  Using electrocardiograms as an example, we demonstrate the characteristic
problems that arise when modeling one-dimensional signals containing inaccurate
repeating pattern by means of standard convolutional networks. We show that
these problems are systemic in nature. They are due to how convolutional
networks work with composite objects, parts of which are not fixed rigidly, but
have significant mobility. We also demonstrate some counterintuitive effects
related to generalization in deep networks.
