Papers › CKConv: Continuous Kernel Convolution For Sequential Data
CKConv: Continuous Kernel Convolution For Sequential Data
David W. Romero, Anna Kuzina, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn
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 faster and simpler manner.
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Code
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Sequential Image Classification | Sequential CIFAR-10 | CKCNN (1M) | Unpermuted Accuracy | 63.74% | #11 of 13 | Archive leaderboard | report |
| Sequential Image Classification | Sequential CIFAR-10 | CKCNN (100k) | Unpermuted Accuracy | 62.25% | #12 of 13 | Archive leaderboard | report |
| Sequential Image Classification | Sequential MNIST | CKCNN (1M) | Permuted Accuracy | 98.54% | #5 of 30 | Archive leaderboard | report |
| Sequential Image Classification | Sequential MNIST | CKCNN (1M) | Unpermuted Accuracy | 99.32% | #5 of 30 | Archive leaderboard | report |
| Sequential Image Classification | Sequential MNIST | CKCNN (100k) | Permuted Accuracy | 98% | #9 of 30 | Archive leaderboard | report |
| Sequential Image Classification | Sequential MNIST | CKCNN (100k) | Unpermuted Accuracy | 99.31% | #9 of 30 | Archive leaderboard | report |
| Time Series Analysis | Speech Commands | CKCNN (100k) | % Test Accuracy | 95.27 | #5 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: CKConv
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