization through Resource-Aware Scheduling Strategies. LION, 2016.
I. Korb, H. Kotthaus, P. Marwedel: mmapcopy: Efficient Memory Footprint Reduction using Application-Knowledge. SAC, 2016.
- 2022
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2022
T. H. Schulz, P. Welke, S. Wrobel: Graph Filtration Kernels. AAAI, 2022.
H. Tan, H. Kotthaus: Surrogate Model-Based Explainability Methods for Point Cloud NNs. WACV, 2022.
N. Andrienko, G. Andrienko, L. Adilova, S. Wrobel: Visual Analytics for Human-Centered Machine Learning. In: IEEE Computer Graphics and Applications 42(1), 2022, 123-133.
D. Antweiler, M. Marmening, N. Marheineke, A. Schmeißer, R. Wegener, P. Welke: Graph-Based Tensile Strength Approximation of Random Nonwoven Materials by Interpretable Regression. In: Machine Learning with Applications 8, 2022.
H.-J. Jin, T. Dong, L. Hou, J. Li, et al: How Can Cross-lingual Knowledge Contribute Better to Fine-Grained Entity Typing?. ACL, 2022.
C. Bauckhage, R. Sifa: Gradient Flows for Linear Discriminant Analysis. LION, 2022.
M. Amir, C. Bauckhage, A. Chircu, C. Czarnecki, C. Knopf, N. Piatkowski, E. Sultanow: What Can We Expect from (Quantum) Digital Twins?. Wirtschaftsinformatik, 2022.
E. Sultanoow, C. Bauckhage, C. Knopf, N. Piatkowski: Sicherheit von Quantum Machine Learning. In: Wirtschaftsinformatik & Management 14, 2022, 144-152.
C. Bauckhage, T. Gerlach, N. Piatkowski: QUBOs for Sorting Lists and Building Trees. arXiv preprint, 2022.
L. Hillebrand, T. Deusser, C. Bauckhage, R. Sifa: KPI-BERT: A Joint Named Entity Recognition and Relation Extraction for Financial Reports. ICPR, 2022.
T. H. Schulz, P. Welke, T. Horvath, S. Wrobel: A Generalized Weisfeiler-Lehman Graph Kernel. In: Machine Learning 111, 2022, 2601-2629.
D. Biesner, R. Ramamurthy, R. Stenzl, M. Luebbering, L. Hillebrand, A. Ladi, M. Pielka, R. Loitz, C. Bauckhage, R. Sifa: Anonymization of German Financial Documents Using Neural Network- Based Language Models with Contextual Word Representations. In: International Journal of Data Science and Analytics 13, 2022, 151-161.
D. Biesner, R. Sifa, C. Bauckhage, B. Kliem: Solving Subset Sum Problems using Binary Optimization with Applications in Auditing and Financial Data Analysis. In: TechRxiv preprint, 2022.
K. Cvejoski, R. Sánchez, C. Bauckhage, C. Ojeda: Dynamic Review-based Recommenders. Data Science – Analytics and Applications, 2022.
K. Beckh, S. Müller, S. Rüping: A Quantitative Human-Grounded Evaluation Process for Explainable ML. HCXAI Workshop at CHI, 2022.
A. Saadallah, M. Jakobs, K. Morik: Explainable Online Ensemble of Deep Neural Network Pruning for Time Series Forecasting. In: Machine Learning, 2022.
H. Liu, M. Brehler, M. Ravishankar, N. Vasilache, B. Vanik, S. Laurenzo: TinyIREE: An ML Execution Environment for Embedded Systems from Compilation to Deployment. In: IEEE Micro, 2022.
R. L. Wilking, M. Jakobs, K. Morik: Fooling Perturbation-Based Explainability Methods. Trustworthy Artificial Intelligence Workshop at ECML PKDD, 2022.
R. Fischer, M. Jakobs, S. Mücke, K. Morik: A Unified Framework for Assessing Energy Efficiency of Machine Learning. Data Science for Social Good Workshop at ECML PKDD, 2022.
K. Morik, H. Kotthaus, L. Heppe, D. Heinrich, R. Fischer, S. Mücke, A. Pauly, M. Jakobs, N. Piatkowski: Yes We Care! – Certification for Machine Learning Methods through the Care Label Framework. In: Frontiers in Artificial Intelligence, 2022.
M. Jakobs, H. Kotthaus, I. Röder, M. Baritz: SancScreen: Towards a real-world dataset for evaluating explainability methods. LWDA, 2022.
L. Pucknat, M. Pielka, R. Sifa: Towards Informed Pre-Training for Critical Error Detection in English-German. LWDA, 2022.
D. Biesner, K. Cvejoski, R. Sifa: Combining Variational Autoencoders and Transformer Language Models for Improved Password Generation. ARES, 2022.
C. L. Chapman, L. Hillebrand, M. R. Stenzel, T. Deusser, D. Biesner, C. Bauckhage, R. Sifa: Towards Generating Financial Reports from Tabular Data Using Transformers. CD-MAKE, 2022.
C. Bauckhage, H. Schneider, B. Wulff, R. Sifa: Gradient Flows for L2 Support Vector Machine Training. ICML, 2022.
A. Gouda, A. Ghanem, C. Reining: DoPose-6D dataset for object segmentation and 6D pose estimation. ICMLA, 2022.
J. Rutinowski, C. Pionzewski, T. Chilla, C. Reining, M. ten Hompel: Computer Vision Based Re-Identification of Wooden Euro-pallets. ICMLA, 2022.
E. Brito, V. Gupta, E. Hahn, S. Giesselbach: Assessing the Performance Gain on Retail Article Categorization at the Expense of Explainability and Resource Efficiency. KI, 2022.
T. Deußer, S. M. Ali, L. Hillebrand, D. Nurchalifah, B. Jacob, C. Bauckhage, R. Sifa: KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents. ICMLA, 2022.
L. Hillebrand, T. Deußer, T. Dilmaghani, B. Kliem, R. Loitz, C. Bauckhage, R. Sifa: Towards automating Numerical Consistency Checks in Financial Reports. BigData, 2022.
