{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T10:11:14Z","timestamp":1730283074803,"version":"3.28.0"},"reference-count":18,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T00:00:00Z","timestamp":1635120000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T00:00:00Z","timestamp":1635120000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,25]]},"DOI":"10.1109\/mlsp52302.2021.9596395","type":"proceedings-article","created":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T22:53:58Z","timestamp":1637016838000},"page":"1-6","source":"Crossref","is-referenced-by-count":0,"title":["A General Parametrization Framework for Pairwise Markov Models: An Application to Unsupervised Image Segmentation"],"prefix":"10.1109","author":[{"given":"Hugo","family":"Gangloff","sequence":"first","affiliation":[{"name":"Samovar, Telecom Sudparis, Institut Polytechnique de Paris"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katherine","family":"Morales","sequence":"additional","affiliation":[{"name":"Samovar, Telecom Sudparis, Institut Polytechnique de Paris"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yohan","family":"Petetin","sequence":"additional","affiliation":[{"name":"Samovar, Telecom Sudparis, Institut Polytechnique de Paris"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W16-5907"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2004.832015"},{"key":"ref12","article-title":"Learning stochastic recurrent networks","author":"bayer","year":"2014","journal-title":"ArXiv Preprint"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/5.18626"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.2307\/2289127"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maxi-mum likelihood from incomplete data via the EM algorithm","volume":"39","author":"dempster","year":"1977","journal-title":"Journal of the Royal Statistical Society Series B (Methodological)"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1214\/16-AOS1435"},{"journal-title":"Space-Filling Curves","year":"2012","author":"sagan","key":"ref17"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-020-00608-w","article-title":"An annotated fluorescence image dataset for training nuclear segmentation methods","volume":"7","author":"kromp","year":"2020","journal-title":"Scientific Data"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2019.05.005"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2017.12.006"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(89)90020-8"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2021.107178"},{"key":"ref8","article-title":"Auto-Encoding Variational Bayes","author":"kingma","year":"2014","journal-title":"2nd International Conference on Learning Representations ICLR 2014"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1038\/323533a0"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2003.1195998"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.5772\/601"},{"key":"ref9","article-title":"Composing graphical models with neural networks for structured representations and fast inference","author":"johnson","year":"2016","journal-title":"ArXiv Preprint"}],"event":{"name":"2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)","start":{"date-parts":[[2021,10,25]]},"location":"Gold Coast, Australia","end":{"date-parts":[[2021,10,28]]}},"container-title":["2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9596063\/9596068\/09596395.pdf?arnumber=9596395","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T23:30:12Z","timestamp":1659483012000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9596395\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,25]]},"references-count":18,"URL":"https:\/\/doi.org\/10.1109\/mlsp52302.2021.9596395","relation":{},"subject":[],"published":{"date-parts":[[2021,10,25]]}}}