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Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence., , , , , , and . NeurIPS, (2020)A Persistent Weisfeiler-Lehman Procedure for Graph Classification., , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 5448-5458. PMLR, (2019)Generating Load Profiles Using Smart Metering Time Series.. EUSFLAT/IWIFSGN (1), volume 641 of Advances in Intelligent Systems and Computing, page 211-223. Springer, (2017)Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology., , , , , , and . ICLR (Poster), OpenReview.net, (2019)Set Functions for Time Series., , , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 4353-4363. PMLR, (2020)Machine learning for early prediction of circulatory failure in the intensive care unit., , , , , , , , , and 5 other author(s). CoRR, (2019)Solving Interoperability in Translational Health., , , , , , , , , and 20 other author(s). Appl. Clin. Inform., 08 (02): 651-659 (2017)Motifs and Manifolds Statistical and Topological Machine Learning for Characterising and Classifying Biomedical Time Series.. ETH Zurich, Zürich, Switzerland, (2021)base-search.net (ftethz:oai:www.research-collection.ethz.ch:20.500.11850/524042).A Wasserstein Subsequence Kernel for Time Series, , , , , and . Proceedings of the ICDM, (2019)A Wasserstein Subsequence Kernel for Time Series., , , , , and . ICDM, page 964-969. IEEE, (2019)