D. Boiar, N. Killich, L. Schulte, V. H. Moreno, J. Deuse, T. Liebig: Forecasting Algae Growth in Photo-Bioreactors using Attention LSTMs. AI4EA Workshop at SEFM, 2022.
- 2021
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2021
L. von Rueden, T. Wirtz, F. Hueger, J. D. Schneider, N. Piatkowski, C. Bauckhage: Street-Map Based Validation of Semantic Segmentation in Autonomous Driving. ICPR, 2021.
P. Welke, F. Alkhoury, C. Bauckhage, S. Wrobel: Decision Snippet Features. ICPR, 2021.
C. Ojeda, R. Sanchez, K. Cvejoski, J. Schuecker, D. Biesner, C. Bauckhage, B. Georgiev: Auto Encoding Explanatory Examples with Stochastic Paths. ICPR, 2021.
C. Ojeda, B. Georgiev, K. Cvejoski, J. Schuecker, C. Bauckhage, R. Sanchez: Switching Dynamical Systems with Deep Neural Networks. ICPR, 2021.
C. Ojeda, K. Cvejoski, J. Schuecker, B. Georgiev, C. Bauckhage, R. Sanchez: An Adversarial Approach towards Queuing Systems Modeling. AAAI, 2021.
V. Olari, K. Cvejoski, Ø. Eide: Introduction to Machine Learning with Robots and Playful Learning. AAAI, 2021.
J. Kalofolias, P. Welke, J. Vreeken: SUSAN: The Structural Similarity Random Walk Kernel. SIAM Data Mining, 2021.
M. Pielka, R. Sifa, L. P. Hillebrand, D. Biesner, R. Ramamurthy, A.Ladi, C. Bauckhage: Tackling Contradiction Detection in German Using Machine Translation and End-to-End Recurrent Neural Networks. ICPR, 2021.
S. Hänold, N. Schlee, D. Antweiler, K. Beckh: Die Nachvollziehbarkeit von KI-Anwendungen in der Medizin – eine Betrachtung aus juristischer Perspektive mit Beispielszenarien. In: Medizinrecht 39, 2021, 516-523.
B. Georgiev, L. Franken, M. Mukherjee: Heating up Decision Boundaries: Isocapacitory Saturation, Adversarial Scenarios and Generalization Bounds. ICLR, 2021.V. Gupta, K. Beckh, S. Giesselbach, D. Wegener, T. Wirtz: Supporting Verification of News Articles with Automated Search for Semantically Similar Articles. ROMCIR Workshop at ECIR, 2021.L. Hillebrand, D. Biesner, C. Bauckhage, R. Sifa: Interpretable Topic Extraction and Word Embedding Learning Using Non-Negative Tensor DEDICOM. In: Machine Learning and Knowledge Extraction 3(19), 2021, 123-167.A. Saadallah, M. Tavakol, K. Morik: An Actor-Critic Ensemble Aggregation Model for Time-Series Forecasting. ICDE, 2021.A. Saadallah, K. Morik: Meta-Adversarial Training of Neural Networks for Binary Classification. IJCNN, 2021.Z. Yao, C. Li, T. Dong , X. Lv, J. Yu, L. Hou, J. Li, Y. Zhang, Z. Dai: Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making. ACL IJCNLP, 2021.J. Rosenzweig, J. Sicking, S. Houben, M. Mock, M. Akila: Patch Shortcuts: Interpretable Proxy Models Efficiently FindBlack-Box Vulnerabilities. SAIAD Workshop at CVPR, 2021.L. Adilova, E. Schulz, M. Akila, S. Houben, J. D. Schneider, F. Hüger, T. Wirtz: Plants Don’t Walk on the Street: Common-Sense Reasoning for Reliable Semantic Segmentation. SAIAD Workshop at CVPR, 2021.C. Ojeda, K. Cvejoski, B. Georgiev, C. Bauckhage, J. Schuecker, R. J. Sanchez: Learning Deep Generative Models for Queueing Systems. AAAI, 2021.
D. Biesner, K. Cvejoski, B. Georgiev, R. Sifa, E. Krupicka: Advances in Password Recovery using Generative Deep Learning Techniques. ICANN, 2021.
A. Saadallah, M. Jakobs, K. Morik: Explainable Online Deep Neural Network Selection using Adaptive Saliency Maps for Time Series Forecasting. ECML PKDD, 2021.
S. Houben, S. Abrecht, M. Akila, A. Bär, F. Brockherde, P. Feifel, T. Fingscheidt, S. S. Gannamanen, S. E. Ghobadi, A. Hammam, A. Haselhoff, F. Hauser, C. Heinzemann, M. Hoffmann, N. Kapoor, F. Kappel, M. Klingner, J. Kronenberger, F. Küppers, J. Löhdefink, M. Mlynarski, M. Mock, F. Mualla, S. Pavlitskaya, M. Poretschkin, A. Pohl, V. Ravi-Kumar, J. Rosenzweig, M. Rottmann, S. Rüping, T. Sämann, J. D. Schneider, E. Schulz, G. Schwalbe, J. Sicking, T. Srivastava, S. Varghese, M. Weber, S. Wirkert, T. Wirtz, M. Woehrle: Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety. In: Deep Neural Networks and Data for Automated Driving, 2021, 3-78.
K. Morik, S. Buschjäger, P. Honysz, L. Pfahler: Very Fast Streaming Submodular Function Maximization. ECML PKDD, 2021.
M. Bunse, K. Morik: Certification of Model Robustness in Active Class Selection. ECML PKDD, 2021.
D. Weichert, F. Horchler, A. Kister, M. Trost, J. Hartung, S. Risse: Monte Carlo Tree Search for High Precision Manufacturing. RL4RL Workshop at ICML, 2021.
M. Bunse, K. Morik: Active Class Selection with Uncertain Deployment Class Proportions. IAL Workshop at ECML PKDD, 2021.
S. Chen, N. V. Andrienko, G. L. Andrienko, J. Lie, X. Yuan: Co-Bridges: Pair-wise Visual Connection and Comparison for Multi-item Data Streams. In: IEEE Trans. Vis. Comput. Graph 27(2), 2021, 1612-1622.
