{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/tensor-networks/papers/3","list_of":"/task/tensor-networks","task":"Tensor Networks","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":3,"pages_in_order":3,"rows_per_page":100,"rows":[201,226],"of":226,"counts":{"archive_papers_tagged":226,"with_a_code_link":71,"where_syntology_ran_a_sample":11,"not_listed_spam_title":0,"listed":226,"listed_where_code_ran":11,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":10,"every_run_a_failure_of_syntologys_instrument":1,"listed_with_a_run_with_no_instrument_failure":10,"listed_every_run_a_failure_of_syntologys_instrument":1,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/tensor-networks","prev":"/task/tensor-networks/papers/2","next":null,"papers":[{"url":null,"slug":"number-state-preserving-tensor-networks-as","title":"Number-State Preserving Tensor Networks as Classifiers for Supervised Learning","date":"2019-05-15","arxiv_id":"1905.06352","repositories_listed":0,"syntology":null},{"url":null,"slug":"incremental-multi-domain-learning-with","title":"Incremental multi-domain learning with network latent tensor factorization","date":"2019-04-12","arxiv_id":"1904.06345","repositories_listed":0,"syntology":null},{"url":null,"slug":"tree-tensor-networks-for-generative-modeling","title":"Tree Tensor Networks for Generative Modeling","date":"2019-01-08","arxiv_id":"1901.02217","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-computing-and-the-brain-quantum-nets","title":"Quantum computing and the brain: quantum nets, dessins d'enfants and neural networks","date":"2018-12-18","arxiv_id":"1812.08338","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-neural-networks-with","title":"Convolutional Neural Networks with Transformed Input based on Robust Tensor Network Decomposition","date":"2018-11-20","arxiv_id":"1812.02622","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-compression-of-sum-product-networks-on","title":"Deep Compression of Sum-Product Networks on Tensor Networks","date":"2018-11-09","arxiv_id":"1811.03963","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-methods-from-tensor-networks","title":"Spectral Methods from Tensor Networks","date":"2018-11-02","arxiv_id":"1811.00944","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-expressive-power-of-parameterized-quantum","title":"The Expressive Power of Parameterized Quantum Circuits","date":"2018-10-29","arxiv_id":"1810.11922","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-learning-with-generalized-tensor","title":"From probabilistic graphical models to generalized tensor networks for supervised learning","date":"2018-06-15","arxiv_id":"1806.05964","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-tensor-networks-with-diagonal-slice","title":"Neural Tensor Networks with Diagonal Slice Matrices","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-machine-learning-matrix-product","title":"Quantum Machine Learning Tensor Network States","date":"2018-04-06","arxiv_id":"1804.02398","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-quantum-machine-learning-with-tensor","title":"Towards Quantum Machine Learning with Tensor Networks","date":"2018-03-30","arxiv_id":"1803.11537","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-by-two-dimensional-1","title":"Machine Learning by Two-Dimensional Hierarchical Tensor Networks: A Quantum Information Theoretic Perspective on Deep Architectures","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-relevant-features-of-data-with-multi","title":"Learning Relevant Features of Data with Multi-scale Tensor Networks","date":"2017-12-31","arxiv_id":"1801.00315","repositories_listed":0,"syntology":null},{"url":"/paper/neural-cross-lingual-entity-linking","slug":"neural-cross-lingual-entity-linking","title":"Neural Cross-Lingual Entity Linking","date":"2017-12-05","arxiv_id":"1712.01813","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-network-language-model","title":"Tensor network language model","date":"2017-10-27","arxiv_id":"1710.10248","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-network-quantum-states-string-bond","title":"Neural-Network Quantum States, String-Bond States, and Chiral Topological States","date":"2017-10-11","arxiv_id":"1710.04045","repositories_listed":0,"syntology":null},{"url":null,"slug":"duality-of-graphical-models-and-tensor","title":"Duality of Graphical Models and Tensor Networks","date":"2017-10-04","arxiv_id":"1710.01437","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-design-and-renormalization","title":"Language Design as Information Renormalization","date":"2017-08-04","arxiv_id":"1708.01525","repositories_listed":0,"syntology":null},{"url":null,"slug":"restricted-recurrent-neural-tensor-networks","title":"Restricted Recurrent Neural Tensor Networks: Exploiting Word Frequency and Compositionality","date":"2017-04-03","arxiv_id":"1704.00774","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-driven-event-embedding-for-stock","title":"Knowledge-Driven Event Embedding for Stock Prediction","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"statistical-machine-translation-features-with","title":"Statistical Machine Translation Features with Multitask Tensor Networks","date":"2015-06-01","arxiv_id":"1506.00698","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-distributed-word-representations-for","title":"Learning Distributed Word Representations for Natural Logic Reasoning","date":"2014-10-15","arxiv_id":"1410.4176","repositories_listed":0,"syntology":null},{"url":null,"slug":"recursive-neural-networks-can-learn-logical","title":"Recursive Neural Networks Can Learn Logical Semantics","date":"2014-06-06","arxiv_id":"1406.1827","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-recursive-recurrent-neural-network-for","title":"A Recursive Recurrent Neural Network for Statistical Machine Translation","date":"2014-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reasoning-with-neural-tensor-networks-for","title":"Reasoning With Neural Tensor Networks for Knowledge Base Completion","date":"2013-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"f2fe67a8a93c8f3030852e81547eceee705a9d023e855bf1ce6be1a51480f9b7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}