News
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08.10.2020 Keynote talk at ICPM 2020 Data Mining on Process Data T. Seidl |
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24.09.2020 Accepted paper at ICDM 2020 Workshop HDM'20 I fold you so! An internal evaluation measure for arbitrary oriented subspace clustering D. Kazempour, A. Beer, P. Kröger, T. Seidl |
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24.09.2020 Accepted paper at ICDM 2020 Workshop HDM'20 You see a set of wagons - I see one train: Towards a unified view of local and global arbitrarily oriented subspace clusters D. Kazempour, L. M. Yan, P. Kröger, T. Seidl |
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24.09.2020 Accepted paper at ICDM 2020 Workshop HDM'20 Towards an Internal Evaluation Measure for Arbitrarily Oriented Subspace Clustering D. Kazempour, P. Kröger, T. Seidl |
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24.09.2020 Accepted paper at ICPM 2020 Workshop SA4PM 2020 Performance Skyline: Inferring Process Performance Models from Interval Events A. Maldonado, J. Sontheim, F. Richter, T. Seidl |
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11.09.2020 Moritz Herrmann receives the Bernd-Streitberg Award of the German Biometric Society for his master thesis 'Large-scale benchmark study of prediction methods using multi-omics data' supervised by our PI Prof. Dr. Anne-Laure Boulesteix. In the context of MCML an article has been published in the renowned Journal 'Briefings in Bioinformatics' as an extension of his master thesis: Open Access |
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13.08.2020 Accepted paper at SISAP 2020 Angle-Based Clustering A. Beer, D. Seeholzer, N. Schüler, T. Seidl |
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30.07.2020 Accepted paper at LWDA 2020 Grace - Limiting the Number of Grid Cells for Clustering High-Dimensional Data A. Beer, D. Kazempour, J. Busch, A. Tekles, T. Seidl |
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29.07.2020 Virtual workshop on 29.07.2020 with over 20 presentations by our PhD students on current research topics. |
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02.07.2020 Announcement PyKEEN 1.0 As a member of the PyKEEN community project, we are happy to announce PyKEEN 1.0 – PyKEEN is a software package to train and evaluate knowledge graph embedding models. |
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01.06.2020 18 papers accepted at CVPR The PIs of TU Munich published 18 papers at CVF/IEEE Conference on Computer Vision and Pattern Recognition (CVPR) this year, the conference with the currently highest impact among all scientific conferences worldwide. |
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28.05.2020 Accepted short vision paper at SSDBM 2020 Orderings of Data - more than a Tripping Hazard A. Beer, V. Hartmann, T. Seidl |
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06.02.2020 Accepted paper at PAKDD 2020 Detecting Arbitrarily Oriented Subspace Clusters in Data Streams Using Hough Transform F. Borutta, D. Kazempour, F. Marty, P. Kröger, T. Seidl |
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20.12.2019 MCML Upgrade We are happy to announce that the MCML upgrade proposal is now accepted by BMBF. |
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19.12.2019 Accepted paper at ECIR 2020 reproducibility track Knowledge Graph Entity Alignment with Graph Neural Networks: Lessons Learned M. Berrendorf, E. Faehrman, V. Melnychuk, V. Tresp, T. Seidl |
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14.11.2019 3rd Place at ACM SIGSPATIAL GisCup 2019 goes to F. Borutta, S. Schmoll, S. Friedl for their contribution 'Optimizing the Spatio-Temporal Resource Search Problem with Reinforcement Learning' |
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10.10.2019 Accepted paper at NeurIPS Workshop 2019 Graph Alignment Networks with Node Matching Scores E. Faerman, O. Voggenreiter, F. Borutta, T. Emrich, M. Berrendorf, M. Schubert |
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12.09.2019 Accepted paper at ICDM Workshop DeepSpatial 2019 Spatial Interpolation with Message Passing Framework E. Faerman, M. Rogalla, N. Strauß, A. Krüger, B. Blümel, M. Berrendorf, M. Fromm, M.Schubert |
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12.09.2019 Accepted journal paper at Datenbank-Spektrum 19 (2019) Chain-detection Between Clusters J. Held, A. Beer, T. Seidl |
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22.08.2019 Accepted paper at ACM SIGSPATIAL 2019 Optimizing the Spatio-Temporal Resource Search Problem with Reinforcement Learning F. Borutta, S. Schmoll, S. Friedl |
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25.07.2019 Accepted paper at LWDA 2019 A Generator for Subspace Clusters A. Beer, N. S. Schüler, T. Seidl |
