Publications related to the Competence Center Machine Learning Rhine-Ruhr (ML2R)

2021

L. v. 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.

2020

L. Pfahler, K. Morik: Semantic Search in Millions of Equations. KDD, 2020.

S. Buschjäger, L. Pfahler, J. Buss, K. Morik, W. Rhode: On-Site Gamma-Hadron Separation with Deep Learning on FPGAs. ECML PKDD, 2020.

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: Engineering Applications of Artificial Intelligence, Elsevier, 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. Transactions on Data Privacy, Vol. 13. April 2020, S. 61 – 66.

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.

R. Fischer, N. Piatkowski, C. Pelletier, G. Webb, F. Petitjean, K. Morik: No Cloud on the Horizon: Probabilistic Gap Filling in Satellite Image. DSAA, 2020.

A. Saadallah, K. Morik: Active Sampling for Learning Interpretable Surrogate Machine Learning Models. DSAA, 2020.

D. Weichert, K. Morik, M. Bunse, A. Kister: Optimal Probabilistic Classification in Active Class Selection. ICDM, 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. IEMTRONICS Workshop at IML, 2020.

B. Kirsch, S. Giesselbach, T. Schmude, S.Rüping: Probabilistic Soft Logic to Improve Information Extraction in the Legal Domain. LWDA, 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. Frontiers in Education, 2020.

R. Fischer, M. Jakobs, S. Mücke, K. Morik: Solving Abstract Reasoning Tasks with Grammatical Evolution. LWDA, 2020.

K. Y. Chai, J. Stenzel, J. Jost: Generation, Classification and Segmentation of Point Clouds in Logistic Context with PointNet++ and DGCNN. IRCE, 2020.

C. Ojeda, R. Sanchez, K. Cvejoski, J. Schuecker, D. Biesner, C. Bauckhage, B. Georgiev: Stochastic-Path Examples Generation for Explanations. ICPR, 2020.

C. Ojeda, B. Georgiev, K. Cvejoski, J. Schuecker, C. Bauckhage, R. Sanchez: Switching Dynamical Systems with Deep Neural Networks. ICPR, 2020.

L. Hillebrand, D. Biesner, C. Bauckhage, R. Sifa: Interpretable Topic Extraction and Word Embedding Learning using row-stochastic DEDICOM. CD-MAKE, 2020.

C. Bauckhage, R. Sifa: Shells within Minimum Enclosing Balls. DSAA, 2020.

L. v. 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.

B. Sliwa, N. Piatkowski, C. Wietfeld: The Channel as a Traffic Sensor: Vehicle Detection and Classification based on Radio Fingerprinting. EEE Internet of Things Journal, 2020.

F. Seiffarth, T. Horvath, S. Wrobel: Maximum Margin Separations in Finite Closure Systems. ECML PKDD, 2020.

B. Sliwa, N. Piatkowski, C. Wietfeld: LIMITS: Lightweight Machine Learning for IoT Systems with Resource Limitations. ICC, 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.

P. Welke, F. Seiffarth, M. Kamp, S. Wrobel: HOPS: Probabilistic Subtree Mining for Small and Large Graphs. KDD, 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.

B. Georgiev, G. Angelov: On Learning a Control System without Continuous Feedback. ESANN, 2020.

B. Georgiev, L. Franken: Explorations in Quantum Neural Networks withIntermediate Measurements. ESANN, 2020.

K. Cvejoski, C. Ojeda, B. Georgiev, C. Bauckhage, R. J. Sanchez: Recurrent Point Review Models. IJCNN, 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.

C. Bauckhage, R. Sanchez, R.Sifa: Problem Solving with Hopfield Networks and Adiabatic Quantum Computing. IJCNN, 2020.

C. Bauckhage, R.Sifa, S. Wrobel: Adiabatic Quantum Computing for Max-Sum Diversication. SDM, 2020.

R. Sifa: DESICOM as Metaheuristic Search. LION, 2020.

M. Cekic, B. Georgiev, M. Mukherjee: Polyhedral billiards, eigenfunction concentration and almost periodic control. Communications in Mathematical Physics, 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. ICANN, 2020.

