Learnt dynamics generalizes across tasks, datasets, and populations
Abstract
Differentiating multivariate dynamic signals is a difficult learning problem as the feature space may be large yet often only a few training examples are available. Traditional approaches to this problem either proceed from handcrafted features or require large datasets to combat the m >> n problem. In this paper, we show that the source of the problem---signal dynamics---can be used to our advantage and noticeably improve classification...
Paper Details
Title
Learnt dynamics generalizes across tasks, datasets, and populations
Published Date
Dec 4, 2019
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