M. Langer, D. Oster, T. Speith, H. Hermanns, L. Kästner, E. Schmidt, A. Sesing, K. Baum: What do we want from Explainable Artificial Intelligence (XAI)? – A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research. In: Artif. Intell. 296, 2021, 103473.
A. Amini, A. S. Periyasamy, S. Behnke: T6D-Direct: Transformers for Multi-Object 6D Pose Direct Regression. DAGM GCPR, 2021.
A. S. Periyasamy, M. Schwarz, S. Behnke: A Dataset for Dynamic Bin Picking Scene Understanding. DAGM GCPR, 2021.
K. Cvejoski, R. Sanchez, C. Bauckhage, C. Ojeda: Dynamic Review based Recommenders. iDSC, 2021.
F. Gonsior, S. Mücke, K. Morik: Conditional Structure Search for Normalizing Flows. FGKDML Workshop at LWDA, 2021.
N. Piatkowski, J. S. Mueller-Roemer, P. Hasse, A. Bachorek, T. Werner, P. Birnstill, A. Morgenstern, L. Stobbe: Generative Machine Learning for Resource-Aware 5G and IoT Systems. Workshop at ICC, 2021.
N. Piatkowski, P. N. Posch, M. Krause: How to Trust Generative Probabilistic Models for Time-Series Data?. LION, 2021.
R. Ramamurthy, M. Lübbering, T. Bell, M. Gebauer, B. Ulusay, D. Uedelhoven, T. D. Khameneh, R. Loitz, M. Pielka, C. Bauckhage, R. Sifa: Automatic Indexing of Financial Documents via Information Extraction. SSCI, 2021.
M. Lübbering, M. Gebauer, R. Ramamurthy, C. Bauckhage, R. Sifa: Decoupling Autoencoders for Robust One-vs-Rest Classification. DSAA, 2021.
R. Agombar, C. Bauckhage, M. Lübbering, R. Sifa: An Optimization for Convolutional Network Layers Using the Viola-Jones Framework and Ternary Weight Networks. In: International Conference on Learning and Intelligent Optimization, 2021, 1-6.
M. Lübbering, M. Gebauer, R. Ramamurthy, R. Sifa, C. Bauckhage: Supervised autoencoder variants for end to end anomaly detection. In: International Conference on Pattern Recognition, 2021, 566-581.
M. Lübbering, M. Pielka, K. Das, M. Gebauer, R. Ramamurthy, C. Bauckhage, R. Sifa: Toxicity Detection in Online Comments with Limited Data: A Comparative Analysis. ESANN, 2021.
M. Lübbering, M. Gebauer, R. Ramamurthy, M. Pielka, C. Bauckhage, R. Sifa: Utilizing representation learning for robust text classification under datasetshift. CEUR Workshop at LWDA, 2021.
D. Biesner, R. Ramamurthy, R. Stenzel, M. Lübbering, L. Hillebrand, A. Ladi, M. Pielka, R. Loitz, C. Bauckhage, R. Sifa: Anonymization of German Financial Documents Using Neural Network- based Language Models with Contextual Word Representations. In: Int J Data Sci Anal, 2021, 1-11.
M. Günder, N. Piatkowski, L. von Rüden, R. Sifa, C. Bauckhage: Towards Intelligent Food Waste Prevention: An Approach Using Scalable and Flexible Harvest Schedule Optimization With Evolutionary Algorithms. In: IEEE Access 9, 2021, 169044-169055.
M. Bulla, L. Hillebrand, M. Lübbering, R. Sifa: Knowledge Graph Based Question Answering System for Financial Securities. In: KI 2021: Advances in Artificial Intelligence, 2021, 44-50.
H. Kondratiuk, R. Sifa: Swords, Data and Balls: Extracting Extreme Behavioural Prototypes with Kernel Minimum Enclosing Balls. CoG, 2021.
L. Pucknat, M. Pielka, R. Sifa: Detecting Contradictions in German Text: A Comparative Study. SSCI, 2021.
R. Ramamurthy, M. Pielka, R. Stenzel, C. Bauckhage, R. Sifa, T. D. Khameneh, U. Warning, B. Kliem, R. Loitz: ALiBERT: Improved Automated List Inspection (ALI) with BERT. DocEng, 2021.
R. Sifa: Predicting Player Churn with Echo State Networks. CoG, 2021.
R. Sifa, A. Drachen, F. Block, S. Moon, A. Dubhashi, H. Xiao, Z. Li, D. Klabjan, S. Demediuk: Archetypal Analysis Based Anomaly Detection for Improved Storytelling in Multiplayer Online Battle Arena Games. ACSW, 2021.
A. Wahab, R. Sifa: DIBERT: Dependency Injected Bidirectional Encoder Representations from Transformers. SSCI, 2021.
C. Bauckhage, J. Gall, A. Schwing: Pattern Recognition. Springer, 2021.
T. Dong, A. Rettinger, J. Tang, B. Tversky, F. van Harmelen: Structure and Learning. In: Dagstuhl Reports 11(8), 2021, 11-34.
P. Honysz, S. Buschjäger, K. Morik: GPU-Accelerated Optimizer-Aware Evaluation of Submodular Exemplar Clustering. arxiv preprint, 2021.
P. Honysz, A. Schulze-Struchtrup, S. Buschjäger, K. Morik: Providing Meaningful Data Summarizations Using Examplar-based Clustering in Industry 4.0. arxiv preprint, 2021.
A. Gouda, A. Ghanem, P. Kaiser, M. ten Hompel: Object class-agnostic segmentation for practical CNN utilization in industry. ICMERR, 2021.
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2020
K. Cvejoski, R. J. Sanchez, B. Georgiev, J.Schuecker, C. Bauckhage, C. Ojeda: Recurrent Point Processes for Dynamic Review Models. WICRS Workshop at AAAI, 2020.
