{"id":51513016,"url":"https://github.com/computorg/published-paper-tsne","last_synced_at":"2026-07-08T08:04:43.855Z","repository":{"id":40325348,"uuid":"399807503","full_name":"computorg/published-paper-tsne","owner":"computorg","description":"A prototype of published Computo paper (using quarto)","archived":false,"fork":false,"pushed_at":"2026-07-07T09:42:59.000Z","size":7777,"stargazers_count":1,"open_issues_count":2,"forks_count":6,"subscribers_count":5,"default_branch":"main","last_synced_at":"2026-07-07T10:26:26.120Z","etag":null,"topics":["interactive-visualizations","mock-paper"],"latest_commit_sha":null,"homepage":"http://computo-journal.org/published-paper-tsne/","language":"TeX","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc-by-4.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/computorg.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2021-08-25T12:19:14.000Z","updated_at":"2026-07-07T09:43:05.000Z","dependencies_parsed_at":"2023-02-19T16:45:33.246Z","dependency_job_id":"2820c3dd-7e3a-4e07-9ced-0649af1c3ce4","html_url":"https://github.com/computorg/published-paper-tsne","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/computorg/published-paper-tsne","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-paper-tsne","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-paper-tsne/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-paper-tsne/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-paper-tsne/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/computorg","download_url":"https://codeload.github.com/computorg/published-paper-tsne/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-paper-tsne/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":35257345,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-07-08T02:00:06.796Z","response_time":61,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["interactive-visualizations","mock-paper"],"created_at":"2026-07-08T08:04:43.071Z","updated_at":"2026-07-08T08:04:43.849Z","avatar_url":"https://github.com/computorg.png","language":"TeX","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Visualizing Data using t-SNE (mock contribution)\nLaurens van der Maaten, Geoffrey Hinton\n2008-08-11\n\n*This page is a reworking of the original t-SNE article using the\nComputo template. It aims to help authors submitting to the journal by\nusing some advanced formatting features. We warmly thank the authors of\nt-SNE and the editor of JMLR for allowing us to use their work to\nillustrate the Computo spirit.*\n\n### Citation\n\nLaurens van der Maaten and Geoffrey Hinton (August 2008). Visualizing Data using t-SNE (mock contribution). Computo.\n\u003chttps://doi.org/10.57750/xxx-xxx\u003e\n\n### Badges\n\n[![build and\npublish](https://github.com/computorg/published-paper-tsne/actions/workflows/build.yml/badge.svg)](https://github.com/computorg/published-paper-tsne/actions/workflows/build.yml)\n[![reviews](https://img.shields.io/badge/review-report-blue)](https://github.com/computorg/published-paper-tsne/issues?q=is%3Aopen+is%3Aissue+label%3Areview)\n[![SWH](https://archive.softwareheritage.org/badge/origin/https://github.com/computorg/published-paper-tsne)](https://archive.softwareheritage.org/browse/origin/?origin_url=https://github.com/computorg/published-paper-tsne)\n[![DOI:10.57750/xxx-xxx](https://img.shields.io/badge/DOI-10.57750%2Fxxx--xxx-034E79.svg)](https://doi.org/10.57750/xxx-xxx)\n[![Creative Commons\nLicense](https://i.creativecommons.org/l/by/4.0/80x15.png)](http://creativecommons.org/licenses/by/4.0/)\n\n### Authors’ affiliations\n\n- [Laurens van der Maaten](https://lvdmaaten.github.io/) (TiCC, Tilburg University)\n- [Geoffrey Hinton](https://www.cs.toronto.edu/~hinton/) (Department of Computer Science, University of Toronto)\n\n### Abstract\n\nWe present a new technique called “t-SNE” that visualizes\nhigh-dimensional data by giving each datapoint a location in a two or\nthree-dimensional map. The technique is a variation of Stochastic\nNeighbor Embedding (Hinton and Roweis 2003) that is much easier to\noptimize, and produces significantly better visualizations by reducing\nthe tendency to crowd points together in the center of the map. t-SNE is\nbetter than existing techniques at creating a single map that reveals\nstructure at many different scales. This is particularly important for\nhigh-dimensional data that lie on several different, but related,\nlow-dimensional manifolds, such as images of objects from multiple\nclasses seen from multiple viewpoints. For visualizing the structure of\nvery large data sets, we show how t-SNE can use random walks on\nneighborhood graphs to allow the implicit structure of all the data to\ninfluence the way in which a subset of the data is displayed. We\nillustrate the performance of t-SNE on a wide variety of data sets and\ncompare it with many other non-parametric visualization techniques,\nincluding Sammon mapping, Isomap, and Locally Linear Embedding. The\nvisualization produced by t-SNE are significantly better than those\nproduced by other techniques on almost all of the data sets.\n\n\u003cdiv id=\"refs\" class=\"references csl-bib-body hanging-indent\"\u003e\n\n\u003cdiv id=\"ref-hinton:stochastic\" class=\"csl-entry\"\u003e\n\nHinton, Geoffrey E, and Sam Roweis. 2003. “Stochastic Neighbor\nEmbedding.” In *Advances in Neural Information Processing Systems*,\nedited by S. Becker, S. Thrun, and K. Obermayer, vol. 15. MIT Press.\n\u003chttps://proceedings.neurips.cc/paper/2002/file/6150ccc6069bea6b5716254057a194ef-Paper.pdf\u003e.\n\n\u003c/div\u003e\n\n\u003c/div\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcomputorg%2Fpublished-paper-tsne","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcomputorg%2Fpublished-paper-tsne","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcomputorg%2Fpublished-paper-tsne/lists"}