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25.07.2019 Accepted paper at WI 2019 Structural Graph Representations based on Multiscale Local Network Topologies F. Borutta, J. Busch, E. Faerman, A. Klink, M. Schubert |
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25.07.2019 Accepted paper at LWDA 2019 Clustering Trend Data Time-Series through Segmentation of FFT-decomposed Signal Constituents D. Kazempour, A. Beer, O. Schrüfer, T. Seidl |
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25.07.2019 Accepted paper at LWDA 2019 CODEC - Detecting Linear Correlations in Dense Clusters with Comedian-based PCA D. Kazempour, M. Hunemörder, A. Beer, and T. Seidl |
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25.07.2019 Accepted paper at LWDA 2019 From Covariance to Comode in context of Principal Component Analysis D. Kazempour, L. M. Yan, T. Seidl |
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11.07.2019 Accepted paper at SISAP 2019 MORe++: k-Means Based Outlier Removal on High-Dimensional Data A. Beer, J. Lauterbach, T. Seidl |
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11.07.2019 Accepted paper at SISAP 2019 k-Distance Approximation for Memory-Efficient RkNN Retrieval M. Berrendorf, F. Borutta, P. Kröger |
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11.07.2019 Accepted paper at SISAP 2019 A Generic Summary Structure for Arbitrarily Oriented Subspace Clustering in Data Streams F. Borutta, P. Kröger, T. Hubauer |
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11.07.2019 Accepted paper at SISAP 2019 On coMADs and Principal Component Analysis D. Kazempour, M. Hünemörder, T. Seidl |
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11.07.2019 Accepted paper at SISAP 2019 On Co-medians and Principal Component Analysis D. Kazempour, T. Seidl |
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16.05.2019 Accepted short paper at SSDBM 2019 Graph Ordering and Clustering - A Circular Approach A. Beer, T. Seidl |
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16.05.2019 Accepted short paper at SSDBM 2019 LUCK - Linear Correlation Clustering Using Cluster Algorithms and a kNN based Distance Function A. Beer, D. Kazempour, L. Stephan, T. Seidl |
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16.05.2019 On systematic hyperparameter analysis through the example of subspace clustering Graph Ordering and Clustering - A Circular Approach D. Kazempour, T. Seidl |
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16.05.2019 Accepted short paper at SSDBM 2019 Detecting Global Periodic Correlated Clusters in Event Series based on Parameter Space Transform D. Kazempour, K. Emmerig, P. Kröger, and T. Seidl |
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29.03.2019 Accepted paper at ICDM Workshop DeepSpatial 2019 XD-STOD: Cross-Domain Superresolution for Tiny Object Detection M. Fromm, M. Berrendorf, E. Faerman, Y. Chen, B. Schüss, M. Schubert |
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12.03.2019 Accepted poster at HCII 2019 Human Learning in Data Science A. Beer, D. Kazempour, M. Baur, T. Seidl |
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12.03.2019 Accepted poster at HCII 2019 Data on RAILs: On interactive generation of artificial linear correlated data D. Kazempour, A. Beer, and T. Seidl |
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27.01.2019 LMU Munich Magazine 'Einsichten' Smarte Wesen: Hat das mit Intelligenz zu tun? Der Informatiker Thomas Seidl und der Statistiker Bernd Bischl debattieren über Maschinelles Lernen, andere Formen von KI und darüber, warum es wichtig ist zu verstehen, wie Rechner damit zu ihren Ergebnissen kommen. |
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17.01.2019 Press release LMU Munich Neues KI-Projekt geht an den Start |
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17.01.2019 Press release LMU Munich Munich Center for Machine Learning geht an den Start |
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15.01.2019 Accepted demo paper at BTW 2019 DICE: Density-based Interactive Clustering and Exploration D. Kazempour, M. Kazakov, P. Kröger, T. Seidl. |
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08.01.2019 Accepted short paper at EDBT 2019 Rock - Let the points roam to their clusters themselvese A. Beer, D. Kazempour, T. Seidl |
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08.01.2019 Accepted short paper at EDBT 2019 A Galaxy of Correlations - Detecting Linear Correlated Clusters through k-Tuples Sampling using Parameter Space Transform D. Kazempour, L. Krombholz, P. Kröger, T. Seidl |
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08.01.2019 Accepted short paper at EDBT 2019 Insights into a running clockwork: On interactive process-aware clustering D. Kazempour, T. Seidl |
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11.12.2018 Accepted poster at BTW 2019 Chain-detection for DBSCAN J. Held, A. Beer, T. Seidl |