Publikationen mit Bezug zum Kompetenzzentrum Maschinelles Lernen Rhein-Ruhr (ML2R)

2020

L. Pfahler, K. Morik: Semantic Search in Millions of Equations, Procs, 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. Series Session EnGeoData of the 7th IEEE International Conference on Data Science and Advanced Analytics, DSAA, 2020.

A. Saadallah, K. Morik: Active Sampling for Learning Interpretable Surrogate Machine Learning Models. Research track of the 7th IEEE International Conference on Data Science and Advanced Analytics, 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, Intelligent Data Analysis (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, ECMLPKDD, 2020.

P. Welke: Efficient Frequent Subgraph Mining in Transactional Databases, DSAA, 2020.

B. Sliwa, N. Piatkowski, C. Wietfeld: LIMITS: Lightweight Machine Learning for IoT Systems with Resource Limitations, ICC, 2020.

T. Dong: A Geometric Approach to The Unification of Symbolic Structures and Neural Networks, Springer, 2020.

K. Cvejoski, R. J. Sanchez, B. Georgiev, J.Schuecker, C. Bauckhage, C. Ojeda: Recurrent Point Processes for Dynamic Review Models, Interactive and Conversational Recommendation Systems (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, Compilers for Machine Learning (C4ML) Workshop, 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, 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, 14th Learning and Intelligent Optimization Conference (LION), 2020.

R. Sifa, C. Bauckhage: Novelty Discovery with Ensemble Kernel Minimum Enclosing Balls, 14th Learning and Intelligent Optimization Conference (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, Proc. International Conference on Artificial Neural Networks (ICANN), 2020.

R. Ramamurthy, R. Sifa, M. Lübbering, C. Bauckhage: Guided Reinforcement Learning via Sequence Learning, Proc. International Conference on Artificial Neural Networks (ICANN), 2020.

M. Lübbering, R. Ramamurthy, M. Gebauer, T. Bell, R. Sifa, C. Bauckhage: From Imbalanced Classification to Supervised Outlier Detection Problems, Proc. International Conference on Artificial Neural Networks (ICANN), 2020.

2019

N. Piatkowski: Distributed Generative Modelling with Sub-Linear Communication Overhead. Proc. DMLE, 2019

N. Piatkowski: Hyper-Parameter-Free Generative Modelling with Deep Boltzmann Trees. ECML, 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. Proc.  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. Proc. ECML, 2019

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

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

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

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

V. Gupta, S. Giesselbach, S. Rüping, C. Bauckhage: Improving Word Embeddings Using Kernel PCA. Proc. Workshop Representation Learning for NLP (RepL4NLP-2019) at ACL, 2019.

B. Kirsch, Z. Niyazova, S. Rüping, M. Mock: Noise Reduction in Distant Supervision for Relation Extraction using Probabilistic Soft Logic. Proc. Workshop Data Integration and Application (DINA) at ECML, 2019.

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

S. Mücke, N. Piatkowski, K. Morik: Hardware Accelerated Learning at the Edge. Proc. Workshop Decentralized Machine Learning at the Edge (DMLE) at ECML, 2019.

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

S. Buschjäger, T. Liebig, K. Morik: Gaussian Model Trees for Traffic Imputation. Regular paper. Procs. ICPRAM 2019.

A. Saadallah, N. Piatkowski, F. Finkeldey, P. Wiederkehr, K. Morik: Learning Ensembles in the Presence of Imbalanced Classes. Short paper. Procs. ICPRAM 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.

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, and 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, and S. Wrobel: Adiabatic Quantum Computing for Kernel k=2 Means Clustering. Proc. LWDA, 2018.

C. Bauckhage, C. Ojeda, J. Schücker, R. Sifa, and S. Wrobel: Informed Machine Learning Through Functional Composition. Proc. LWDA, 2018.

M. Bunse, N. Piatkowski, and K. Morik: Towards a Unifying View on Deconvolution in Cherenkov Astronomy. Proc. LWDA, 2018.

S. Buschjäger and 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, and S. Wrobel: Efficient Decentralized Deep Learning by Dynamic Model Averaging. arXiv:1807.03210 [cs.LG], 2018.

B. Kirsch, S. Giesselbach, D. Knodt, and 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, and S. Buschjäger: Big Data Science. KI 32(1), 2018.

S. Hess, N. Piatkowski, and K. Morik: The Trustworthy Pal: Controlling the False Discovery Rate in Boolean Matrix Factorization. Proc. SIAM Int. Conf. on Data Mining (SDM), 2018.

R. Ramamurthy, C. Bauckhage, R. Sifa, and S. Wrobel: Policy Learning Using SPSA. Proc. Int. Conf. on Artificial Neural Networks (ICANN), 2018.

