_:n8798c06f26af4e279b9770aef7c41418xb53
<http://schema.org/description>
"Conventional neural architectures for sequential data present important\nlimitations. Recurrent networks suffer from exploding and vanishing gradients,\nsmall effective memory horizons, and must be trained sequentially.\nConvolutional networks are unable to handle sequences of unknown size and their\nmemory horizon must be defined a priori. In this work, we show that all these\nproblems can be solved by formulating convolutional kernels in CNNs as\ncontinuous functions. The resulting Continuous Kernel Convolution (CKConv)\nallows us to model arbitrarily long sequences in a parallel manner, within a\nsingle operation, and without relying on any form of recurrence. We show that\nContinuous Kernel Convolutional Networks (CKCNNs) obtain state-of-the-art\nresults in multiple datasets, e.g., permuted MNIST, and, thanks to their\ncontinuous nature, are able to handle non-uniformly sampled datasets and\nirregularly-sampled data natively. CKCNNs match or perform better than neural\nODEs designed for these purposes in a much faster and simpler manner."
<https://deepai.org/profile/mark-hoogendoorn>