{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T20:55:07Z","timestamp":1730235307100,"version":"3.28.0"},"reference-count":15,"publisher":"IEEE","license":[{"start":{"date-parts":[[2017,7,1]],"date-time":"2017-07-01T00:00:00Z","timestamp":1498867200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2017,7,1]],"date-time":"2017-07-01T00:00:00Z","timestamp":1498867200000},"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":[[2017,7]]},"DOI":"10.1109\/icci-cc.2017.8109731","type":"proceedings-article","created":{"date-parts":[[2017,11,16]],"date-time":"2017-11-16T21:49:41Z","timestamp":1510868981000},"page":"65-70","source":"Crossref","is-referenced-by-count":4,"title":["Transfer learning for brain decoding using deep architectures"],"prefix":"10.1109","author":[{"given":"Burak","family":"Velioglu","sequence":"first","affiliation":[{"name":"Computer Engineering, Middle East Technical University, Ankara, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fatos T. Yarman","family":"Vural","sequence":"additional","affiliation":[{"name":"Computer Engineering, Middle East Technical University, Ankara, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1016\/j.media.2014.03.005","article-title":"Joint maximum likelihood estimation of activation and hemodynamic response function for fmri","volume":"18","author":"negar","year":"2014","journal-title":"Medical Image Analysis"},{"journal-title":"Mesh Learning for Classifying Cognitive Processes","year":"2012","author":"mete","key":"ref11"},{"key":"ref12","first-page":"3371","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"pascal","year":"2010","journal-title":"The Journal of Machine Learning Research"},{"journal-title":"Convex optimization","year":"2004","author":"stephen","key":"ref13"},{"journal-title":"ADADELTA An Adaptive Learning Rate Method","year":"2012","author":"matthew","key":"ref14"},{"key":"ref15","article-title":"Torch7: A matlab-like environment for machine learning","author":"ronan","year":"2011","journal-title":"BigLearn NIPS Workshop"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000006","article-title":"Learning deep architectures for ai. Foundations and trends","volume":"2","author":"yoshua","year":"2009","journal-title":"Machine Learning"},{"key":"ref3","first-page":"5","article-title":"Modeling voxel connectivity for brain decoding","author":"itir","year":"2015","journal-title":"Pattern Recognition in Neuroimaging"},{"journal-title":"Cobre Group Mr data from 75 schizophrenia and 72 healthy subjects","year":"2012","key":"ref6"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.05.041"},{"journal-title":"Deep learning for neuroimaging a validation study","year":"2013","author":"sergey","key":"ref8"},{"journal-title":"Deep learning of fmri big data a novel approach to subject-transfer decoding","year":"2015","author":"sotetsu","key":"ref7"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"3863","DOI":"10.1073\/pnas.0600244103","article-title":"Information-based functional brain mapping","volume":"103","author":"nikolaus","year":"2006","journal-title":"Proceedings of the National Academy of Sciences of the USA"},{"key":"ref1","doi-asserted-by":"crossref","first-page":"199s","DOI":"10.1016\/j.neuroimage.2008.11.007","article-title":"Machine learning classifiers and fmri: A tutorial overview","volume":"45","author":"francisco","year":"2009","journal-title":"NeuroImage"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.neuroimage.2015.05.018","article-title":"Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia","volume":"124","author":"junghoe","year":"2016","journal-title":"NeuroImage"}],"event":{"name":"2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","start":{"date-parts":[[2017,7,26]]},"location":"Oxford, UK","end":{"date-parts":[[2017,7,28]]}},"container-title":["2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8100805\/8109722\/08109731.pdf?arnumber=8109731","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T19:21:47Z","timestamp":1678389707000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8109731\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,7]]},"references-count":15,"URL":"https:\/\/doi.org\/10.1109\/icci-cc.2017.8109731","relation":{},"subject":[],"published":{"date-parts":[[2017,7]]}}}