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ORCIDBastian Rieck
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- affiliation: Helmholtz Munich, Germany
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2020 – today
- 2023
[i34]Joshua Southern, Jeremy Wayland, Michael M. Bronstein, Bastian Rieck:
Curvature Filtrations for Graph Generative Model Evaluation. CoRR abs/2301.12906 (2023)- [i33]Bastian Rieck:
On the Expressivity of Persistent Homology in Graph Learning. CoRR abs/2302.09826 (2023) - [i32]Kalyan Varma Nadimpalli, Amit Chattopadhyay, Bastian Rieck:
Euler Characteristic Transform Based Topological Loss for Reconstructing 3D Images from Single 2D Slices. CoRR abs/2303.05286 (2023) - 2022
- [c20]Max Horn, Edward De Brouwer, Michael Moor, Yves Moreau, Bastian Rieck
, Karsten M. Borgwardt:
Topological Graph Neural Networks. ICLR 2022 - [c19]Leslie O'Bray, Max Horn, Bastian Rieck, Karsten M. Borgwardt:
Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions. ICLR 2022 - [c18]Stefan Horoi, Jessie Huang, Bastian Rieck, Guillaume Lajoie, Guy Wolf, Smita Krishnaswamy:
Exploring the Geometry and Topology of Neural Network Loss Landscapes. IDA 2022: 171-184 - [c17]Renming Liu, Semih Cantürk, Frederik Wenkel, Sarah McGuire, Xinyi Wang, Anna Little, Leslie O'Bray, Michael Perlmutter, Bastian Rieck, Matthew J. Hirn, Guy Wolf, Ladislav Rampásek:
Taxonomy of Benchmarks in Graph Representation Learning. LoG 2022: 6 - [c16]Bastian Rieck, Razvan Pascanu, Yuanqi Du, Hannes Stärk, Derek Lim, Chaitanya K. Joshi, Andreea Deac, Iulia Duta, Joshua Robinson, Gabriele Corso, Leonardo Cotta, Yanqiao Zhu, Kexin Huang, Michelle M. Li, Sofia Bourhim, Ilia Igashov:
The First Learning on Graphs Conference: Preface. LoG 2022: i-xxiii - [c15]Dominik Jens Elias Waibel, Scott Atwell, Matthias Meier, Carsten Marr, Bastian Rieck:
Capturing Shape Information with Multi-scale Topological Loss Terms for 3D Reconstruction. MICCAI (4) 2022: 150-159 - [e1]Bastian Rieck, Razvan Pascanu:
Learning on Graphs Conference, LoG 2022, 9-12 December 2022, Virtual Event. Proceedings of Machine Learning Research 198, PMLR 2022 [contents] - [i31]Dominik Jens Elias Waibel, Scott Atwell, Matthias Meier, Carsten Marr, Bastian Rieck:
Capturing Shape Information with Multi-Scale Topological Loss Terms for 3D Reconstruction. CoRR abs/2203.01703 (2022) - [i30]Guillaume Huguet, Alexander Tong, Bastian Rieck, Jessie Huang, Manik Kuchroo, Matthew J. Hirn, Guy Wolf, Smita Krishnaswamy:
Time-inhomogeneous diffusion geometry and topology. CoRR abs/2203.14860 (2022) - [i29]Dhananjay Bhaskar, Kincaid MacDonald, Oluwadamilola Fasina, Dawson Thomas, Bastian Rieck, Ian Adelstein, Smita Krishnaswamy:
Diffusion Curvature for Estimating Local Curvature in High Dimensional Data. CoRR abs/2206.03977 (2022) - [i28]Renming Liu, Semih Cantürk, Frederik Wenkel, Dylan Sandfelder, Devin Kreuzer, Anna Little, Sarah McGuire, Leslie O'Bray, Michael Perlmutter, Bastian Rieck, Matthew J. Hirn, Guy Wolf, Ladislav Rampásek:
Taxonomy of Benchmarks in Graph Representation Learning. CoRR abs/2206.07729 (2022) - [i27]Corinna Coupette, Jilles Vreeken, Bastian Rieck:
All the World's a (Hyper)Graph: A Data Drama. CoRR abs/2206.08225 (2022) - [i26]Celia Hacker, Bastian Rieck:
On the Surprising Behaviour of node2vec. CoRR abs/2206.08252 (2022) - [i25]Dominik Jens Elias Waibel, Ernst Röell, Bastian Rieck, Raja Giryes, Carsten Marr:
A Diffusion Model Predicts 3D Shapes from 2D Microscopy Images. CoRR abs/2208.14125 (2022) - [i24]Julius von Rohrscheidt, Bastian Rieck:
TOAST: Topological Algorithm for Singularity Tracking. CoRR abs/2210.00069 (2022) - [i23]Corinna Coupette, Sebastian Dalleiger, Bastian Rieck:
Ollivier-Ricci Curvature for Hypergraphs: A Unified Framework. CoRR abs/2210.12048 (2022) - 2021
- [j13]Anja C. Gumpinger, Bastian Rieck, Dominik G. Grimm, Karsten M. Borgwardt:
Network-guided search for genetic heterogeneity between gene pairs. Bioinform. 37(1): 57-65 (2021) - [j12]Felix Hensel, Michael Moor, Bastian Rieck:
A Survey of Topological Machine Learning Methods. Frontiers Artif. Intell. 4: 681108 (2021) - [j11]Robin Vandaele, Bastian Rieck, Yvan Saeys, Tijl De Bie:
Stable topological signatures for metric trees through graph approximations. Pattern Recognit. Lett. 147: 85-92 (2021) - [c14]Leslie O'Bray, Bastian Rieck, Karsten M. Borgwardt:
Filtration Curves for Graph Representation. KDD 2021: 1267-1275 - [c13]Sarah C. Brüningk, Felix Hensel, Louis P. Lukas, Merel Kuijs, Catherine R. Jutzeler, Bastian Rieck:
Back to the basics with inclusion of clinical domain knowledge - A simple, scalable and effective model of Alzheimer's Disease classification. MLHC 2021: 730-754 - [c12]Malte Lücken, Daniel Burkhardt, Robrecht Cannoodt, Christopher Lance, Aditi Agrawal, Hananeh Aliee, Ann Chen, Louise Deconinck, Angela Detweiler, Alejandro Granados, Shelly Huynh, Laura Isacco, Yang Kim, Dominik Klein, Bony de Kumar, Sunil Kuppasani, Heiko Lickert, Aaron McGeever, Joaquin Melgarejo, Honey Mekonen, Maurizio Morri, Michaela Müller, Norma Neff, Sheryl Paul, Bastian Rieck, Kaylie Schneider, Scott Steelman, Michael Sterr, Daniel Treacy, Alexander Tong, Alexandra-Chloé Villani, Guilin Wang, Jia Yan, Ce Zhang, Angela Pisco, Smita Krishnaswamy, Fabian J. Theis, Jonathan M. Bloom:
A sandbox for prediction and integration of DNA, RNA, and proteins in single cells. NeurIPS Datasets and Benchmarks 2021 - [i22]Max Horn, Edward De Brouwer, Michael Moor, Yves Moreau, Bastian Rieck, Karsten M. Borgwardt:
Topological Graph Neural Networks. CoRR abs/2102.07835 (2021) - [i21]Bastian Rieck:
Basic Analysis of Bin-Packing Heuristics. CoRR abs/2104.12235 (2021) - [i20]Leslie O'Bray, Max Horn, Bastian Rieck, Karsten M. Borgwardt:
Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions. CoRR abs/2106.01098 (2021) - [i19]Michael Moor, Nicolas Bennett, Drago Plecko, Max Horn, Bastian Rieck, Nicolai Meinshausen, Peter Bühlmann, Karsten M. Borgwardt:
Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning. CoRR abs/2107.05230 (2021) - [i18]Renming Liu, Semih Cantürk, Frederik Wenkel, Dylan Sandfelder, Devin Kreuzer, Anna Little, Sarah McGuire, Leslie O'Bray, Michael Perlmutter, Bastian Rieck, Matthew J. Hirn, Guy Wolf, Ladislav Rampásek:
Towards a Taxonomy of Graph Learning Datasets. CoRR abs/2110.14809 (2021) - [i17]Michael F. Adamer, Leslie O'Bray, Edward De Brouwer, Bastian Rieck, Karsten M. Borgwardt:
The magnitude vector of images. CoRR abs/2110.15188 (2021) - [i16]Merel Kuijs, Catherine R. Jutzeler, Bastian Rieck, Sarah C. Brüningk:
Interpretability Aware Model Training to Improve Robustness against Out-of-Distribution Magnetic Resonance Images in Alzheimer's Disease Classification. CoRR abs/2111.08701 (2021) - [i15]Christopher Morris, Yaron Lipman, Haggai Maron, Bastian Rieck, Nils M. Kriege, Martin Grohe, Matthias Fey, Karsten M. Borgwardt:
Weisfeiler and Leman go Machine Learning: The Story so far. CoRR abs/2112.09992 (2021) - 2020