D. Biesner, R. Ramamurthy, M. Lübbering, B. Fürst, H. Ismail, L. Hillebrand, A. Ladi, M. Pielka, R. Stenzel, T. Khameneh, V. Krapp, I. Huseynov, J. Schlums, U. Stoll, U. Warning, B. Kliem, C. Bauckhage, R. Sifa: Leveraging Contextual Text Representations for Anonymizing German Financial Documents. AAAI Workshop on Knowledge Discovery from Unstructured Data in Financial Services at KDF, 2020.
O. Urbann, S. Camphausen, A. Moos, I. Schwarz, S. Kerner, M. Otten: A C Code Generator for Fast Inference and Simple Deployment of Convolutional Neural Networks on Resource Constrained Systems. C4ML Workshop at CGO, 2020.
M. Nanni, A. Gennady, A.-L. Barabasi, C. A. Boldrini, F. Bonchi, C. Cattuto, F. Chiaromonte, G. Commande, M. Conti, M. Cote, F. Dignum, V. Dignum, J. Domingo-Ferrer, P. Ferragina, F. Giannotti, R. Guidotti, D. Helbing, K. Kaski, J. Kertesz, S. Lehmann, B. Lepri, P. Lukowicz, S. Matwin, J. Megias, D. Megias, A. Monreale, K. Morik, N. Oliver, A. Passarella, A. Passerini, D. Pedreschi, A. Pentland, F. Pianesi, F. Pratesi, S. Rinzivillo, S. Ruggieri, A. Siebes, V. Torra, R. Trasarti, J. van der Hoven, A. Vespignani: Give More Data, Awareness and Control to Individual Citizens, and They Will Help COVID-19 Containment. In: Trans. Data Priv. 13, 2020, 61-66.
L. von Rueden, S. Mayer, R. Sifa, C. Bauckhage, J. Garcke: Combining Machine Learning and Simulation to a Hybrid Modelling Approach: Current and Future Directions. IDA, 2020.
C. Bauckhage, R. Sifa, S. Wrobel: Adiabatic Quantum Computing for Max-Sum Diversification. SDM, 2020.
R. Sifa: DESICOM as Metaheuristic Search. LION, 2020.
R. Sifa, C. Bauckhage: Novelty Discovery with Kernel Minimum Enclosing Balls. LION, 2020.
M. Cekic, B. Georgiev, M. Mukherjee: Polyhedral Billiards, Eigenfunction Concentration and Almost Periodic Control. In: Commun. in Math. Phys. 377, 2020, 2451-2487.
C. Bauckhage, R. Sanchez, R. Sifa: Problem Solving with Hopfield Networks and Adiabatic Quantum Computing. IJCNN, 2020.
K. Cvejoski, C. Ojeda, B. Georgiev, C. Bauckhage, R. J. Sanchez: Recurrent Point Review Models. IJCNN, 2020.
A. Kiwan, S. Giesselbach, S. Rüping: Incorporating Knowledge Bases into SciBERT and BioBERT Pre-Trained Language Models. SciNLP Workshop at AKBC, 2020.
B. Sliwa, N. Piatkowski, C. Wietfeld: LIMITS: Lightweight Machine Learning for IoT Systems with Resource Limitations. ICC, 2020.
B. Sliwa, N. Piatkowski, C. Wietfeld: The Channel as a Traffic Sensor: Vehicle Detection and Classification Based on Radio Fingerprinting. In: IEEE Internet of Things Journal 7, 2020, 7392-7406.
F. Finkeldey, A. Saadallah, P. Widerkehr, K. Morik: Real-Time Prediction of Process Forces in Milling Operations Using Synchronized Data Fusion of Simulation and Sensor Data. In: Eng. Appl. Artif. Intell. 94, 2020.
L. von Rueden, T. Wirtz, F. Hueger, J. D. Schneider, C. Bauckhage: Towards Map-Based Validation of Semantic Segmentation Masks. AIAD Workshop at ICML, 2020.
K. Y. Chai, J. Stenzel, J. Jost: Generation, Classification and Segmentation of Point Clouds in Logistic Context with PointNet++ and DGCNN. IRCE, 2020.
L. Hillebrand, D. Biesner, C. Bauckhage, R. Sifa: Interpretable Topic Extraction and Word Embedding Learning Using Row-Stochastic DEDICOM. CD-MAKE, 2020.
L. Pfahler, K. Morik: Semantic Search in Millions of Equations. KDD, 2020.
P. Welke, F. Seiffarth, M. Kamp, S. Wrobel: HOPS: Probabilistic Subtree Mining for Small and Large Graphs. KDD, 2020.
B. Kirsch, S. Giesselbach, T. Schmude, M. Völkening, F. Rostalsko, S.Rüping: Probabilistic Soft Logic to Improve Information Extraction in the Legal Domain. LWDA, 2020.
R. Fischer, M. Jakobs, S. Mücke, K. Morik: Solving Abstract Reasoning Tasks with Grammatical Evolution. LWDA, 2020.
J. Kindermann, K. Beckh: Fusing Multi-label Classification and Semantic Tagging. KDML Workshop at LWDA, 2020.
S. Buschjäger, L. Pfahler, J. Buss, K. Morik, W. Rhode: On-Site Gamma-Hadron Separation with Deep Learning on FPGAs. ECML PKDD, 2020.
L. Heppe, M. Kamp, L. Adilova, N. Piatkowski, D. Heinrich, K. Morik: Resource-Constrained On-Device Learning by Dynamic Averaging. PDFL Workshop at ECML PKDD, 2020.
F. Seiffarth, T. Horvath, S. Wrobel: Maximum Margin Separations in Finite Closure Systems. ECML PKDD, 2020.
B. Georgiev, G. Angelov: On Learning a Control System without Continuous Feedback. ESANN, 2020.
B. Georgiev, L. Franken: Explorations in Quantum Neural Networks with Intermediate Measurements. ESANN, 2020.
R. Fischer, N. Piatkowski, C. Pelletier, G. Webb, F. Petitjean, K. Morik: No Cloud on the Horizon: Probabilistic Gap Filling in Satellite Image Series. DSAA, 2020.
