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Differentiating multivariate dynamic signals is a difficult learning problemas the feature space may be large yet often only a few training examples areavailable. Traditional approaches to this problem either proceed fromhandcrafted features or require large datasets to combat the m >> n problem. Inthis paper, we show that the source of the problem---signal dynamics---can beused to our advantage and noticeably improve classification performance on arange of discrimination tasks when training data is scarce. We demonstrate thatself-supervised pre-training guided by signal dynamics produces embedding thatgeneralizes across tasks, datasets, data collection sites, and datadistributions. We perform an extensive evaluation of this approach on a rangeof tasks including simulated data, keyword detection problem, and a range offunctional neuroimaging data, where we show that a single embedding learnt onhealthy subjects generalizes across a number of disorders, age groups, anddatasets.

Learnt dynamics generalizes across tasks, datasets, and populations