  In this paper, we propose a method of improving temporal Convolutional Neural
Networks (CNN) by determining the optimal alignment of weights and inputs using
dynamic programming. Conventional CNN convolutions linearly match the shared
weights to a window of the input. However, it is possible that there exists a
more optimal alignment of weights. Thus, we propose the use of Dynamic Time
Warping (DTW) to dynamically align the weights to the input of the
convolutional layer. Specifically, the dynamic alignment overcomes issues such
as temporal distortion by finding the minimal distance matching of the weights
and the inputs under constraints. We demonstrate the effectiveness of the
proposed architecture on the Unipen online handwritten digit and character
datasets, the UCI Spoken Arabic Digit dataset, and the UCI Activities of Daily
Life dataset.
