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ORCIDBastian Rieck
Person information
- affiliation: University of Fribourg, Switzerland
- affiliation (former): Helmholtz Munich, Germany
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Books and Theses
- 2017
[b1]Bastian Rieck:
Persistent homology in multivariate data visualization. University of Heidelberg, Germany, 2017, pp. 1-307
Journal Articles
- 2024
- [j19]Michael F. Adamer
, Edward De Brouwer, Leslie O'Bray, Bastian Rieck:
The magnitude vector of images. J. Appl. Comput. Topol. 8(3): 447-473 (2024) - [j18]Corinna Coupette, Jilles Vreeken, Bastian Rieck:
All the world's a (hyper)graph: A data drama. Digit. Scholarsh. Humanit. 39(1): 74-96 (2024) - 2023
- [j17]Daisuke Yoneoka, Bastian Rieck:
A Note on Cherry-Picking in Meta-Analyses. Entropy 25(4): 691 (2023) - [j16]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. J. Mach. Learn. Res. 24: 333:1-333:59 (2023) - [j15]Guillaume Huguet, Alexander Tong, Bastian Rieck, Jessie Huang, Manik Kuchroo, Matthew J. Hirn, Guy Wolf, Smita Krishnaswamy:
Time-Inhomogeneous Diffusion Geometry and Topology. SIAM J. Math. Data Sci. 5(2): 346-372 (2023) - 2021
- [j14]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) - [j13]Felix Hensel, Michael Moor, Bastian Rieck:
A Survey of Topological Machine Learning Methods. Frontiers Artif. Intell. 4: 681108 (2021) - [j12]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) - 2020
- [j11]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) - [j10]Thomas Gumbsch, Christian Bock, Michael Moor, Bastian Rieck, Karsten M. Borgwardt:
Enhancing statistical power in temporal biomarker discovery through representative shapelet mining. Bioinform. 36(Supplement-2): i840-i848 (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) - 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) - 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) - 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) - 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)
Conference and Workshop Papers
- 2024
- [c37]Kelly Maggs, Celia Hacker, Bastian Rieck:
Simplicial Representation Learning with Neural k-Forms. ICLR 2024 - [c36]Ernst Röell, Bastian Rieck:
Differentiable Euler Characteristic Transforms for Shape Classification. ICLR 2024 - [c35]Theodore Papamarkou, Tolga Birdal, Michael M. Bronstein, Gunnar E. Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Lio, Paolo Di Lorenzo, Vasileios Maroulas, Nina Miolane, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck, Simone Scardapane, Michael T. Schaub, Petar Velickovic, Bei Wang, Yusu Wang, Guo-Wei Wei, Ghada Zamzmi:
Position: Topological Deep Learning is the New Frontier for Relational Learning. ICML 2024 - [c34]Jeremy Wayland, Corinna Coupette, Bastian Rieck:
Mapping the Multiverse of Latent Representations. ICML 2024 - [c33]Katharina Limbeck, Rayna Andreeva, Rik Sarkar, Bastian Rieck:
Metric Space Magnitude for Evaluating the Diversity of Latent Representations. NeurIPS 2024 - 2023
- [c32]Kalyan Varma Nadimpalli, Amit Chattopadhyay, Bastian Rieck:
Euler Characteristic Transform Based Topological Loss for Reconstructing 3D Images from Single 2D Slices. CVPR Workshops 2023: 571-579 - [c31]Corinna Coupette, Sebastian Dalleiger, Bastian Rieck:
Ollivier-Ricci Curvature for Hypergraphs: A Unified Framework. ICLR 2023 - [c30]Julius von Rohrscheidt, Bastian Rieck:
Topological Singularity Detection at Multiple Scales. ICML 2023: 35175-35197 - [c29]Dominik Jens Elias Waibel, Ernst Röell, Bastian Rieck, Raja Giryes, Carsten Marr:
A Diffusion Model Predicts 3D Shapes from 2D Microscopy Images. ISBI 2023: 1-5 - [c28]Hamed Alhoori, Edward A. Fox, Ingo Frommholz, Haiming Liu, Corinna Coupette, Bastian Rieck, Tirthankar Ghosal, Jian Wu:
Who can Submit an Excellent Review for this Manuscript in the Next 30 Days? - Peer Reviewing in the Age of Overload. JCDL 2023: 319-320 - [c27]Joshua Southern, Jeremy Wayland, Michael M. Bronstein, Bastian Rieck:
Curvature Filtrations for Graph Generative Model Evaluation. NeurIPS 2023 - [c26]Timothy Doster, Tegan Emerson, Henry Kvinge, Nina Miolane, Mathilde Papillon, Bastian Rieck, Sophia Sanborn:
Preface. TAG-ML 2023: 1-2 - [c25]Mathilde Papillon, Mustafa Hajij, Audun Myers, Florian Frantzen, Ghada Zamzmi, Helen Jenne, Johan Mathe, Josef Hoppe, Michael T. Schaub, Theodore Papamarkou, Aldo Guzmán-Sáenz, Bastian Rieck, Neal Livesay, Tamal K. Dey, Abraham Rabinowitz, Aiden Brent, Alessandro Salatiello, Alexander Nikitin, Ali Zia, Claudio Battiloro, Dmitrii Gavrilev, Georg Bökman, German Magai, Gleb Bazhenov, Guillermo Bernárdez, Indro Spinelli, Jens Agerberg, Kalyan Varma Nadimpalli, Lev Telyatnikov, Luca Scofano, Lucia Testa, Manuel Lecha, Maosheng Yang, Mohammed Hassanin, Odin Hoff Gardaa, Olga Zaghen, Paul Häusner, Paul Snopoff, Pavlo Melnyk, Rubén Ballester, Sadrodin Barikbin, Sergio Escalera, Simone Fiorellino, Henry Kvinge, Jan Meissner, Karthikeyan Natesan Ramamurthy, Michael Scholkemper, Paul Rosen, Robin Walters, Shreyas N. Samaga, Soham Mukherjee, Sophia Sanborn, Tegan Emerson, Timothy Doster, Tolga Birdal, Vincent P. Grande, Abdelwahed Khamis, Simone Scardapane, Suraj Singh, Tatiana Malygina, Yixiao Yue, Nina Miolane:
ICML 2023 Topological Deep Learning Challenge: Design and Results. TAG-ML 2023: 3-8 - [c24]Rayna Andreeva, Katharina Limbeck, Bastian Rieck, Rik Sarkar:
Metric Space Magnitude and Generalisation in Neural Networks. TAG-ML 2023: 242-253 - 2022
- [c23]Max Horn, Edward De Brouwer, Michael Moor, Yves Moreau, Bastian Rieck, Karsten M. Borgwardt:
Topological Graph Neural Networks. ICLR 2022 - [c22]Leslie O'Bray, Max Horn, Bastian Rieck, Karsten M. Borgwardt:
Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions. ICLR 2022 - [c21]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 - [c20]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 - [c19]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 - [c18]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 - [c17]Dhananjay Bhaskar, Kincaid MacDonald, Oluwadamilola Fasina, Dawson Thomas, Bastian Rieck, Ian Adelstein, Smita Krishnaswamy:
Diffusion Curvature for Estimating Local Curvature in High Dimensional Data. NeurIPS 2022 - [c16]Florian Graf, Sebastian Zeng, Bastian Rieck, Marc Niethammer, Roland Kwitt:
On Measuring Excess Capacity in Neural Networks. NeurIPS 2022 - [c15]Celia Hacker, Bastian Rieck:
On the Surprising Behaviour of \textttnode2vec. TAG-ML 2022: 142-151 - 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 - 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 - 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 - 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
Parts in Books or Collections
- 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
Editorship
- 2023
- [e2]Timothy Doster, Tegan Emerson, Henry Kvinge, Nina Miolane, Mathilde Papillon, Bastian Rieck, Sophia Sanborn:
Topological, Algebraic and Geometric Learning Workshops 2023, 28 July 2023, Honolulu, HI, USA. Proceedings of Machine Learning Research 221, PMLR 2023 [contents] - 2022
- [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]
Data and Artifacts
- 2022
- [d2]Corinna Coupette, Jilles Vreeken, Bastian Rieck:
Hyperbard (Dataset). Zenodo, 2022
- [d1]Corinna Coupette, Jilles Vreeken, Bastian Rieck:
Hyperbard (Code). Zenodo, 2022
Informal and Other Publications
- 2024
- [i56]Jeremy Wayland, Corinna Coupette, Bastian Rieck:
Mapping the Multiverse of Latent Representations. CoRR abs/2402.01514 (2024) - [i55]Theodore Papamarkou, Tolga Birdal, Michael M. Bronstein, Gunnar E. Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Liò, Paolo Di Lorenzo, Vasileios Maroulas, Nina Miolane, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck, Simone Scardapane, Michael T. Schaub, Petar Velickovic, Bei Wang, Yusu Wang, Guo-Wei Wei, Ghada Zamzmi:
Position Paper: Challenges and Opportunities in Topological Deep Learning. CoRR abs/2402.08871 (2024) - [i54]Benjamin Holmgren, Eli Quist, Jordan Schupbach, Brittany Terese Fasy, Bastian Rieck:
The Manifold Density Function: An Intrinsic Method for the Validation of Manifold Learning. CoRR abs/2402.09529 (2024) - [i53]Sara Kalisnik, Bastian Rieck, Ana Zegarac:
Persistent Homology via Ellipsoids. CoRR abs/2408.11450 (2024) - [i52]Jeremy Wayland, Russel J. Funk, Bastian Rieck:
Characterizing Physician Referral Networks with Ricci Curvature. CoRR abs/2408.16022 (2024) - [i51]Katharina Limbeck, Bastian Rieck:
Detecting Spatial Dependence in Transcriptomics Data using Vectorised Persistence Diagrams. CoRR abs/2409.03575 (2024) - [i50]Davide Buffelli, Farzin Soleymani, Bastian Rieck:
CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique Graphs. CoRR abs/2409.08217 (2024) - [i49]Rubén Ballester, Ernst Röell, Daniel Bin Schmid, Mathieu Alain, Sergio Escalera, Carles Casacuberta, Bastian Rieck:
MANTRA: The Manifold Triangulations Assemblage. CoRR abs/2410.02392 (2024) - [i48]Julius von Rohrscheidt, Bastian Rieck:
Diss-l-ECT: Dissecting Graph Data with local Euler Characteristic Transforms. CoRR abs/2410.02622 (2024) - [i47]Irene Cannistraci, Emanuele Rodolà, Bastian Rieck:
Detecting and Approximating Redundant Computational Blocks in Neural Networks. CoRR abs/2410.04941 (2024) - [i46]Mathieu Alain, So Takao, Xiaowen Dong, Bastian Rieck, Emmanuel Noutahi:
Graph Classification Gaussian Processes via Hodgelet Spectral Features. CoRR abs/2410.10546 (2024) - [i45]Bastian Rieck:
Topology meets Machine Learning: An Introduction using the Euler Characteristic Transform. CoRR abs/2410.17760 (2024) - [i44]Ernst Röell, Bastian Rieck:
Generative Topology for Shape Synthesis. CoRR abs/2410.18987 (2024) - 2023
- [i43]Joshua Southern, Jeremy Wayland, Michael M. Bronstein, Bastian Rieck:
Curvature Filtrations for Graph Generative Model Evaluation. CoRR abs/2301.12906 (2023) - [i42]Bastian Rieck:
On the Expressivity of Persistent Homology in Graph Learning. CoRR abs/2302.09826 (2023) - [i41]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) - [i40]Barbara Giunti, Janis Lazovskis, Bastian Rieck:
DONUT - Creation, Development, and Opportunities of a Database. CoRR abs/2304.12417 (2023) - [i39]Rayna Andreeva, Katharina Limbeck, Bastian Rieck, Rik Sarkar:
Metric Space Magnitude and Generalisation in Neural Networks. CoRR abs/2305.05611 (2023) - [i38]Leon Hetzel, Johanna Sommer, Bastian Rieck, Fabian J. Theis, Stephan Günnemann:
MAGNet: Motif-Agnostic Generation of Molecules from Shapes. CoRR abs/2305.19303 (2023) - [i37]Bastian Rieck, Corinna Coupette:
Evaluating the "Learning on Graphs" Conference Experience. CoRR abs/2306.00586 (2023) - [i36]Salome Kazeminia, Ario Sadafi, Asya Makhro, Anna Bogdanova, Carsten Marr, Bastian Rieck:
Topologically-Regularized Multiple Instance Learning for Red Blood Cell Disease Classification. CoRR abs/2307.14025 (2023) - [i35]Franz Srambical, Bastian Rieck:
Filtration Surfaces for Dynamic Graph Classification. CoRR abs/2309.03616 (2023) - [i34]Ernst Röell, Bastian Rieck:
Differentiable Euler Characteristic Transforms for Shape Classification. CoRR abs/2310.07630 (2023) - [i33]Katharina Limbeck, Rayna Andreeva, Rik Sarkar, Bastian Rieck:
Metric Space Magnitude for Evaluating Unsupervised Representation Learning. CoRR abs/2311.16054 (2023) - [i32]Kelly Maggs, Celia Hacker, Bastian Rieck:
Simplicial Representation Learning with Neural k-forms. CoRR abs/2312.08515 (2023) - 2022
- [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
- [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
- [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) - 2019
- [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
- [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)
Coauthor Index
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Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
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Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
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Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
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Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
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OpenAlex data
Load additional information about publications from 
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last updated on 2025-02-15 01:20 CET by the dblp team
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