- [j10]Caroline Weis, Max Horn, Bastian Rieck, Aline Cuénod, Adrian Egli, Karsten M. Borgwardt:
Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra. Bioinform. 36(Supplement-1): i30-i38 (2020) - [j9]Karsten M. Borgwardt, M. Elisabetta Ghisu, Felipe Llinares-López, Leslie O'Bray, Bastian Rieck:
Graph Kernels: State-of-the-Art and Future Challenges. Found. Trends Mach. Learn. 13(5-6) (2020) - [c11]Christoph D. Hofer, Florian Graf, Bastian Rieck, Marc Niethammer, Roland Kwitt:
Graph Filtration Learning. ICML 2020: 4314-4323 - [c10]Max Horn, Michael Moor, Christian Bock, Bastian Rieck, Karsten M. Borgwardt:
Set Functions for Time Series. ICML 2020: 4353-4363 - [c9]Michael Moor, Max Horn, Bastian Rieck, Karsten M. Borgwardt:
Topological Autoencoders. ICML 2020: 7045-7054 - [c8]Bastian Rieck, Tristan Yates, Christian Bock, Karsten M. Borgwardt, Guy Wolf, Nicholas B. Turk-Browne, Smita Krishnaswamy:
Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence. NeurIPS 2020 - [i14]Michael Moor, Max Horn, Christian Bock, Karsten M. Borgwardt, Bastian Rieck:
Path Imputation Strategies for Signature Models. CoRR abs/2005.12359 (2020) - [i13]Bastian Rieck, Tristan Yates, Christian Bock, Karsten M. Borgwardt, Guy Wolf, Nicholas B. Turk-Browne, Smita Krishnaswamy:
Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence. CoRR abs/2006.07882 (2020) - [i12]Jannis Born, Nina Wiedemann, Gabriel Brändle, Charlotte Buhre, Bastian Rieck, Karsten M. Borgwardt:
Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis. CoRR abs/2009.06116 (2020) - [i11]Karsten M. Borgwardt, M. Elisabetta Ghisu, Felipe Llinares-López, Leslie O'Bray, Bastian Rieck:
Graph Kernels: State-of-the-Art and Future Challenges. CoRR abs/2011.03854 (2020) - [i10]Sarah C. Brüningk, Felix Hensel, Catherine R. Jutzeler, Bastian Rieck:
Image analysis for Alzheimer's disease prediction: Embracing pathological hallmarks for model architecture design. CoRR abs/2011.06531 (2020) - [i9]Stefan Groha, Caroline Weis, Alexander Gusev, Bastian Rieck:
Topological Data Analysis of copy number alterations in cancer. CoRR abs/2011.11070 (2020)
2010 – 2019
- 2019
- [j8]Boyan Zheng, Bastian Rieck, Heike Leitte, Filip Sadlo:
Visualization of Equivalence in 2D Bivariate Fields. Comput. Graph. Forum 38(3): 311-323 (2019) - [c7]Christian Bock, Matteo Togninalli, M. Elisabetta Ghisu, Thomas Gumbsch, Bastian Rieck, Karsten M. Borgwardt:
A Wasserstein Subsequence Kernel for Time Series. ICDM 2019: 964-969 - [c6]Bastian Rieck, Matteo Togninalli, Christian Bock, Michael Moor, Max Horn, Thomas Gumbsch, Karsten M. Borgwardt:
Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology. ICLR (Poster) 2019 - [c5]Bastian Rieck, Christian Bock, Karsten M. Borgwardt:
A Persistent Weisfeiler-Lehman Procedure for Graph Classification. ICML 2019: 5448-5458 - [c4]Michael Moor, Max Horn, Bastian Rieck, Damian Roqueiro, Karsten M. Borgwardt:
Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping. MLHC 2019: 2-26 - [c3]Matteo Togninalli, M. Elisabetta Ghisu, Felipe Llinares-López, Bastian Rieck, Karsten M. Borgwardt:
Wasserstein Weisfeiler-Lehman Graph Kernels. NeurIPS 2019: 6436-6446 - [i8]Michael Moor, Max Horn, Bastian Rieck, Damian Roqueiro, Karsten M. Borgwardt:
Temporal Convolutional Networks and Dynamic Time Warping can Drastically Improve the Early Prediction of Sepsis. CoRR abs/1902.01659 (2019) - [i7]Stephanie L. Hyland, Martin Faltys, Matthias Hüser, Xinrui Lyu, Thomas Gumbsch, Cristóbal Esteban, Christian Bock, Max Horn, Michael Moor, Bastian Rieck, Marc Zimmermann, Dean A. Bodenham, Karsten M. Borgwardt, Gunnar Rätsch, Tobias M. Merz:
Machine learning for early prediction of circulatory failure in the intensive care unit. CoRR abs/1904.07990 (2019) - [i6]Michael Moor, Max Horn, Bastian Rieck, Karsten M. Borgwardt:
Topological Autoencoders. CoRR abs/1906.00722 (2019) - [i5]Matteo Togninalli, M. Elisabetta Ghisu, Felipe Llinares-López, Bastian Rieck, Karsten M. Borgwardt:
Wasserstein Weisfeiler-Lehman Graph Kernels. CoRR abs/1906.01277 (2019) - [i4]Bastian Rieck, Markus Banagl, Filip Sadlo, Heike Leitte:
Persistent Intersection Homology for the Analysis of Discrete Data. CoRR abs/1907.13485 (2019) - [i3]Bastian Rieck, Filip Sadlo, Heike Leitte:
Topological Machine Learning with Persistence Indicator Functions. CoRR abs/1907.13496 (2019) - [i2]Max Horn, Michael Moor, Christian Bock, Bastian Rieck, Karsten M. Borgwardt:
Set Functions for Time Series. CoRR abs/1909.12064 (2019) - 2018
- [j7]Christian Bock, Thomas Gumbsch, Michael Moor, Bastian Rieck, Damian Roqueiro, Karsten M. Borgwardt:
Association mapping in biomedical time series via statistically significant shapelet mining. Bioinform. 34(13): i438-i446 (2018) - [j6]Lutz Hofmann, Bastian Rieck, Filip Sadlo:
Visualization of 4D Vector Field Topology. Comput. Graph. Forum 37(3): 301-313 (2018) - [j5]Bastian Rieck, Ulderico Fugacci, Jonas Lukasczyk, Heike Leitte:
Clique Community Persistence: A Topological Visual Analysis Approach for Complex Networks. IEEE Trans. Vis. Comput. Graph. 24(1): 822-831 (2018) - [c2]Kai Sdeo, Bastian Rieck, Filip Sadlo:
Visualization of Fullerene Fragmentation. PacificVis 2018: 111-115 - [c1]Karsten Hanser, Ole Klein, Bastian Rieck, Bettina Wiebe, Tobias Selz, Marian Piatkowski, Antoni Sagristà, Boyan Zheng, Mária Lukácová-Medvid'ová, George Craig, Heike Leitte, Filip Sadlo:
Visualization of Parameter Sensitivity of 2D Time-Dependent Flow. ISVC 2018: 359-370 - [i1]Bastian Rieck, Matteo Togninalli, Christian Bock, Michael Moor, Max Horn, Thomas Gumbsch, Karsten M. Borgwardt:
Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology. CoRR abs/1812.09764 (2018) - 2017
- [b1]Bastian Rieck:
Persistent homology in multivariate data visualization. University of Heidelberg, Germany, 2017, pp. 1-307 - 2016
- [j4]Bastian Rieck, Heike Leitte:
Exploring and Comparing Clusterings of Multivariate Data Sets Using Persistent Homology. Comput. Graph. Forum 35(3): 81-90 (2016) - [p1]Jens Fangerau, Burkhard Höckendorf, Bastian Rieck, Christian Heine, Joachim Wittbrodt, Heike Leitte:
Interactive Similarity Analysis and Error Detection in Large Tree Collections. Visualization in Medicine and Life Sciences III 2016: 287-307 - 2015
- [j3]Bastian Rieck, Heike Leitte:
Persistent Homology for the Evaluation of Dimensionality Reduction Schemes. Comput. Graph. Forum 34(3): 431-440 (2015) - 2014
- [j2]Bastian Rieck, Heike Leitte:
Structural Analysis of Multivariate Point Clouds Using Simplicial Chains. Comput. Graph. Forum 33(8): 28-37 (2014) - 2012
- [j1]Bastian Rieck, Hubert Mara, Heike Leitte:
Multivariate Data Analysis Using Persistence-Based Filtering and Topological Signatures. IEEE Trans. Vis. Comput. Graph. 18(12): 2382-2391 (2012)
Coauthor Index
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last updated on 2023-03-26 01:26 CET by the dblp team
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