What is the theoretical foundation of the proposed sequence classification method based on convolutional neural networks?
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The proposed sequence classification method based on convolutional neural networks (CNN) has a theoretical foundation rooted in wavelet-based multiresolution analysis . The key component of the model is the multiresolution convolution, which captures multiscale trends in the input sequence. This approach takes inspiration from wavelet decompositions and utilizes a building block called a MultiresLayer. The MultiresConv, implemented with shared filters across a dilated causal convolution tree, combines the computational advantages of convolutional networks with the theoretical motivation of wavelet decompositions. The model requires significantly fewer parameters and maintains a memory footprint of at most O(NlogN) for a length N sequence. By stacking these MultiresLayers, the model achieves state-of-the-art performance on sequence classification tasks .
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17 Nov 2022 | The paper does not explicitly mention the theoretical foundation of the proposed sequence classification method based on convolutional neural networks. |
| The paper does not explicitly mention the theoretical foundation of the proposed sequence classification method based on convolutional neural networks. | |
23 Citations | The paper does not explicitly mention the theoretical foundation of the proposed sequence classification method based on convolutional neural networks. |
| The theoretical foundation of the proposed sequence classification method is based on wavelet decompositions, which are used to define a new building block called MultiresLayer. | |
02 May 2023 | The theoretical foundation of the proposed sequence classification method is based on wavelet decompositions and multiresolution analysis. |
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