{"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/unsupervised-learning-with-contrastive-latent","title":"Unsupervised learning with contrastive latent variable models","arxiv_id":"1811.06094","date":"2018-11-14","proceeding":null,"authors":["Kristen Severson","Soumya Ghosh","Kenney Ng"],"abstract":"In unsupervised learning, dimensionality reduction is an important tool for\ndata exploration and visualization. Because these aims are typically\nopen-ended, it can be useful to frame the problem as looking for patterns that\nare enriched in one dataset relative to another. These pairs of datasets occur\ncommonly, for instance a population of interest vs. control or signal vs.\nsignal free recordings.However, there are few methods that work on sets of data\nas opposed to data points or sequences. Here, we present a probabilistic model\nfor dimensionality reduction to discover signal that is enriched in the target\ndataset relative to the background dataset. The data in these sets do not need\nto be paired or grouped beyond set membership. By using a probabilistic model\nwhere some structure is shared amongst the two datasets and some is unique to\nthe target dataset, we are able to recover interesting structure in the latent\nspace of the target dataset. The method also has the advantages of a\nprobabilistic model, namely that it allows for the incorporation of prior\ninformation, handles missing data, and can be generalized to different\ndistributional assumptions. We describe several possible variations of the\nmodel and demonstrate the application of the technique to de-noising, feature\nselection, and subgroup discovery settings.","url_abs":"http://arxiv.org/abs/1811.06094v1","url_pdf":"http://arxiv.org/pdf/1811.06094v1.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":"unsupervised-learning-with-contrastive-latent","repo_url":"https://github.com/kseverso/contrastive-LVM","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"subgroup-discovery","task_name":"Subgroup Discovery"},{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.06094","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.06094"}},"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/kseverso/contrastive-LVM","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":3},"by_repo_kind":{"official":{"samples":3,"ran":0,"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":0,"samples":[{"code_sha256_prefix":"ee3a0e403935631f","entry":"build_test_dataset","repo":"kseverso/contrastive-LVM","repo_kind":"official","path":"ppca_tfp.py","file_url":"https://github.com/kseverso/contrastive-LVM/blob/HEAD/ppca_tfp.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ee3a0e403935631f"}},{"code_sha256_prefix":"30351295943b24e9","entry":"build_toy_dataset","repo":"kseverso/contrastive-LVM","repo_kind":"official","path":"ppca_tfp.py","file_url":"https://github.com/kseverso/contrastive-LVM/blob/HEAD/ppca_tfp.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"30351295943b24e9"}},{"code_sha256_prefix":"f1da5d36ac653196","entry":"build_toy_missing_dataset","repo":"kseverso/contrastive-LVM","repo_kind":"official","path":"experiments/missing_syn.py","file_url":"https://github.com/kseverso/contrastive-LVM/blob/HEAD/experiments/missing_syn.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f1da5d36ac653196"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}