C. Bauckhage, M. Bortz, R. Sifa: Shells within Minimum Enclosing Balls. DSAA, 2020.
P. Welke: Efficient Frequent Subgraph Mining in Transactional Databases. DSAA, 2020.
A. Saadallah, K. Morik: Active Sampling for Learning Interpretable Surrogate Machine Learning Models. DSAA, 2020.
A. Mehler, W. Hemati, P. Welke, M. Konca, T. Uslu: Multiple Texts as a Limiting Factor in Online Learning: Quantifying (Dis-)similarities of Knowledge Networks across Languages. In: Frontiers in Education, 2020.
M. Bunse, D. Weichert, A. Kister, K. Morik: Optimal Probabilistic Classification in Active Class Selection. ICDM, 2020.
D. Antweiler, P. Welke: Temporal Graph Analysis for Outbreak Pattern Detection in COVID-19 Contact Tracing Networks. MLPH Workshop at NeurIPS, 2020.
T. Dong: A Geometric Approach to The Unification of Symbolic Structures and Neural Networks. Springer, 2020.
C. Bauckhage, R. Ramamurthy, R. Sifa: Hopfield Networks for Vector Quantization. ICANN, 2020.
R. Ramamurthy, R. Sifa, M. Lübbering, C. Bauckhage: Guided Reinforcement Learning via Sequence Learning. ICANN, 2020.
M. Lübbering, R. Ramamurthy, M. Gebauer, T. Bell, R. Sifa, C. Bauckhage: From Imbalanced Classification to Supervised Outlier Detection Problems: Adversarially Trained Auto Encoders. ICANN, 2020.
M. Masoudinejad: Open-Loop Dynamic Modeling of Low-Budget Batteries with Low-Power Loads. In: MDPI Batteries 6(4), 2020, 50.
S. Buschjäger, P. J. Honysz, K. Morik: Randomized Outlier Detection with Trees. In: Int. J. Data Sci. Anal. 13, 2020, 91-204.
L. Franken, B. Georgiev, S. Muecke, M. Wolter, N. Piatkowski, C. Bauckhage: Gradient-Free Quantum Optimization on NISQ Devices. arxiv preprint, 2020.
M. Elahi, R. Hosseini, M. H. Rimaz, F. B. Moghaddam, C. Trattner: Visually-Aware Video Recommendation in the Cold Start. HT, 2020.
X. Han, T. Grubenmann, R. Cheng, S. C. Wong, X. Li, W. Sun: Traffic Incident Detection: A Trajectory-Based Approach. ICDE, 2020.
L. Reimann, G. Kniesel-Wünsche: Achieving Guidance in Applied Machine Learning through Software Engineering Techniques. Programming, 2020.
A. Sadeghi, D. Graux, H. S. Yazdi, J. Lehmann: MDE: Multiple Distance Embeddings for Link Prediction in Knowledge Graphs. ECAI, 2020.
H. Zafar, M. Tavakol, J. Lehmann: Distantly Supervised Question Parsing. ECAI, 2020.
F. A. Musyaffa, M. Vidal, F. Orlandi, J. Lehmann, H. Jabeen: IOTA: Interlinking of Heterogeneous Multilingual Open Fiscal DaTA. In: Expert Syst. Appl. 147(4), 2020.
M. Cremaschi, F. De Paoli, A. Rula, B. Spahiu: A Fully Automated Approach to a Complete Semantic Table Interpretation. In: Future Gener. Comput. Syst. 112, 2020, 478 – 500.
D. Tomaszuk, R. Angles, H. Thakkar: PGO: Describing Property Graphs in RDF. In: IEEE Access 8, 2020, 118355 – 118369.
S. Payrosangari, A. Sadeghi, D. Graux, J. Lehmann: Meta-hyperband: Hyperparameter Optimization with Meta-Learning and Coarse-to-Fine. IDEAL, 2020.
M. Nayyeri, X. Zhou, S. Vahdati, R. Izanloo, H. S. Yazdi, J. Lehmann: Let the Margin SlidE for Knowledge Graph Embeddings via a Correntropy Objective Function. IJCNN, 2020.
H. Jabeen, D. Graux, G. Sejdiu: Scalable Knowledge Graph Processing Using SANSA. In: Knowledge Graphs and Big Data Processing 12072, 2020, 105 – 121.
R. Nedelchev, R. Usbeck, J. Lehmann: Treating Dialogue Quality Evaluation as an Anomaly Detection Problem. LREC, 2020.
M. T. Chau, D. Esteves, J. Lehmann: A Neural-Based Model to Predict the Future Natural Gas Market Price through Open-domain Event Extraction. ESWC, 2020.
S. R. Bader, I. Grangel-González, P. Nanjappa, M. Vidal, M. Maleshkova: A Knowledge Graph for Industry 4.0. ESWC, 2020.
E. Kacupaj, H. Zafar, J. Lehmann, M. Maleshkova: VQuAnDa: Verbalization QUestion ANswering DAtaset. ESWC, 2020.
M. Nayyeri, S. Vahdati, X. Zhou, H. S. Yazdi, J. Lehmann: Embedding-Based Recommendations on Scholarly Knowledge Graphs. ESWC, 2020.
M. Nayyeri, C. Xu, S. Vahdati, N. Vassilyeva, E. Sallinger, H. S. Yazdi, J. Lehmann: Fantastic Knowledge Graph Embeddings and How to Find the Right Space for Them. ISWC, 2020.
C. Xu, M. Nayyeri, F. Alkhoury, H. S. Yazdi, J. Lehmann: Temporal Knowledge Graph Completion Based on Time Series Gaussian Embedding. ISWC, 2020.
T. Grubenmann, R. C. K. Cheng, L. V. S. Lakshmanan: TSA: A Truthful Mechanism for Social Advertising. WSDM, 2020.
I. O. Mulang, K. Singh, A. Vyas, S. Shekarpour, M. Vidal, S. Auer, J. Lehmann: Encoding Knowledge Graph Entity Aliases in Attentive Neural Network for Wikidata Entity Linking. WISE, 2020.
