{"id":536,"date":"2018-04-07T12:56:52","date_gmt":"2018-04-07T12:56:52","guid":{"rendered":"http:\/\/web.eecs.umich.edu\/~girasole\/?page_id=536"},"modified":"2018-04-07T12:56:52","modified_gmt":"2018-04-07T12:56:52","slug":"sparsity-and-non-linearity","status":"publish","type":"page","link":"https:\/\/web.eecs.umich.edu\/~girasole\/?page_id=536","title":{"rendered":"Sparsity and Non-Linearity"},"content":{"rendered":"<p>A great deal of work in sparse signal processing focuses on linear measurements. Applications, on the other hand, often have fundamental nonlinearities that need to be modeled. We have work studying algebraic variety models, monotonic nonlinear measurement functions, pairwise comparison or ranking data, and sparsity in deep networks.<\/p>\n<div id=\"zotpress-7ce297fa9eaac026f15827369ec79f5a\" class=\"zp-Zotpress zp-Zotpress-Bib wp-block-group\">\n\n\t\t<span class=\"ZP_API_USER_ID ZP_ATTR\">1399621<\/span>\n\t\t<span class=\"ZP_ITEM_KEY ZP_ATTR\"><\/span>\n\t\t<span class=\"ZP_COLLECTION_ID ZP_ATTR\">F7QF4T2Q<\/span>\n\t\t<span class=\"ZP_TAG_ID ZP_ATTR\"><\/span>\n\t\t<span class=\"ZP_AUTHOR ZP_ATTR\"><\/span>\n\t\t<span class=\"ZP_YEAR ZP_ATTR\"><\/span>\n        <span class=\"ZP_ITEMTYPE ZP_ATTR\"><\/span>\n\t\t<span class=\"ZP_INCLUSIVE ZP_ATTR\">1<\/span>\n\t\t<span class=\"ZP_STYLE ZP_ATTR\">chicago-author-date<\/span>\n\t\t<span class=\"ZP_LIMIT ZP_ATTR\">50<\/span>\n\t\t<span class=\"ZP_SORTBY ZP_ATTR\">date<\/span>\n\t\t<span class=\"ZP_ORDER ZP_ATTR\">desc<\/span>\n\t\t<span class=\"ZP_TITLE ZP_ATTR\"><\/span>\n\t\t<span class=\"ZP_SHOWIMAGE ZP_ATTR\"><\/span>\n\t\t<span class=\"ZP_SHOWTAGS ZP_ATTR\"><\/span>\n\t\t<span class=\"ZP_DOWNLOADABLE ZP_ATTR\"><\/span>\n\t\t<span class=\"ZP_NOTES ZP_ATTR\">1<\/span>\n\t\t<span class=\"ZP_ABSTRACT ZP_ATTR\"><\/span>\n\t\t<span class=\"ZP_CITEABLE ZP_ATTR\"><\/span>\n\t\t<span class=\"ZP_TARGET ZP_ATTR\"><\/span>\n\t\t<span class=\"ZP_URLWRAP ZP_ATTR\"><\/span>\n\t\t<span class=\"ZP_FORCENUM ZP_ATTR\"><\/span>\n        <span class=\"ZP_HIGHLIGHT ZP_ATTR\"><\/span>\n        <span class=\"ZP_POSTID ZP_ATTR\">536<\/span>\n\t\t<span class=\"ZOTPRESS_PLUGIN_URL ZP_ATTR\">https:\/\/web.eecs.umich.edu\/~girasole\/wp-content\/plugins\/zotpress\/<\/span>\n\n\t\t<div class=\"zp-List loading\">\n\t\t\t<div class=\"zp-SEO-Content\">\n\t\t\t\t<span class=\"ZP_JSON ZP_ATTR\">%7B%22status%22%3A%22success%22%2C%22updateneeded%22%3Afalse%2C%22instance%22%3Afalse%2C%22meta%22%3A%7B%22request_last%22%3A0%2C%22request_next%22%3A0%2C%22used_cache%22%3Atrue%7D%2C%22data%22%3A%5B%7B%22key%22%3A%22VJTA8NLZ%22%2C%22library%22%3A%7B%22id%22%3A1399621%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Ongie%20et%20al.%22%2C%22parsedDate%22%3A%222021-01-01%22%2C%22numChildren%22%3A2%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BOngie%2C%20Greg%2C%20Daniel%20Pimentel-Alarc%26%23xF3%3Bn%2C%20Laura%20Balzano%2C%20Rebecca%20Willett%2C%20and%20Robert%20D.%20Nowak.%202021.%20%26%23x201C%3BTensor%20Methods%20for%20Nonlinear%20Matrix%20Completion.%26%23x201D%3B%20%26lt%3Bi%26gt%3BSIAM%20Journal%20on%20Mathematics%20of%20Data%20Science%26lt%3B%5C%2Fi%26gt%3B%2C%20January%201%2C%20253%26%23x2013%3B79.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-DOIURL%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1137%5C%2F20M1323448%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1137%5C%2F20M1323448%26lt%3B%5C%2Fa%26gt%3B.