A. Kiwan, S. Gisselbach, S. Rüping: Incorporating Knowledge Bases into SciBERT and BioBERT pre-trainedlanguage models. SciNLP Workshop at AKBC, 2020.

L. v. Rueden, T. Wirtz, F. Hueger, J. D. Schneider, C. Bauckhage: Towards Map-Based Validation of Semantic Segmentation Masks. AIAD Workshop at ICML, 2020.

J. Kindermann, K. Beckh: Fusing Multi-label Classification and Semantic Tagging. LWDA, 2020.

S. Buschjäger, P.-J. Honysz, K. Morik: Randomized Outlier Detection with Trees. International Journal of Data Science and Analytics, 2020.

D. Antweiler, P. Welke: Temporal Graph Analysis for Outbreak Pattern Detection in COVID-19 Contact Tracing Networks. Machine Learning for Public Health Workshop at NeurIPS, 2020.

2019

N. Piatkowski: Distributed Generative Modelling with Sub-Linear Communication Overhead. DMLE, 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. WTC, 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.

M. Tavakol, S. Mair, K. Morik: HyperUCB: Hyperparameter Optimization using Contextual Bandits. ECML, 2019.

C. Bauckhage, R. Sifa, T. Dong: Prototypes within Minimum Enclosing Balls. ICANN, 2019.

C. Bauckhage, N. Piatkowski, R. Sifa, D. Hecker, S. Wrobel: A QUBO Formulation of the k-Medoids Problem. LWDA, 2019.

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.

T. Dong, Z. Wang, J. Li, C. Bauckhage, and A. B. Cremers: Triple Classification Using Regions and Fine-Grained Entity Typing. AAAI, 2019.

R. Fischer, N. Piatkowski, K. Morik: Parameter Sharing for Spatio-Temporal Process Models. LWDA, 2019.

V. Gupta, S. Giesselbach, S. Rüping, C. Bauckhage: Improving Word Embeddings Using Kernel PCA. RepL4NLP Workshop at ACL, 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, 2019.

S. Mücke, N. Piatkowski, K. Morik: Learning Bit by Bit: Extracting the Essence of Machine Learning. LWDA, 2019.

S. Mücke, N. Piatkowski, K. Morik: Hardware Accelerated Learning at the Edge. DMLE Workshop at ECML, 2019.

R. Ramamurthy, C. Bauckhage, R. Sifa, J. Schücker, S. Wrobel: Leveraging Domain Knowledge for Reinforcement Learning Using MMC Architectures. ICANN, 2019.

R. Sifa, R. Yawar, R. Ramarmurthy, C. Bauckhage, K. Kersting: Matrix- and Tensor Factorization for Game Content Recommendation. KI – Künstliche Intelligenz (online first), 2019.

P. Welke, T. Horvath, S. Wrobel: Probabilistic and Exact Frequent Subtree Mining in Graphs Beyond Forests. Machine Learning 108(7), 2019.

D. Trabold, T. Horvath, S. Wrobel: Effective Approximation of Parametrized Closure Systems over Transactional Data Streams. Machine Learning (online first), 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.

L. Pfahler, J. Schill, K. Morik: The Search for Equations – Learning to Identify Similarities between Mathematical Expressions. 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. Horvath, S. Wrobel: Maximal Closed Set and Half-Space Separations in Finite Closure Systems. ECML PKDD, 2019.

S. Kerner, J. Leveling , M. Otten, O. Urbann, M. Vogel, L. Weickhmann: Anwendungsfelder von künstlicher Intelligenz in Industrie 4.0 Systemen. Handbuch Industrie 4.0, 2019.

