Papers › Sequence Modeling with Multiresolution Convolutional Memory

Sequence Modeling with Multiresolution Convolutional Memory

2 May 2023arXiv:2305.01638archive 2025-07-28

Jiaxin Shi, Ke Alexander Wang, Emily B. Fox

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 𝒪(NlogN) 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.

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MultiresLayer thjashin/multires-conv/layers/multireslayer.py official repository ran · metamorphic tier: deterministic MIT (permissive) · c27418dba9fbd66d · report
forward_uniform thjashin/multires-conv/layers/multireslayer.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 88f02c31c56c7058 · report
masked_meanpool thjashin/multires-conv/classification.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · d34b5691b224ae80 · report
eval thjashin/multires-conv/autoregressive.py official repository unverified MIT (permissive) · 32d5fef9113aac56 · report
forward_fading thjashin/multires-conv/layers/multireslayer.py official repository unverified MIT (permissive) · a50200f714b086df · report
train thjashin/multires-conv/classification.py official repository unverified MIT (permissive) · 6bde61fec84781de · report
train thjashin/multires-conv/autoregressive.py official repository unverified MIT (permissive) · ad628a83df7e160a · report
uniform_tree_select thjashin/multires-conv/layers/multireslayer.py official repository unverified MIT (permissive) · b05c3cd049bf812e · report

Tasks

Density EstimationListOpsSequential Image Classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Sequential Image Classification Sequential CIFAR-10 MultiresConv Unpermuted Accuracy 93.15% #1 of 13 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

Causal ConvolutionConvolutionDilated Causal Convolution

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