Z. Say, S. Fathalla, S. Vahdati, J. Lehmann, S. Auer: Ontology Design for Pharmaceutical Research Outcomes. TPDL, 2020.
M. Elias, M. R. Tavakoli, S. Lohmann, G. Kismihók, S. Auer: An OER Recommender System Supporting Accessibility Requirements. ASSETS, 2020.
H. Jabeen, E. Haziiev, G. Sejdiu, J. Lehmann: DISE: A Distributed in-Memory SPARQL Processing Engine over Tensor Data. ICSC, 2020.
H. Thakkar, R. Angles, M. Rodriguez, S. Mallette, J. Lehmann: Let’s Build Bridges, Not Walls: SPARQL Querying of TinkerPop Graph Databases with Sparql-Gremlin. ICSC, 2020.
R. Angles, H. Thakkar, D. Tomaszuk: Mapping RDF Databases to Property Graph Databases. In: IEEE Access 8, 2020, 86091 – 86110.
H. Jabeen, J. Weinz, J. Lehmann: AutoChef: Automated Generation of Cooking Recipes. CEC, 2020.
H. Jabeen: Big Data Outlook, Tools, and Architectures. In: Knowledge Graphs and Big Data Processing, 2020, 35 – 55.
M. Tasnim, D. Collarana, D. Graux, M. Vidal: Context-Based Entity Matching for Big Data. In: Knowledge Graphs and Big Data Processing, 2020, 122 – 146.
J. Rose, J. Lehmann: CASQAD – A New Dataset for Context-Aware Spatial Question Answering. ISWC, 2020.
I. O. Mulang‘, K. Singh, C. Prabhu, A. Nadgeri, J. Hoffart, J. Lehmann: Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models. CIKM, 2020.
J. Armitage, E. Kacupaj, G. Tahmasebzadeh, Swati, M. Maleshkova, R. Ewerth, J. Lehmann: MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities. CIKM, 2020.
M. Nayyeri, M. M. Alam, J. Lehmann, S. Vahdati: 3D Learning and Reasoning in Link Prediction Over Knowledge Graphs. In: IEEE Access 8, 2020.
A. Say, S. Fathalla, S. Vahdati, J. Lehmann S. Auer: Semantic Representation of Physics Research Data. IC3K, 2020.
J. Armitage, S. Thakur, R. Tripathi, J. Lehmann, M. Maleshkova: Training Multimodal Systems for Classification with Multiple Objectives. CLEOPATRA Workshop at ESWC, 2020.
M. Galkin, P. Trivedi, G. Maheshwari, R. Usbeck, J. Lehmann: Message Passing for Hyper-Relational Knowledge Graphs. EMNLP, 2020.
C. Xu, M. Nayyeri, Y. Chen, J. Lehmann: Knowledge Graph Embeddings in Geometric Algebras. COLING, 2020.
C. Xu, M. Nayyeri, F. Alkhoury, H. S. Yazdi, J. Lehmann: TeRo: A Time-Aware Knowledge Graph Embedding via Temporal Rotation. COLING, 2020.
R. Nedelchev, J. Lehmann, R. Usbeck: Language Model Transformers as Evaluators for Open-Domain Dialogues. COLING, 2020.
E. Iglesias, S. Jozashoori, D. Chaves-Fraga, D. Collarana, M. Vidal: SDM-RDFizer: An RML Interpreter for the Efficient Creation of RDF Knowledge Graphs. CIKM, 2020.
A. Rivas, I. Grangel-González, D. Collarana, J. Lehmann, M. Vidal: Unveiling Relations in the Industry 4.0 Standards Landscape Based on Knowledge Graph Embeddings. DEXA, 2020.
D. Banerjee, D. Chaudhuri, M. Dubey, J. Lehmann: PNEL: Pointer Network Based End-To-End Entity Linking over Knowledge Graphs. ISWC, 2020.
C. Ojeda, R. Sanchez, K. Cvejoski, J. Schuecker, D. Biesner, C. Bauckhage, B. Georgiev: Auto Encoding Explanatory Examples with Stochastic Paths. ICPR, 2020.
C. Ojeda, B. Georgiev, K. Cvejoski, J. Schuecker, C. Bauckhage, R. Sanchez: Switching Dynamical Systems with Deep Neural Networks. ICPR, 2020.
L. v. Rüden, S. Mayer, K. Beckh, B. Georgiev, S. Giesselbach, R. Heese, B. Kirsch, J. Pfrommer, A. Pick, R. Ramamurthy, M. Walczak, J. Garcke, C. Bauckhage, J. Schuecker: Informed Machine Learning – A Taxonomy and Survey of Integrating Knowledge into Learning Systems. In: IEEE Trans. Knowl. Data Eng., 2020.
P. H. Nguyen, R. Henkin, S. Chen, N. V. Andrienko, G. L. Andrienko, O. Thonnard, C. Turkay: VASABI: Hierarchical User Profiles for Interactive Visual User Behaviour Analytics. In: IEEE Trans. Vis. Comput. Graph 26(1), 2020, 77 – 86.
S. Chen, N. V. Andrienko, G. L. Andrienko, L. Adilova, J. Barlet, J. Kindermann, P. H: Nguyen, O. Thonnard, C. Turkay: LDA Ensembles for Interactive Exploration and Categorization of Behaviors. In: IEEE Trans. Vis. Comput. Graph 26(9), 2020, 2775 – 2792.
F. Seiffarth, T. Horvath, S. Wrobel: Maximal Closed Set and Half-Space Separations in Finite Closure Systems. ECML PKDD, 2020.
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2019
T. Dong, Z. Wang, J. Li, C. Bauckhage, and A. B. Cremers: Triple Classification Using Regions and Fine-Grained Entity Typing. AAAI, 2019.
S. Hess, W. Duivesteijn, K.Morik, P.-J. Honysz: The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering. AAAI, 2019.
S. Buschjäger, T. Liebig, K. Morik: Gaussian Model Trees for Traffic Imputation. ICPRAM, 2019.
A. Saadallah, N. Piatkowski, F. Finkeldey, P. Wiederkehr, K. Morik: Learning Ensembles in the Presence of Imbalanced Classes. ICPRAM, 2019.