J. HaiLong, H. Lei, L. Juanzi, T. Dong: Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks. EMNLP, 2019.

2018

K. Morik: Daten – wem gehören sie, wer speichert sie, wer darf auf sie zugreifen? In: K. Morik & W. Kraemer, Hg. Daten – wem gehören sie, wer speichert sie, wer darf auf sie zugreifen?, 2018.

K. Morik, W. Kraemer (Hg.): Daten – wem gehören sie, wer speichert sie, wer darf auf sie zugreifen?, 2018.

R. Schiffers, K. Morik, A. Schulze Struchtrup, P.-J. Honysz, J. Wortberg: Anomaly detection in injection molding process data based on unsupervised learning. Journal of Plastics Technology, 2018.

L. Adilova, S. Giesselbach, S. Rüping: Making Efficient Use of a Domain Expert’s Time in Relation Extraction. arXiv:1807.04687 [cs.LG], 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.

S. Buschjäger, K. Morik: Decision Tree and Random Forest Implementations for Fast Filtering of Sensor Data. IEEE Trans. on Circuits and Systems 65-I(1), 2018.

M. Kamp, L. Adilova, J. Sicking, F. Hüger, P. Schlicht, T. Wirtz, S. Wrobel: Efficient Decentralized Deep Learning by Dynamic Model Averaging. arXiv:1807.03210 [cs.LG], 2018.

B. Kirsch, S. Giesselbach, D. Knodt, S. Rüping: Robust End-User-Driven Social Media Monitoring for Law Enforcement and Emergency Monitoring. In: G. Leventakis and M. Haberfeld (eds) Community-Oriented Policing and Technological Innovations, Springer, 2018.

K. Morik, C. Bockermann, S. Buschjäger: Big Data Science. KI 32(1), 2018.

S. Hess, N. Piatkowski, K. Morik: The Trustworthy Pal: Controlling the False Discovery Rate in Boolean Matrix Factorization. SIAM SDM, 2018.

R. Ramamurthy, C. Bauckhage, R. Sifa, S. Wrobel: Policy Learning Using SPSA. ICANN, 2018.

E. Schubert, S. Hess, K. Morik: The Relationship of DBSCAN to Matrix Factorization and Spectral Clustering. LWDA, 2018.

R. Sifa, D. Paurat, D. Trabold, C. Bauckhage: Simple Recurrent Neural Networks for Support Vector Machine Training. ICANN, 2018.

P. Welke, T. Horvath, S. Wrobel: Probabilistic frequent subtrees for efficient graph classification and retrieval. Machine Learning 107(11), 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. Hao, X. Ma, T. Dong, A. B. Cremers, C. Chun: An Assertion Framework for Mobile Robotic Programming with Spatial Reasoning. COMPSAC, 2018.

S. Giesselbach, K. Ullrich, M. Kamp, D. Paurat, T. Gärtner: Corresponding Projections for Orphan Screening. ML4H Workshop at NeurIPS, 2018.

C. Bauckhage, E. Brito, K. Cvejoski, C. Ojeda, R. Sifa, S. Wrobel: Ising Models for Binary Clustering via Adiabatic Quantum Computing. EMMCVPR, 2017.

S. Hess, K. Morik, N. Piatkowski: The PRIMPING routine – Tiling through proximal alternating linearized minimization. Data Mining and Knowledge Discovery 31(4), 2017.

T. Liebig, N. Piatkowski, C. Bockermann, K. Morik: Dynamic route planning with real-time traffic predictions. Information Systems 64, 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.

K. Ullrich, M. Kamp, T. Gärtner, M. Vogt, S. Wrobel: Co-Regularised Support Vector Regression. ECML PKDD, 2017.

2016

M. Neumann, R. Garnett, C. Bauckhage, K. Kersting: Propagation kernels: efficient graph kernels from propagated information. Machine Learning 102(2), 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. Neurocomputing 173, 2016.

C. Pölitz, W. Duivesteijn, K. Morik: Interpretable domain adaptation via optimization over the Stiefel manifold. Machine Learning 104(2-3), 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.

