CKConv: Continuous Kernel Convolution For Sequential Data
David W. Romero, Anna Kuzina, Erik J Bekkers, Jakub Mikolaj Tomczak, Mark Hoogendoorn
Abstract
Conventional neural architectures for sequential data present important limitations. Recurrent neural networks suffer from exploding and vanishing gradients, small effective memory horizons, and must be trained sequentially. Convolutional neural networks cannot handle sequences of unknown size and their memory horizon must be defined a priori. In this work, we show that these problems can be solved by formulating the convolutional kernels of CNNs as continuous functions. The resulting Continuous Kernel Convolution (CKConv) handles 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 faster and simpler manner.
BibTeX
@inproceedings{
romero2022ckconv,
title={{CKC}onv: Continuous Kernel Convolution For Sequential Data},
author={David W. Romero and Anna Kuzina and Erik J Bekkers and Jakub Mikolaj Tomczak and Mark Hoogendoorn},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=8FhxBtXSl0}
}