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

R. Sifa, D. Paurat, D. Trabold, and C. Bauckhage: Simple Recurrent Neural Networks for Support Vector Machine Training. Proc. Int. Conf. on Artificial Neural Networks (ICANN), 2018.

P. Welke, T. Horvath, and S. Wrobel: Probabilistic frequent subtrees for efficient graph classification and retrieval. Machine Learning 107(11), 2018.

B. Wulff, J. Schücker, and C. Bauckhage: SPSA for Layer-Wise Training of Deep Networks. Proc. Int. Conf. on Artificial Neural Networks (ICANN), 2018.

2017

C. Bauckhage: A Neural Network Implementation of Frank-Wolfe Optimization. Proc. Int. Conf. on Artificial Neural Networks (ICANN),

C. Bauckhage, E. Brito, K. Cvejoski, C. Ojeda, R. Sifa, and S. Wrobel: Ising Models for Binary Clustering via Adiabatic Quantum Computing. Proc. Int. Conf. on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2017

S. Hess and K. Morik: C-SALT: Mining Class-Specific ALTerations in Boolean Matrix Factorization. Proc. Europ. Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD),

S. Hess, K. Morik, and 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, and V. Krech: Learning Low-Rank Document Embeddings with Weighted Nuclear Norm Regularization. Proc. IEEE Int. Conf. on Data Science and Advanced Analytics (DSAA), 2017.

R. Sifa and C. Bauckhage: Online k-Maxoids Clustering. Proc. IEEE Int. Conf. on Data Science and Advanced Analytics (DSAA), 2017.

K. Ullrich, M. Kamp, T. Gärtner, M. Vogt, and S. Wrobel: Co-Regularised Support Vector Regression. Proc. Europ. Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2017.

2016

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

N. Piatkowski and K. Morik: Stochastic Discrete Clenshaw-Curtis Quadrature. Proc. Int. Conf on Machine Learning (ICML), 2016.

N. Piatkowski, S. Lee, and K. Morik: Integer undirected graphical models for resource-constrained systems. Neurocomputing 173, 2016.

C. Pölitz, W. Duivesteijn, and 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, and C. Bauckhage: Predicting Retention in Sandbox Games with Tensor Factorization-based Representation Learning. Proc. IEEE Int Conf. on Computational Intelligence and Games (CIG), 2016.

2020

2020

L. Pfahler, K. Morik: Semantic Search in Millions of Equations, Procs, 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. Series Session EnGeoData of the 7th IEEE International Conference on Data Science and Advanced Analytics, DSAA, 2020.

A. Saadallah, K. Morik: Active Sampling for Learning Interpretable Surrogate Machine Learning Models. Research track of the 7th IEEE International Conference on Data Science and Advanced Analytics, 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, Intelligent Data Analysis (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, ECMLPKDD, 2020.

P. Welke: Efficient Frequent Subgraph Mining in Transactional Databases, DSAA, 2020.

B. Sliwa, N. Piatkowski, C. Wietfeld: LIMITS: Lightweight Machine Learning for IoT Systems with Resource Limitations, ICC, 2020.

T. Dong: A Geometric Approach to The Unification of Symbolic Structures and Neural Networks, Springer, 2020.

K. Cvejoski, R. J. Sanchez, B. Georgiev, J.Schuecker, C. Bauckhage, C. Ojeda: Recurrent Point Processes for Dynamic Review Models, Interactive and Conversational Recommendation Systems (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, Compilers for Machine Learning (C4ML) Workshop, 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, 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, 14th Learning and Intelligent Optimization Conference (LION), 2020.

R. Sifa, C. Bauckhage: Novelty Discovery with Ensemble Kernel Minimum Enclosing Balls, 14th Learning and Intelligent Optimization Conference (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, Proc. International Conference on Artificial Neural Networks (ICANN), 2020.

R. Ramamurthy, R. Sifa, M. Lübbering, C. Bauckhage: Guided Reinforcement Learning via Sequence Learning, Proc. International Conference on Artificial Neural Networks (ICANN), 2020.

M. Lübbering, R. Ramamurthy, M. Gebauer, T. Bell, R. Sifa, C. Bauckhage: From Imbalanced Classification to Supervised Outlier Detection Problems, Proc. International Conference on Artificial Neural Networks (ICANN), 2020.

2019

2019

N. Piatkowski: Distributed Generative Modelling with Sub-Linear Communication Overhead. Proc. DMLE, 2019

N. Piatkowski: Hyper-Parameter-Free Generative Modelling with Deep Boltzmann Trees. ECML, 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. Proc.  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. Proc. ECML, 2019

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

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

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

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

V. Gupta, S. Giesselbach, S. Rüping, C. Bauckhage: Improving Word Embeddings Using Kernel PCA. Proc. Workshop Representation Learning for NLP (RepL4NLP-2019) at ACL, 2019.

