{"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/tensorizing-neural-networks","title":"Tensorizing Neural Networks","arxiv_id":"1509.06569","date":"2015-09-22","proceeding":"NeurIPS 2015 12","authors":["Alexander Novikov","Dmitry Podoprikhin","Anton Osokin","Dmitry Vetrov"],"abstract":"Deep neural networks currently demonstrate state-of-the-art performance in\nseveral domains. At the same time, models of this class are very demanding in\nterms of computational resources. In particular, a large amount of memory is\nrequired by commonly used fully-connected layers, making it hard to use the\nmodels on low-end devices and stopping the further increase of the model size.\nIn this paper we convert the dense weight matrices of the fully-connected\nlayers to the Tensor Train format such that the number of parameters is reduced\nby a huge factor and at the same time the expressive power of the layer is\npreserved. In particular, for the Very Deep VGG networks we report the\ncompression factor of the dense weight matrix of a fully-connected layer up to\n200000 times leading to the compression factor of the whole network up to 7\ntimes.","url_abs":"http://arxiv.org/abs/1509.06569v2","url_pdf":"http://arxiv.org/pdf/1509.06569v2.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":"tensorizing-neural-networks","repo_url":"https://github.com/Bihaqo/TensorNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"tensorizing-neural-networks","repo_url":"https://github.com/Gyiming/MobileSLAM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"tensorizing-neural-networks","repo_url":"https://github.com/philip-bl/tensorizing_neural_networks-novikov_2015","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"tensorizing-neural-networks","repo_url":"https://github.com/timgaripov/TensorNet-TF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-mnist","task":"Image Classification","dataset":"MNIST","model":"Perceptron with a tensor train layer","rank_in_archive_order":54,"of":81,"metrics":{"Accuracy":"98.2","Percentage error":"1.8"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1509.06569","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1509.06569"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/philip-bl/tensorizing_neural_networks-novikov_2015","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/timgaripov/TensorNet-TF","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Bihaqo/TensorNet","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Gyiming/MobileSLAM","reach":{"status":"ok"}}],"summary":{"ran_fixture":1},"by_repo_kind":{"listed":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"856b72c2e50598d5","entry":"permute_pixels","repo":"philip-bl/tensorizing_neural_networks-novikov_2015","repo_kind":"listed","path":"mnist.py","file_url":"https://github.com/philip-bl/tensorizing_neural_networks-novikov_2015/blob/HEAD/mnist.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"856b72c2e50598d5"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}