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The existing deep learning based time series classification (TSC) algorithms have some success in multivariate time series, their accuracy is not high when we apply them on brain EEG time series (65
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challenging data problem. Weak signals from collisions of compact objects can be dug out of noisy time series because we understand what the signal should look like, and can therefore use simple algorithms
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-readable representations, such as distributed representations of text augmented with random noises [1] or unnatural text curated by replacing sensitive tokens with random non-sensitive ones [2]. First, such
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distributions. We wish to represent the biological networks into proper formats, e.g., vector representations, so that existing machine learning algorithms (e.g., support vector machines) can readily be used