Efficiently capturing the long-range patterns in sequential data sources
salient to a given task -- such as classification and generative modeling --
poses a fundamental challenge. Popular approaches in the space tradeoff between
the memory burden of brute-force enumeration and comparison, as in
transformers, the computational burden of complicated sequential dependencies,
as in recurrent neural networks, or the parameter burden of convolutional
networks with many or large filters. We instead take inspiration from
wavelet-based multiresolution analysis to define a new building block for
sequence modeling, which we call a MultiresLayer. The key component of our
model is the multiresolution convolution, capturing multiscale trends in the
input sequence. Our MultiresConv can be implemented with shared filters across
a dilated causal convolution tree. Thus it garners the computational advantages
of convolutional networks and the principled theoretical motivation of wavelet
decompositions. Our MultiresLayer is straightforward to implement, requires
significantly fewer parameters, and maintains at most a $\mathcal{O}(N\log N)$
memory footprint for a length $N$ sequence. Yet, by stacking such layers, our
model yields state-of-the-art performance on a number of sequence
classification and autoregressive density estimation tasks using CIFAR-10,
ListOps, and PTB-XL datasets.

我们提出一种基于多分辨率分析的序列模型，通过引入 multiresolution 卷积来捕捉输入序列的多尺度趋势，该模型性能优异，适用于各种序列分类和密度估计任务。