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Convolutional neural networks (CNNs) with dilated filters such as the Wavenetor the Temporal Convolutional Network (TCN) have shown good results in avariety of sequence modelling tasks. However, efficiently modelling long-termdependencies in these sequences is still challenging. Although the receptivefield of these models grows exponentially with the number of layers, computingthe convolutions over very long sequences of features in each layer is time andmemory-intensive, prohibiting the use of longer receptive fields in practice.To increase efficiency, we make use of the "slow feature" hypothesis statingthat many features of interest are slowly varying over time. For this, we use aU-Net architecture that computes features at multiple time-scales and adapt itto our auto-regressive scenario by making convolutions causal. We apply ourmodel ("Seq-U-Net") to a variety of tasks including language and audiogeneration. In comparison to TCN and Wavenet, our network consistently savesmemory and computation time, with speed-ups for training and inference of over4x in the audio generation experiment in particular, while achieving acomparable performance in all tasks.

Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence Modelling