@inproceedings{
cunningham2024reparameterized,
title={Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling},
author={Harry Jake Cunningham and Giorgio Giannone and Mingtian Zhang and Marc Peter Deisenroth},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=RwgNbIpCpk}
}
Global convolutions have shown increasing promise as powerful general-purpose sequence models. However, training long convolutions is challenging, and kernel parameterizations must be able to learn long-range dependencies without overfitting. This work introduces reparameterized multi-resolution convolutions ($\texttt{MRConv}$), a novel approach to parameterizing global convolutional kernels for long-sequence modeling. By leveraging multi-resolution convolutions, incorporating structural reparameterization and introducing learnable kernel decay, $\texttt{MRConv}$ learns expressive long-range kernels that perform well across various data modalities. Our experiments demonstrate state-of-the-art performance on the Long Range Arena, Sequential CIFAR, and Speech Commands tasks among convolution models and linear-time transformers. Moreover, we report improved performance on ImageNet classification by replacing 2D convolutions with 1D $\texttt{MRConv}$ layers.
Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling
Harry Jake Cunningham Giorgio Giannone Mingtian Zhang Marc Peter Deisenroth