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Set Functions for Time Series
Max Horn · Michael Moor · Christian Bock · Bastian Rieck · Karsten Borgwardt

Wed Jul 15 09:00 AM -- 09:45 AM & Wed Jul 15 10:00 PM -- 10:45 PM (PDT) @ None #None

Despite the eminent successes of deep neural networks, many architectures are often hard to transfer to irregularly-sampled and asynchronous time series that commonly occur in real-world datasets, especially in healthcare applications. This paper proposes a novel approach for classifying irregularly-sampled time series with unaligned measurements, focusing on high scalability and data efficiency. Our method SeFT (Set Functions for Time Series) is based on recent advances in differentiable set function learning, extremely parallelizable with a beneficial memory footprint, thus scaling well to large datasets of long time series and online monitoring scenarios. Furthermore, our approach permits quantifying per-observation contributions to the classification outcome. We extensively compare our method with existing algorithms on multiple healthcare time series datasets and demonstrate that it performs competitively whilst significantly reducing runtime.

Author Information

Max Horn (ETH Zurich)
Michael Moor (ETH Zurich)
Christian Bock (ETH Zurich)
Bastian Rieck (ETH Zurich)
Karsten Borgwardt (ETH Zurich)

Karsten Borgwardt is Professor of Data Mining at ETH Z├╝rich, at the Department of Biosystems located in Basel. His work has won several awards, including the NIPS 2009 Outstanding Paper Award, the Krupp Award for Young Professors 2013 and a Starting Grant 2014 from the ERC-backup scheme of the Swiss National Science Foundation. Since 2013, he is heading the Marie Curie Initial Training Network for "Machine Learning for Personalized Medicine" with 12 partner labs in 8 countries (http://www.mlpm.eu). The business magazine "Capital" listed him as one of the "Top 40 under 40" in Science in/from Germany in 2014, 2015 and 2016. For more information, visit: https://www.bsse.ethz.ch/mlcb

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