{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/seq-u-net-a-one-dimensional-causal-u-net-for","title":"Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence Modelling","arxiv_id":"1911.06393","date":"2019-11-14","proceeding":null,"authors":["Daniel Stoller","Mi Tian","Sebastian Ewert","Simon Dixon"],"abstract":"Convolutional neural networks (CNNs) with dilated filters such as the Wavenet or the Temporal Convolutional Network (TCN) have shown good results in a variety of sequence modelling tasks. However, efficiently modelling long-term dependencies in these sequences is still challenging. Although the receptive field of these models grows exponentially with the number of layers, computing the convolutions over very long sequences of features in each layer is time and memory-intensive, prohibiting the use of longer receptive fields in practice. To increase efficiency, we make use of the \"slow feature\" hypothesis stating that many features of interest are slowly varying over time. For this, we use a U-Net architecture that computes features at multiple time-scales and adapt it to our auto-regressive scenario by making convolutions causal. We apply our model (\"Seq-U-Net\") to a variety of tasks including language and audio generation. In comparison to TCN and Wavenet, our network consistently saves memory and computation time, with speed-ups for training and inference of over 4x in the audio generation experiment in particular, while achieving a comparable performance in all tasks.","url_abs":"https://arxiv.org/abs/1911.06393v1","url_pdf":"https://arxiv.org/pdf/1911.06393v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"seq-u-net-a-one-dimensional-causal-u-net-for","repo_url":"https://github.com/f90/Seq-U-Net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"audio-generation","task_name":"Audio Generation"},{"task_slug":null,"task_name":"Causal Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"music-modeling","task_name":"Music Modeling"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dilated-causal-convolution","method_name":"Dilated Causal Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"mixture-of-logistic-distributions","method_name":"Mixture of Logistic Distributions"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"},{"method_slug":"wavenet","method_name":"WaveNet"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/language-modelling-on-penn-treebank-character","task":"Language Modelling","dataset":"Penn Treebank (Character Level)","model":"Seq-U-Net","rank_in_archive_order":16,"of":20,"metrics":{"Bit per Character (BPC)":"1.3","Number of params":"5.9M"},"uses_additional_data":false},{"leaderboard":"/sota/language-modelling-on-penn-treebank-character","task":"Language Modelling","dataset":"Penn Treebank (Character Level)","model":"TCN","rank_in_archive_order":18,"of":20,"metrics":{"Bit per Character (BPC)":"1.31","Number of params":"5.9M"},"uses_additional_data":false},{"leaderboard":"/sota/language-modelling-on-penn-treebank-word","task":"Language Modelling","dataset":"Penn Treebank (Word Level)","model":"Seq-U-Net","rank_in_archive_order":42,"of":43,"metrics":{"Params":"14.9M","Test perplexity":"107.95"},"uses_additional_data":false},{"leaderboard":"/sota/language-modelling-on-penn-treebank-word","task":"Language Modelling","dataset":"Penn Treebank (Word Level)","model":"TCN","rank_in_archive_order":43,"of":43,"metrics":{"Params":"14.7M","Test perplexity":"108.47"},"uses_additional_data":false},{"leaderboard":"/sota/music-modeling-on-jsb-chorales","task":"Music Modeling","dataset":"JSB Chorales","model":"TCN","rank_in_archive_order":8,"of":10,"metrics":{"NLL":"8.154","Parameters":"534K"},"uses_additional_data":false},{"leaderboard":"/sota/music-modeling-on-jsb-chorales","task":"Music Modeling","dataset":"JSB Chorales","model":"Seq-U-Net","rank_in_archive_order":9,"of":10,"metrics":{"NLL":"8.173","Parameters":"522K"},"uses_additional_data":false},{"leaderboard":"/sota/music-modeling-on-nottingham","task":"Music Modeling","dataset":"Nottingham","model":"TCN","rank_in_archive_order":2,"of":8,"metrics":{"NLL":"2.783","Parameters":"1.7M"},"uses_additional_data":false},{"leaderboard":"/sota/music-modeling-on-nottingham","task":"Music Modeling","dataset":"Nottingham","model":"Seq-U-Net","rank_in_archive_order":3,"of":8,"metrics":{"NLL":"2.97","Parameters":"1.7M"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1911.06393","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.06393"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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