G. Meschke, B.-T. Cao, A. Egorov, A. Saadallah, S. Freitag, K. Morik: Big Data and Simulation – A New Approach for Real-Time TBM Steering. In: Tunnels and Underground Cities: Engineering and Innovation meet Archaeology, Architecture and Art, 2019, 2681-2690.
T. Dong, C. Bauckhage, H. Jin, J. Li, O. Cremers, D. Speicher, A.B. Cremers, J. Zimmermann: Imposing Category Trees onto Word-Embeddings Using a Geometric Construction. ICLR, 2019.
V. Gupta, S. Giesselbach, S. Rüping, C. Bauckhage: Improving Word Embeddings Using Kernel PCA. RepL4NLP Workshop at ACL, 2019.
A. Saadallah, A. Egorov, B.-T. Cao, S. Freitag, K. Morik, G. Meschke: Active Learning for Accurate Settlement Prediction Using Numerical Simulations in Mechanized Tunneling. CIRP, 2019.
P. Welke, T. Horvath, S. Wrobel: Probabilistic and Exact Frequent Subtree Mining in Graphs Beyond Forests. In: Machine Learning 108, 2019, 1137-1164.
D. Trabold, T. Horvath, S. Wrobel: Effective Approximation of Parametrized Closure Systems over Transactional Data Streams. In: Machine Learning 109, 2019, 1147-1177.
N. Piatkowski: Distributed Generative Modelling with Sub-Linear Communication Overhead. DMLE Workshop at ECML PKDD, 2019.
S. Mücke, N. Piatkowski, K. Morik: Hardware Accelerated Learning at the Edge. DMLE Workshop at ECML PKDD, 2019.
B. Kirsch, Z. Niyazova, S. Rüping, M. Mock: Noise Reduction in Distant Supervision for Relation Extraction Using Probabilistic Soft Logic. DINA Workshop at ECML PKDD, 2019.
N. Piatkowski: Hyper-Parameter-Free Generative Modelling with Deep Boltzmann Trees. ECML PKDD, 2019.
M. Tavakol, S. Mair, K. Morik: HyperUCB: Hyperparameter Optimization Using Contextual Bandits. International Workshop at ECML PKDD, 2019.
L. Pfahler, J. Schill, K. Morik: The Search for Equations – Learning to Identify Similarities Between Mathematical Expressions. ECML PKDD, 2019.
A. Saadallah, F. Priebe, K. Morik: A Drift-based Dynamic Ensemble Members Selection Using Clustering for Time Series Forecasting. ECML PKDD, 2019.
P. Welke, T. Schulz: On the Necessity of Graph Kernel Baselines. Graph Embedding and Mining Workhop at ECML PKDD, 2019.
F. Seiffarth, T. Horváth, S. Wrobel: Maximal Closed Set and Half-Space Separations in Finite Closure Systems. ECML PKDD, 2019.
C. Bauckhage, R. Sifa, T. Dong: Prototypes within Minimum Enclosing Balls. Workshop and Special Session at ICANN, 2019.
R. Ramamurthy, C. Bauckhage, R. Sifa, J. Schücker, S. Wrobel: Leveraging Domain Knowledge for Reinforcement Learning Using MMC Architectures. ICANN, 2019.
C. Bauckhage, N. Piatkowski, R. Sifa, D. Hecker, S. Wrobel: A QUBO Formulation of the k-Medoids Problem. LWDA, 2019.
R. Fischer, N. Piatkowski, K. Morik: Parameter Sharing for Spatio-Temporal Process Models. LWDA, 2019.
S. Mücke, N. Piatkowski, K. Morik: Learning Bit by Bit: Extracting the Essence of Machine Learning. LWDA, 2019.
J. HaiLong, H. Lei, L. Juanzi, T. Dong: Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks. EMNLP/IJCNLP, 2019.
R. Sifa, R. Yawar, R. Ramarmurthy, C. Bauckhage, K. Kersting: Matrix- and Tensor Factorization for Game Content Recommendation. In: KI – Künstliche Intelligenz 34, 2019, 57-67.
M. Masoudinejad: Data-Sets for Indoor Photovoltaic Behavior in Low Lighting Conditions. In: MPDI Data 5(2), 2019, 32.
S. Kerner, J. Leveling , M. Otten, O. Urbann, M. Vogel, L. Weickhmann: Anwendungsfelder von künstlicher Intelligenz in Industrie 4.0 Systemen. In: Handbuch Industrie 4.0, 2019, 1-24.
P. Tözün, H. Kotthaus: Scheduling Data-Intensive Tasks on Heterogeneous Many Cores. In: IEEE Data Eng. Bull., 2019.
H. Kotthaus, L. Schönberger, A. Lang, J. Chen, P. Marwedel: Can Flexible Multi-Core Scheduling Help to Execute Machine Learning Algorithms Resource-Efficiently? SCOPES, 2019.
S. Mücke, N. Piatkowski, K. Morik: Hardware Acceleration of Machine Learning Beyond Linear Algebra. International Workshop at ECML PKDD, 2019.
- 2018
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2018
R. Schiffers, K. Morik, A. Schulze Struchtrup, P.-J. Honysz, J. Wortberg: Anomaly Detection in Injection Molding Process Data Based on Unsupervised Learning. In: Journal of Plastics Technology, 2018, 301 – 347.
S. Hess, N. Piatkowski, K. Morik: The Trustworthy Pal: Controlling the False Discovery Rate in Boolean Matrix Factorization. SIAM SDM, 2018.
K. Morik, W. Kraemer (Hg.): Daten – wem gehören sie, wer speichert sie, wer darf auf sie zugreifen? 2018.
K. Morik: Daten – wem gehören sie, wer speichert sie, wer darf auf sie zugreifen? In: Daten – wem gehören sie, wer speichert sie, wer darf auf sie zugreifen? 2018, 15 – 47.
L. Adilova, S. Giesselbach, S. Rüping: Making Efficient Use of a Domain Expert’s Time in Relation Extraction. DMNLP Workshop at ECML PKDD 2017. arXiv:1807.04687 [cs.LG], 2018.