2021

2021

L. v. 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.

2020

2020

L. Pfahler, K. Morik: Semantic Search in Millions of Equations. KDD, 2020.

S. Buschjäger, L. Pfahler, J. Buss, K. Morik, W. Rhode: On-Site Gamma-Hadron Separation with Deep Learning on FPGAs. ECML PKDD, 2020.

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: Engineering Applications of Artificial Intelligence, Elsevier, 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. Transactions on Data Privacy, Vol. 13. April 2020, S. 61 – 66.

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.

R. Fischer, N. Piatkowski, C. Pelletier, G. Webb, F. Petitjean, K. Morik: No Cloud on the Horizon: Probabilistic Gap Filling in Satellite Image. DSAA, 2020.

A. Saadallah, K. Morik: Active Sampling for Learning Interpretable Surrogate Machine Learning Models. DSAA, 2020.

D. Weichert, K. Morik, M. Bunse, A. Kister: Optimal Probabilistic Classification in Active Class Selection. ICDM, 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. IEMTRONICS Workshop at IML, 2020.

B. Kirsch, S. Giesselbach, T. Schmude, S.Rüping: Probabilistic Soft Logic to Improve Information Extraction in the Legal Domain. LWDA, 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. Frontiers in Education, 2020.

R. Fischer, M. Jakobs, S. Mücke, K. Morik: Solving Abstract Reasoning Tasks with Grammatical Evolution. LWDA, 2020.

K. Y. Chai, J. Stenzel, J. Jost: Generation, Classification and Segmentation of Point Clouds in Logistic Context with PointNet++ and DGCNN. IRCE, 2020.

C. Ojeda, R. Sanchez, K. Cvejoski, J. Schuecker, D. Biesner, C. Bauckhage, B. Georgiev: Stochastic-Path Examples Generation for Explanations. ICPR, 2020.

C. Ojeda, B. Georgiev, K. Cvejoski, J. Schuecker, C. Bauckhage, R. Sanchez: Switching Dynamical Systems with Deep Neural Networks. ICPR, 2020.

L. Hillebrand, D. Biesner, C. Bauckhage, R. Sifa: Interpretable Topic Extraction and Word Embedding Learning using row-stochastic DEDICOM. CD-MAKE, 2020.

C. Bauckhage, R. Sifa: Shells within Minimum Enclosing Balls. DSAA, 2020.

L. v. 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.

B. Sliwa, N. Piatkowski, C. Wietfeld: The Channel as a Traffic Sensor: Vehicle Detection and Classification based on Radio Fingerprinting. EEE Internet of Things Journal, 2020.

F. Seiffarth, T. Horvath, S. Wrobel: Maximum Margin Separations in Finite Closure Systems. ECML PKDD, 2020.

B. Sliwa, N. Piatkowski, C. Wietfeld: LIMITS: Lightweight Machine Learning for IoT Systems with Resource Limitations. ICC, 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.

P. Welke, F. Seiffarth, M. Kamp, S. Wrobel: HOPS: Probabilistic Subtree Mining for Small and Large Graphs. KDD, 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.

B. Georgiev, G. Angelov: On Learning a Control System without Continuous Feedback. ESANN, 2020.

B. Georgiev, L. Franken: Explorations in Quantum Neural Networks withIntermediate Measurements. ESANN, 2020.

K. Cvejoski, C. Ojeda, B. Georgiev, C. Bauckhage, R. J. Sanchez: Recurrent Point Review Models. IJCNN, 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.

C. Bauckhage, R. Sanchez, R.Sifa: Problem Solving with Hopfield Networks and Adiabatic Quantum Computing. IJCNN, 2020.

C. Bauckhage, R.Sifa, S. Wrobel: Adiabatic Quantum Computing for Max-Sum Diversication. SDM, 2020.

R. Sifa: DESICOM as Metaheuristic Search. LION, 2020.

M. Cekic, B. Georgiev, M. Mukherjee: Polyhedral billiards, eigenfunction concentration and almost periodic control. Communications in Mathematical Physics, 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. ICANN, 2020.

