Abstract: Conventional neural architectures for sequential data present important
limitations. Recurrent networks suffer from exploding and vanishing gradients,
small effective memory horizons, and must be trained sequentially.
Convolutional networks are unable to handle sequences of unknown size and their
memory horizon must be defined a priori. In this work, we show that all these
problems can be solved by formulating convolutional kernels in CNNs as
continuous functions. The resulting Continuous Kernel Convolution (CKConv)
allows us to model arbitrarily long sequences in a parallel manner, within a
single operation, and without relying on any form of recurrence. We show that
Continuous Kernel Convolutional Networks (CKCNNs) obtain state-of-the-art
results in multiple datasets, e.g., permuted MNIST, and, thanks to their
continuous nature, are able to handle non-uniformly sampled datasets and
irregularly-sampled data natively. CKCNNs match or perform better than neural
ODEs designed for these purposes in a much faster and simpler manner.