{"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/tensor-decomposition-for-compressing","title":"Tensor Decomposition for Compressing Recurrent Neural Network","arxiv_id":"1802.10410","date":"2018-02-28","proceeding":null,"authors":["Andros Tjandra","Sakriani Sakti","Satoshi Nakamura"],"abstract":"In the machine learning fields, Recurrent Neural Network (RNN) has become a\npopular architecture for sequential data modeling. However, behind the\nimpressive performance, RNNs require a large number of parameters for both\ntraining and inference. In this paper, we are trying to reduce the number of\nparameters and maintain the expressive power from RNN simultaneously. We\nutilize several tensor decompositions method including CANDECOMP/PARAFAC (CP),\nTucker decomposition and Tensor Train (TT) to re-parameterize the Gated\nRecurrent Unit (GRU) RNN. We evaluate all tensor-based RNNs performance on\nsequence modeling tasks with a various number of parameters. Based on our\nexperiment results, TT-GRU achieved the best results in a various number of\nparameters compared to other decomposition methods.","url_abs":"http://arxiv.org/abs/1802.10410v2","url_pdf":"http://arxiv.org/pdf/1802.10410v2.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":"tensor-decomposition-for-compressing","repo_url":"https://github.com/androstj/tensor_rnn","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"tensor-decomposition","task_name":"Tensor Decomposition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.10410","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.10410"}},"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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