{"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/autoencoders-and-probabilistic-inference-with","title":"Autoencoders and Probabilistic Inference with Missing Data: An Exact Solution for The Factor Analysis Case","arxiv_id":"1801.03851","date":"2018-01-11","proceeding":null,"authors":["Christopher K. I. Williams","Charlie Nash","Alfredo Nazábal"],"abstract":"Latent variable models can be used to probabilistically \"fill-in\" missing\ndata entries. The variational autoencoder architecture (Kingma and Welling,\n2014; Rezende et al., 2014) includes a \"recognition\" or \"encoder\" network that\ninfers the latent variables given the data variables. However, it is not clear\nhow to handle missing data variables in this network. The factor analysis (FA)\nmodel is a basic autoencoder, using linear encoder and decoder networks. We\nshow how to calculate exactly the latent posterior distribution for the factor\nanalysis (FA) model in the presence of missing data, and note that this\nsolution implies that a different encoder network is required for each pattern\nof missingness. We also discuss various approximations to the exact solution.\nExperiments compare the effectiveness of various approaches to filling in the\nmissing data.","url_abs":"http://arxiv.org/abs/1801.03851v3","url_pdf":"http://arxiv.org/pdf/1801.03851v3.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":"autoencoders-and-probabilistic-inference-with","repo_url":"https://github.com/Kismuz/crypto_spread_test","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1801.03851","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}