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arXiv Machine Learning By Pierre Houedry, Romain Tavenard, Nicolas Courty, Romaric Gaudel

Fast Gradient-Based Approximation of Continuous Dynamic Time Warping

Read the original on arXiv Machine Learning →

The paper introduces FastCDTW, an efficient approximation for Continuous Dynamic Time Warping (CDTW). It offers two computational strategies: an exact closed‑form solution for piecewise‑linear warping functions and an unbiased Monte Carlo estimator for smooth warping functions such as those implemented by implicit neural networks. Both approaches provide gradients with respect to the warping function and the input time series, making the algorithm GPU‑friendly and suitable for integration into complex learning tasks like time‑series averaging, classification, and deep domain adaptation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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