%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Tensor%20Methods%20for%20Nonlinear%20Matrix%20Completion%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Greg%22%2C%22lastName%22%3A%22Ongie%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Daniel%22%2C%22lastName%22%3A%22Pimentel-Alarc%5Cu00f3n%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Laura%22%2C%22lastName%22%3A%22Balzano%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Rebecca%22%2C%22lastName%22%3A%22Willett%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Robert%20D.%22%2C%22lastName%22%3A%22Nowak%22%7D%5D%2C%22abstractNote%22%3A%22In%20the%20low-rank%20matrix%20completion%20%28LRMC%29%20problem%2C%20the%20low-rank%20assumption%20means%20that%20the%20columns%20%28or%20rows%29%20of%20the%20matrix%20to%20be%20completed%20are%20points%20on%20a%20low-dimensional%20linear%20algebraic%20variety.%20This%20paper%20extends%20this%20thinking%20to%20cases%20where%20the%20columns%20are%20points%20on%20a%20low-dimensional%20nonlinear%20algebraic%20variety%2C%20a%20problem%20we%20call%20low%20algebraic%20dimension%20matrix%20completion%20%28LADMC%29.%20Matrices%20whose%20columns%20belong%20to%20a%20union%20of%20subspaces%20are%20an%20important%20special%20case.%20We%20propose%20an%20LADMC%20algorithm%20that%20leverages%20existing%20LRMC%20methods%20on%20a%20tensorized%20representation%20of%20the%20data.%20For%20example%2C%20a%20second-order%20tensorized%20representation%20is%20formed%20by%20taking%20the%20Kronecker%20product%20of%20each%20column%20with%20itself%2C%20and%20we%20consider%20higher-order%20tensorizations%20as%20well.%20This%20approach%20will%20succeed%20in%20many%20cases%20where%20traditional%20LRMC%20is%20guaranteed%20to%20fail%20because%20the%20data%20are%20low-rank%20in%20the%20tensorized%20representation%20but%20are%20not%20in%20the%20original%20representation.%20We%20also%20provide%20a%20formal%20mathematical%20justification%20for%20the%20success%20of%20our%20method.%20In%20particular%2C%20we%20give%20bounds%20on%20the%20rank%20of%20these%20data%20in%20the%20tensorized%20representation%2C%20and%20we%20prove%20sampling%20requirements%20to%20guarantee%20uniqueness%20of%20the%20solution.%20We%20also%20provide%20experimental%20results%20showing%20that%20the%20new%20approach%20outperforms%20existing%20state-of-the-art%20methods%20for%20matrix%20completion%20under%20a%20union-of-subspaces%20model.%22%2C%22date%22%3A%22January%201%2C%202021%22%2C%22section%22%3A%22%22%2C%22partNumber%22%3A%22%22%2C%22partTitle%22%3A%22%22%2C%22DOI%22%3A%2210.1137%5C%2F20M1323448%22%2C%22citationKey%22%3A%22%22%2C%22url%22%3A%22http%3A%5C%2F%5C%2Fepubs.siam.org%5C%2Fdoi%5C%2Fabs%5C%2F10.1137%5C%2F20M1323448%22%2C%22PMID%22%3A%22%22%2C%22PMCID%22%3A%22%22%2C%22ISSN%22%3A%22%22%2C%22language%22%3A%22%22%2C%22collections%22%3A%5B%22427SEM27%22%2C%22DZFDBB6V%22%2C%22F7QF4T2Q%22%2C%22HJQ26QYG%22%2C%22WUTP7CH6%22%5D%2C%22dateModified%22%3A%222021-03-10T15%3A33%3A01Z%22%7D%7D%2C%7B%22key%22%3A%22NR6ZND2K%22%2C%22library%22%3A%7B%22id%22%3A1399621%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Zhang%20et%20al.%22%2C%22parsedDate%22%3A%222018-04-30%22%2C%22numChildren%22%3A2%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BZhang%2C%20Dejiao%2C%20Haozhu%20Wang%2C%20Mario%20Figueiredo%2C%20and%20Laura%20Balzano.%202018.%20%26%23x201C%3BLearning%20to%20Share%3A%20Simultaneous%20Parameter%20Tying%20and%20Sparsification%20in%20Deep%20Learning.%26%23x201D%3B%20%26lt%3Bi%26gt%3BInternational%20Conference%20on%20Learning%20Representations%20%28ICLR%29%26lt%3B%5C%2Fi%26gt%3B%2C%20April%2030.