A. Kiwan, S. Gisselbach, S. Rüping: Incorporating Knowledge Bases into SciBERT and BioBERT pre-trainedlanguage models. SciNLP Workshop at AKBC, 2020.

L. v. Rueden, T. Wirtz, F. Hueger, J. D. Schneider, C. Bauckhage: Towards Map-Based Validation of Semantic Segmentation Masks. AIAD Workshop at ICML, 2020.

J. Kindermann, K. Beckh: Fusing Multi-label Classification and Semantic Tagging. LWDA, 2020.

S. Buschjäger, P.-J. Honysz, K. Morik: Randomized Outlier Detection with Trees. International Journal of Data Science and Analytics, 2020.

D. Antweiler, P. Welke: Temporal Graph Analysis for Outbreak Pattern Detection in COVID-19 Contact Tracing Networks. Machine Learning for Public Health Workshop at NeurIPS, 2020.

2019

2019

N. Piatkowski: Distributed Generative Modelling with Sub-Linear Communication Overhead. DMLE, 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. WTC, 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.

M. Tavakol, S. Mair, K. Morik: HyperUCB: Hyperparameter Optimization using Contextual Bandits. ECML, 2019.

C. Bauckhage, R. Sifa, T. Dong: Prototypes within Minimum Enclosing Balls. ICANN, 2019.

C. Bauckhage, N. Piatkowski, R. Sifa, D. Hecker, S. Wrobel: A QUBO Formulation of the k-Medoids Problem. LWDA, 2019.

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.

T. Dong, Z. Wang, J. Li, C. Bauckhage, and A. B. Cremers: Triple Classification Using Regions and Fine-Grained Entity Typing. AAAI, 2019.

R. Fischer, N. Piatkowski, K. Morik: Parameter Sharing for Spatio-Temporal Process Models. LWDA, 2019.

V. Gupta, S. Giesselbach, S. Rüping, C. Bauckhage: Improving Word Embeddings Using Kernel PCA. RepL4NLP Workshop at ACL, 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, 2019.

S. Mücke, N. Piatkowski, K. Morik: Learning Bit by Bit: Extracting the Essence of Machine Learning. LWDA, 2019.

S. Mücke, N. Piatkowski, K. Morik: Hardware Accelerated Learning at the Edge. DMLE Workshop at ECML, 2019.

R. Ramamurthy, C. Bauckhage, R. Sifa, J. Schücker, S. Wrobel: Leveraging Domain Knowledge for Reinforcement Learning Using MMC Architectures. ICANN, 2019.

R. Sifa, R. Yawar, R. Ramarmurthy, C. Bauckhage, K. Kersting: Matrix- and Tensor Factorization for Game Content Recommendation. KI – Künstliche Intelligenz (online first), 2019.

P. Welke, T. Horvath, S. Wrobel: Probabilistic and Exact Frequent Subtree Mining in Graphs Beyond Forests. Machine Learning 108(7), 2019.

D. Trabold, T. Horvath, S. Wrobel: Effective Approximation of Parametrized Closure Systems over Transactional Data Streams. Machine Learning (online first), 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.

L. Pfahler, J. Schill, K. Morik: The Search for Equations – Learning to Identify Similarities between Mathematical Expressions. 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. Horvath, S. Wrobel: Maximal Closed Set and Half-Space Separations in Finite Closure Systems. ECML PKDD, 2019.

S. Kerner, J. Leveling , M. Otten, O. Urbann, M. Vogel, L. Weickhmann: Anwendungsfelder von künstlicher Intelligenz in Industrie 4.0 Systemen. Handbuch Industrie 4.0, 2019.