B. Kirsch, Z. Niyazova, S. Rüping, M. Mock: Noise Reduction in Distant Supervision for Relation Extraction using Probabilistic Soft Logic. Proc. Workshop Data Integration and Application (DINA) at ECML, 2019.

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

S. Mücke, N. Piatkowski, K. Morik: Hardware Accelerated Learning at the Edge. Proc. Workshop Decentralized Machine Learning at the Edge (DMLE) at ECML, 2019.

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

S. Buschjäger, T. Liebig, K. Morik: Gaussian Model Trees for Traffic Imputation. Regular paper. Procs. ICPRAM 2019.

A. Saadallah, N. Piatkowski, F. Finkeldey, P. Wiederkehr, K. Morik: Learning Ensembles in the Presence of Imbalanced Classes. Short paper. Procs. ICPRAM 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.

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, and 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, and S. Wrobel: Adiabatic Quantum Computing for Kernel k=2 Means Clustering. Proc. LWDA, 2018.

C. Bauckhage, C. Ojeda, J. Schücker, R. Sifa, and S. Wrobel: Informed Machine Learning Through Functional Composition. Proc. LWDA, 2018.

M. Bunse, N. Piatkowski, and K. Morik: Towards a Unifying View on Deconvolution in Cherenkov Astronomy. Proc. LWDA, 2018.

S. Buschjäger and 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, and S. Wrobel: Efficient Decentralized Deep Learning by Dynamic Model Averaging. arXiv:1807.03210 [cs.LG], 2018.

B. Kirsch, S. Giesselbach, D. Knodt, and 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, and S. Buschjäger: Big Data Science. KI 32(1), 2018.

S. Hess, N. Piatkowski, and K. Morik: The Trustworthy Pal: Controlling the False Discovery Rate in Boolean Matrix Factorization. Proc. SIAM Int. Conf. on Data Mining (SDM), 2018.

R. Ramamurthy, C. Bauckhage, R. Sifa, and S. Wrobel: Policy Learning Using SPSA. Proc. Int. Conf. on Artificial Neural Networks (ICANN), 2018.

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

R. Sifa, D. Paurat, D. Trabold, and C. Bauckhage: Simple Recurrent Neural Networks for Support Vector Machine Training. Proc. Int. Conf. on Artificial Neural Networks (ICANN), 2018.

P. Welke, T. Horvath, and S. Wrobel: Probabilistic frequent subtrees for efficient graph classification and retrieval. Machine Learning 107(11), 2018.

B. Wulff, J. Schücker, and C. Bauckhage: SPSA for Layer-Wise Training of Deep Networks. Proc. Int. Conf. on Artificial Neural Networks (ICANN), 2018.

2017

2017

C. Bauckhage: A Neural Network Implementation of Frank-Wolfe Optimization. Proc. Int. Conf. on Artificial Neural Networks (ICANN),

C. Bauckhage, E. Brito, K. Cvejoski, C. Ojeda, R. Sifa, and S. Wrobel: Ising Models for Binary Clustering via Adiabatic Quantum Computing. Proc. Int. Conf. on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2017

S. Hess and K. Morik: C-SALT: Mining Class-Specific ALTerations in Boolean Matrix Factorization. Proc. Europ. Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD),

S. Hess, K. Morik, and 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, and V. Krech: Learning Low-Rank Document Embeddings with Weighted Nuclear Norm Regularization. Proc. IEEE Int. Conf. on Data Science and Advanced Analytics (DSAA), 2017.

R. Sifa and C. Bauckhage: Online k-Maxoids Clustering. Proc. IEEE Int. Conf. on Data Science and Advanced Analytics (DSAA), 2017.

K. Ullrich, M. Kamp, T. Gärtner, M. Vogt, and S. Wrobel: Co-Regularised Support Vector Regression. Proc. Europ. Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2017.

2016

2016

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

N. Piatkowski and K. Morik: Stochastic Discrete Clenshaw-Curtis Quadrature. Proc. Int. Conf on Machine Learning (ICML), 2016.

N. Piatkowski, S. Lee, and K. Morik: Integer undirected graphical models for resource-constrained systems. Neurocomputing 173, 2016.

C. Pölitz, W. Duivesteijn, and 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, and C. Bauckhage: Predicting Retention in Sandbox Games with Tensor Factorization-based Representation Learning. Proc. IEEE Int Conf. on Computational Intelligence and Games (CIG), 2016.