Papers › Trellis Networks for Sequence Modeling

Trellis Networks for Sequence Modeling

15 Oct 2018ICLR 2019 5arXiv:1810.06682archive 2025-07-28

Shaojie Bai, J. Zico Kolter, Vladlen Koltun

We present trellis networks, a new architecture for sequence modeling. On the one hand, a trellis network is a temporal convolutional network with special structure, characterized by weight tying across depth and direct injection of the input into deep layers. On the other hand, we show that truncated recurrent networks are equivalent to trellis networks with special sparsity structure in their weight matrices. Thus trellis networks with general weight matrices generalize truncated recurrent networks. We leverage these connections to design high-performing trellis networks that absorb structural and algorithmic elements from both recurrent and convolutional models. Experiments demonstrate that trellis networks outperform the current state of the art methods on a variety of challenging benchmarks, including word-level language modeling and character-level language modeling tasks, and stress tests designed to evaluate long-term memory retention. The code is available at https://github.com/locuslab/trellisnet .

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count_parameters locuslab/trellisnet/TrellisNet/seq_MNIST_CIFAR/utils.py official repository ran · honoured contract MIT (permissive) · f6b944f50d3f15ae · report
repackage_hidden locuslab/trellisnet/TrellisNet/word_PTB/utils.py official repository ran · honoured contract fingerprinted MIT (permissive) · 4372bb4533fd1936 · report
batchify locuslab/trellisnet/TrellisNet/word_PTB/utils.py official repository unverified MIT (permissive) · 82c927821e8de854 · report
char_tensor locuslab/trellisnet/TrellisNet/char_PTB/utils.py official repository unverified MIT (permissive) · 1e3a5f83246ebc4f · report
data_generator locuslab/trellisnet/TrellisNet/seq_MNIST_CIFAR/utils.py official repository unverified MIT (permissive) · 512e27f728a83a6b · report
embedded_dropout locuslab/trellisnet/TrellisNet/optimizations.py official repository unverified MIT (permissive) · ad3308380ac371b2 · report
repackage_hidden4 locuslab/trellisnet/TrellisNet/char_PTB/utils.py official repository unverified MIT (permissive) · ae7fc1f2f81e0504 · report
weight_norm locuslab/trellisnet/TrellisNet/optimizations.py official repository unverified MIT (permissive) · 4bf00e788db9f349 · report

Tasks

Language ModelingLanguage ModellingSequential Image Classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Language Modelling Penn Treebank (Character Level) Trellis Network Bit per Character (BPC) 1.158 #4 of 20 Archive leaderboard report
Language Modelling Penn Treebank (Character Level) Trellis Network Number of params 13.4M #4 of 20 Archive leaderboard report
Language Modelling Penn Treebank (Word Level) Trellis Network Test perplexity 54.19 #19 of 43 Archive leaderboard report
Language Modelling WikiText-103 Trellis Network Test perplexity 29.19 #67 of 89 Archive leaderboard report
Sequential Image Classification Sequential CIFAR-10 Trellis Network Unpermuted Accuracy 73.42% #8 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

Weight Tying

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