S. Hao, X. Ma, T. Dong, A. B. Cremers, C. Chun: An Assertion Framework for Mobile Robotic Programming with Spatial Reasoning. COMPSAC, 2018.
J. HaiLong, L. Hou, J. Li, T. Dong: Attributed and Predictive Entity Embedding for Fine-Grained Entity Typing in Knowledge Bases. COLING, 2018.
C. Bauckhage, C. Ojeda, R. Sifa, S. Wrobel: Adiabatic Quantum Computing for Kernel k=2 Means Clustering. LWDA, 2018.
C. Bauckhage, C. Ojeda, J. Schücker, R. Sifa, S. Wrobel: Informed Machine Learning Through Functional Composition. LWDA, 2018.
M. Bunse, N. Piatkowski, K. Morik: Towards a Unifying View on Deconvolution in Cherenkov Astronomy. LWDA, 2018.
E. Schubert, S. Hess, K. Morik: The Relationship of DBSCAN to Matrix Factorization and Spectral Clustering. LWDA, 2018.
R. Ramamurthy, C. Bauckhage, R. Sifa, S. Wrobel: Policy Learning Using SPSA. ICANN, 2018.
R. Sifa, D. Paurat, D. Trabold, C. Bauckhage: Simple Recurrent Neural Networks for Support Vector Machine Training. ICANN, 2018.
B. Wulff, J. Schücker, C. Bauckhage: SPSA for Layer-Wise Training of Deep Networks. ICANN, 2018.
Y. Cao, L. Hou, J. Li, Z. Liu, C. Li, X. Chen, T. Dong: Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision. EMNLP, 2018.
S. Buschjäger, K. Morik: Decision Tree and Random Forest Implementations for Fast Filtering of Sensor Data. In: IEEE Trans. on Circuits and Systems 65(1), 2018, 209-222.
P. Welke, T. Horvath, S. Wrobel: Probabilistic Frequent Subtrees for Efficient Graph Classification and Retrieval. In: Machine Learning 107(11), 2018, 1847 – 1873.
S. Giesselbach, K. Ullrich, M. Kamp, D. Paurat, T. Gärtner: Corresponding Projections for Orphan Screening. ML4H Workshop at NeurIPS, 2018.
M. Kamp, L. Adilova, J. Sicking, F. Hüger, P. Schlicht, T. Wirtz, S. Wrobel: Efficient Decentralized Deep Learning by Dynamic Model Averaging. ECML PKDD, 2018.
B. Kirsch, S. Giesselbach, D. Knodt, S. Rüping: Robust End-User-Driven Social Media Monitoring for Law Enforcement and Emergency Monitoring. In: Community-Oriented Policing and Technological Innovations, 2018, 29 – 36.
K. Morik, C. Bockermann, S. Buschjäger: Big Data Science. In: KI- Künstliche Intelligenz 32(1), 2018, 27 – 36.
H. Kotthaus: Methods for Efficient Resource Utilization in Statistical Machine Learning Algorithms. TUD, 2018.
C. Bauckhage, E. Brito, K. Cvejoski, C. Ojeda, J. Schücker, R. Sifa: Towards Shortest Paths via Adiabatic Quantum Computing. MLG, 2018.
- 2017
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2017
T. Liebig, N. Piatkowski, C. Bockermann, K. Morik: Dynamic route planning with real-time traffic predictions. In: Information Systems 64, 2017.
S. Hess, K. Morik, N. Piatkowski: The PRIMPING routine – Tiling through proximal alternating linearized minimization. In: Data Mining and Knowledge Discovery 31(4), 2017.
C. Bauckhage: A Neural Network Implementation of Frank-Wolfe Optimization. ICANN, 2017.
S. Hess, K. Morik: C-SALT: Mining Class-Specific ALTerations in Boolean Matrix Factorization. ECML PKDD, 2017.
K. Ullrich, M. Kamp, T. Gärtner, M. Vogt, S. Wrobel: Co-Regularised Support Vector Regression. ECML PKDD, 2017.
L. Pfahler, K. Morik, F. Elwert, S. Tabti, V. Krech: Learning Low-Rank Document Embeddings with Weighted Nuclear Norm Regularization. IEEE DSAA, 2017.
R. Sifa, C. Bauckhage: Online k-Maxoids Clustering. IEEE DSAA, 2017.
C. Bauckhage, E. Brito, K. Cvejoski, C. Ojeda, R. Sifa, S. Wrobel: Ising Models for Binary Clustering via Adiabatic Quantum Computing. EMMCVPR, 2017.
H. Kotthaus, J. Richter, A. Lang, J. Thomas, B. Bischl, P. Marwedel, J. Rahnenführer, M. Lang: RAMBO: Resource-Aware Model-Based Optimization with Scheduling for Heterogeneous Runtimes and a Comparison with Asynchronous Model-Based Optimization. LION, 2017.
- 2016
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2016
M. Neumann, R. Garnett, C. Bauckhage, K. Kersting: Propagation kernels: efficient graph kernels from propagated information. In: Machine Learning 102(2), 2016.
C. Pölitz, W. Duivesteijn, K. Morik: Interpretable domain adaptation via optimization over the Stiefel manifold. In: Machine Learning 104(2-3), 2016.
N. Piatkowski, K. Morik: Stochastic Discrete Clenshaw-Curtis Quadrature. ICML, 2016.
N. Piatkowski, S. Lee, K. Morik: Integer undirected graphical models for resource-constrained systems. In: Neurocomputing 173, 2016.
R. Sifa, S. Srikanth, A. Drachen, C. Ojeda, C. Bauckhage: Predicting Retention in Sandbox Games with Tensor Factorization-based Representation Learning. IEEE CIG, 2016.
J. Richter, H. Kotthaus, B. Bischl, P. Marwedel, J. Rahnenführer, M. Lang: Faster Model-Based Optimization through Resource-Aware Scheduling Strategies. LION, 2016.
I. Korb, H. Kotthaus, P. Marwedel: mmapcopy: Efficient Memory Footprint Reduction using Application-Knowledge. SAC, 2016.