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Set Functions for Time Series
[article]
2020
arXiv
pre-print
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
arXiv:1909.12064v3
fatcat:2sjbyvc37rglndluspjolt2g5a