{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"AUAI Press","status":"public","publist_id":"4381","date_published":"2013-07-11T00:00:00Z","pubrep_id":"137","author":[{"last_name":"Quadrianto","first_name":"Novi","full_name":"Quadrianto, Novi"},{"orcid":"0000-0003-0192-9308","id":"2EA6D09E-F248-11E8-B48F-1D18A9856A87","full_name":"Sharmanska, Viktoriia","first_name":"Viktoriia","last_name":"Sharmanska"},{"last_name":"Knowles","first_name":"David","full_name":"Knowles, David"},{"full_name":"Ghahramani, Zoubin","first_name":"Zoubin","last_name":"Ghahramani"}],"publication_identifier":{"isbn":["9780974903996"]},"language":[{"iso":"eng"}],"oa":1,"title":"The supervised IBP: Neighbourhood preserving infinite latent feature models","ddc":["000"],"_id":"2520","date_created":"2018-12-11T11:58:09Z","has_accepted_license":"1","scopus_import":1,"publication":"Proceedings of the 29th conference uncertainty in Artificial Intelligence","abstract":[{"text":"We propose a probabilistic model to infer supervised latent variables in\r\nthe Hamming space from observed data. Our model allows simultaneous\r\ninference of the number of binary latent variables, and their values. The\r\nlatent variables preserve neighbourhood structure of the data in a sense\r\nthat objects in the same semantic concept have similar latent values, and\r\nobjects in different concepts have dissimilar latent values. We formulate\r\nthe supervised infinite latent variable problem based on an intuitive\r\nprinciple of pulling objects together if they are of the same type, and\r\npushing them apart if they are not. We then combine this principle with a\r\nflexible Indian Buffet Process prior on the latent variables. We show that\r\nthe inferred supervised latent variables can be directly used to perform a\r\nnearest neighbour search for the purpose of retrieval.  We introduce a new\r\napplication of dynamically extending hash codes, and show how to\r\neffectively couple the structure of the hash codes with continuously\r\ngrowing structure of the neighbourhood preserving infinite latent feature\r\nspace.","lang":"eng"}],"file_date_updated":"2020-07-14T12:45:42Z","day":"11","quality_controlled":"1","conference":{"end_date":"2013-07-15","location":"Bellevue, WA, United States","name":"UAI: Uncertainty in Artificial Intelligence","start_date":"2013-07-11"},"year":"2013","type":"conference","month":"07","publication_status":"published","department":[{"_id":"ChLa"}],"citation":{"apa":"Quadrianto, N., Sharmanska, V., Knowles, D., &#38; Ghahramani, Z. (2013). The supervised IBP: Neighbourhood preserving infinite latent feature models. In <i>Proceedings of the 29th conference uncertainty in Artificial Intelligence</i> (pp. 527–536). Bellevue, WA, United States: AUAI Press.","mla":"Quadrianto, Novi, et al. “The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models.” <i>Proceedings of the 29th Conference Uncertainty in Artificial Intelligence</i>, AUAI Press, 2013, pp. 527–36.","ieee":"N. Quadrianto, V. Sharmanska, D. Knowles, and Z. Ghahramani, “The supervised IBP: Neighbourhood preserving infinite latent feature models,” in <i>Proceedings of the 29th conference uncertainty in Artificial Intelligence</i>, Bellevue, WA, United States, 2013, pp. 527–536.","ista":"Quadrianto N, Sharmanska V, Knowles D, Ghahramani Z. 2013. The supervised IBP: Neighbourhood preserving infinite latent feature models. Proceedings of the 29th conference uncertainty in Artificial Intelligence. UAI: Uncertainty in Artificial Intelligence, 527–536.","short":"N. Quadrianto, V. Sharmanska, D. Knowles, Z. Ghahramani, in:, Proceedings of the 29th Conference Uncertainty in Artificial Intelligence, AUAI Press, 2013, pp. 527–536.","chicago":"Quadrianto, Novi, Viktoriia Sharmanska, David Knowles, and Zoubin Ghahramani. “The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models.” In <i>Proceedings of the 29th Conference Uncertainty in Artificial Intelligence</i>, 527–36. AUAI Press, 2013.","ama":"Quadrianto N, Sharmanska V, Knowles D, Ghahramani Z. The supervised IBP: Neighbourhood preserving infinite latent feature models. In: <i>Proceedings of the 29th Conference Uncertainty in Artificial Intelligence</i>. AUAI Press; 2013:527-536."},"oa_version":"Submitted Version","page":"527 - 536","file":[{"file_id":"5134","date_created":"2018-12-12T10:15:16Z","relation":"main_file","creator":"system","access_level":"open_access","file_name":"IST-2013-137-v1+1_QuaShaKnoGha13.pdf","date_updated":"2020-07-14T12:45:42Z","checksum":"325f20c4b926bd74d39006b97df572bd","file_size":1117100,"content_type":"application/pdf"}],"date_updated":"2023-02-23T10:46:36Z"}