J. HaiLong, H. Lei, L. Juanzi, T. Dong: Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks. EMNLP, 2019.

2018

2018

K. Morik: Daten – wem gehören sie, wer speichert sie, wer darf auf sie zugreifen? In: K. Morik & W. Kraemer, Hg. Daten – wem gehören sie, wer speichert sie, wer darf auf sie zugreifen?, 2018.

K. Morik, W. Kraemer (Hg.): Daten – wem gehören sie, wer speichert sie, wer darf auf sie zugreifen?, 2018.

R. Schiffers, K. Morik, A. Schulze Struchtrup, P.-J. Honysz, J. Wortberg: Anomaly detection in injection molding process data based on unsupervised learning. Journal of Plastics Technology, 2018.

L. Adilova, S. Giesselbach, S. Rüping: Making Efficient Use of a Domain Expert’s Time in Relation Extraction. arXiv:1807.04687 [cs.LG], 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.

S. Buschjäger, K. Morik: Decision Tree and Random Forest Implementations for Fast Filtering of Sensor Data. IEEE Trans. on Circuits and Systems 65-I(1), 2018.

M. Kamp, L. Adilova, J. Sicking, F. Hüger, P. Schlicht, T. Wirtz, S. Wrobel: Efficient Decentralized Deep Learning by Dynamic Model Averaging. arXiv:1807.03210 [cs.LG], 2018.

B. Kirsch, S. Giesselbach, D. Knodt, S. Rüping: Robust End-User-Driven Social Media Monitoring for Law Enforcement and Emergency Monitoring. In: G. Leventakis and M. Haberfeld (eds) Community-Oriented Policing and Technological Innovations, Springer, 2018.

K. Morik, C. Bockermann, S. Buschjäger: Big Data Science. KI 32(1), 2018.

S. Hess, N. Piatkowski, K. Morik: The Trustworthy Pal: Controlling the False Discovery Rate in Boolean Matrix Factorization. SIAM SDM, 2018.

R. Ramamurthy, C. Bauckhage, R. Sifa, S. Wrobel: Policy Learning Using SPSA. ICANN, 2018.

E. Schubert, S. Hess, K. Morik: The Relationship of DBSCAN to Matrix Factorization and Spectral Clustering. LWDA, 2018.

R. Sifa, D. Paurat, D. Trabold, C. Bauckhage: Simple Recurrent Neural Networks for Support Vector Machine Training. ICANN, 2018.

P. Welke, T. Horvath, S. Wrobel: Probabilistic frequent subtrees for efficient graph classification and retrieval. Machine Learning 107(11), 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. Hao, X. Ma, T. Dong, A. B. Cremers, C. Chun: An Assertion Framework for Mobile Robotic Programming with Spatial Reasoning. COMPSAC, 2018.

S. Giesselbach, K. Ullrich, M. Kamp, D. Paurat, T. Gärtner: Corresponding Projections for Orphan Screening. ML4H Workshop at NeurIPS, 2018.

2017

C. Bauckhage, E. Brito, K. Cvejoski, C. Ojeda, R. Sifa, S. Wrobel: Ising Models for Binary Clustering via Adiabatic Quantum Computing. EMMCVPR, 2017.

S. Hess, K. Morik, N. Piatkowski: The PRIMPING routine – Tiling through proximal alternating linearized minimization. Data Mining and Knowledge Discovery 31(4), 2017.

T. Liebig, N. Piatkowski, C. Bockermann, K. Morik: Dynamic route planning with real-time traffic predictions. Information Systems 64, 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.

K. Ullrich, M. Kamp, T. Gärtner, M. Vogt, S. Wrobel: Co-Regularised Support Vector Regression. ECML PKDD, 2017.

2016

2016

M. Neumann, R. Garnett, C. Bauckhage, K. Kersting: Propagation kernels: efficient graph kernels from propagated information. Machine Learning 102(2), 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. Neurocomputing 173, 2016.

C. Pölitz, W. Duivesteijn, K. Morik: Interpretable domain adaptation via optimization over the Stiefel manifold. Machine Learning 104(2-3), 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.