{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:31:52Z","timestamp":1723015912864},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,8]]},"abstract":"<jats:p>We address the problem of online model adaptation when learning representations from non-stationary data streams. Specifically, we focus here on online dictionary learning (i.e. sparse linear autoencoder), and propose a simple but effective online model selection approach involving \u201cbirth\u201d (addition) and \u201cdeath\u201d (removal) of hidden units representing dictionary\n\nelements, in response to changing inputs; we draw inspiration from the adult neurogenesis phenomenon in the dentate gyrus of the hippocampus, known to be associated with better adaptation to new environments. Empirical evaluation on real-life datasets (images and text), as well as on synthetic data, demonstrates that the proposed approach can considerably outperform the state-of-art non-adaptive online sparse coding of [Mairal et al., 2009] in the presence of non-stationary data. Moreover, we identify certain data- and model properties\n\nassociated with such improvements.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/235","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T05:14:07Z","timestamp":1501218847000},"page":"1696-1702","source":"Crossref","is-referenced-by-count":5,"title":["Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World"],"prefix":"10.24963","author":[{"given":"Sahil","family":"Garg","sequence":"first","affiliation":[{"name":"IBM Thomas J. Watson Research Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Irina","family":"Rish","sequence":"additional","affiliation":[{"name":"IBM Thomas J. Watson Research Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guillermo","family":"Cecchi","sequence":"additional","affiliation":[{"name":"IBM Thomas J. Watson Research Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aurelie","family":"Lozano","sequence":"additional","affiliation":[{"name":"IBM Thomas J. Watson Research Center"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"acronym":"IJCAI-2017","name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","start":{"date-parts":[[2017,8,19]]},"theme":"Artificial Intelligence","location":"Melbourne, Australia","end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T07:52:56Z","timestamp":1501228376000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/235"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/235","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}