Summary of Reparameterized Multi-resolution Convolutions For Long Sequence Modelling, by Harry Jake Cunningham et al.
Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling
by Harry Jake Cunningham, Giorgio Giannone, Mingtian Zhang, Marc Peter Deisenroth
First submitted to arxiv on: 18 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces reparameterized multi-resolution convolutions (MRConv), a new approach to parameterizing global convolutional kernels for long-sequence modeling. By leveraging multi-resolution convolutions, incorporating structural reparameterization, and introducing learnable kernel decay, MRConv learns expressive long-range kernels that perform well across various data modalities. The paper demonstrates state-of-the-art performance on the Long Range Arena, Sequential CIFAR, and Speech Commands tasks among convolution models and linear-time transformers. Additionally, the authors report improved performance on ImageNet classification by replacing 2D convolutions with 1D MRConv layers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to make computers understand long sequences of information, like words or sounds. This approach uses something called multi-resolution convolutions and helps the computer learn important patterns in the data without getting too good at memorizing specific examples. The results show that this method works well on different kinds of tasks, such as recognizing speech or classifying images. It’s a step forward in making computers better at understanding language and other sequences. |
Keywords
» Artificial intelligence » Classification