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-ItemURL%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fopenreview.net%5C%2Fforum%3Fid%3DrypT3fb0b%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fopenreview.net%5C%2Fforum%3Fid%3DrypT3fb0b%26lt%3B%5C%2Fa%26gt%3B.%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Learning%20to%20Share%3A%20Simultaneous%20Parameter%20Tying%20and%20Sparsification%20in%20Deep%20Learning%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Dejiao%22%2C%22lastName%22%3A%22Zhang%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Haozhu%22%2C%22lastName%22%3A%22Wang%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Mario%22%2C%22lastName%22%3A%22Figueiredo%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Laura%22%2C%22lastName%22%3A%22Balzano%22%7D%5D%2C%22abstractNote%22%3A%22Deep%20neural%20networks%20%28DNNs%29%20usually%20contain%20millions%2C%20maybe%20billions%2C%20of%20parameters%5C%2Fweights%2C%20making%20both%20storage%20and%20computation%20very%20expensive.%20This%20has%20motivated%20a%20large%20body%20of%20work%20to%20reduce...%22%2C%22date%22%3A%222018%5C%2F04%5C%2F30%22%2C%22section%22%3A%22%22%2C%22partNumber%22%3A%22%22%2C%22partTitle%22%3A%22%22%2C%22DOI%22%3A%22%22%2C%22citationKey%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fopenreview.net%5C%2Fforum%3Fid%3DrypT3fb0b%22%2C%22PMID%22%3A%22%22%2C%22PMCID%22%3A%22%22%2C%22ISSN%22%3A%22%22%2C%22language%22%3A%22%22%2C%22collections%22%3A%5B%22DZFDBB6V%22%2C%22F7QF4T2Q%22%2C%22ZA8QMDGD%22%5D%2C%22dateModified%22%3A%222018-04-16T15%3A19%3A53Z%22%7D%7D%2C%7B%22key%22%3A%22B3LAG3NC%22%2C%22library%22%3A%7B%22id%22%3A1399621%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Ganti%20et%20al.%22%2C%22parsedDate%22%3A%222017-02-13%22%2C%22numChildren%22%3A2%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BGanti%2C%20Ravi%2C%20Nikhil%20Rao%2C%20Laura%20Balzano%2C%20Rebecca%20Willett%2C%20and%20Robert%20Nowak.%202017.%20%26%23x201C%3BOn%20Learning%20High%20Dimensional%20Structured%20Single%20Index%20Models.%26%23x201D%3B%20Paper%20presented%20at%20Thirty-First%20AAAI%20Conference%20on%20Artificial%20Intelligence.%20%26lt%3Bi%26gt%3BThirty-First%20AAAI%20Conference%20on%20Artificial%20Intelligence%26lt%3B%5C%2Fi%26gt%3B%2C%20February%2013.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-ItemURL%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fwww.aaai.org%5C%2Focs%5C%2Findex.php%5C%2FAAAI%5C%2FAAAI17%5C%2Fpaper%5C%2Fview%5C%2F14480%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fwww.aaai.org%5C%2Focs%5C%2Findex.php%5C%2FAAAI%5C%2FAAAI17%5C%2Fpaper%5C%2Fview%5C%2F14480%26lt%3B%5C%2Fa%26gt%3B.%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22conferencePaper%22%2C%22title%22%3A%22On%20Learning%20High%20Dimensional%20Structured%20Single%20Index%20Models%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Ravi%22%2C%22lastName%22%3A%22Ganti%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Nikhil%22%2C%22lastName%22%3A%22Rao%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Laura%22%2C%22lastName%22%3A%22Balzano%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Rebecca%22%2C%22lastName%22%3A%22Willett%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Robert%22%2C%22lastName%22%3A%22Nowak%22%7D%5D%2C%22abstractNote%22%3A%22Single%20Index%20Models%20%28SIMs%29%20are%20simple%20yet%20flexible%20semi-parametric%20models%20for%20machine%20learning%2C%20where%20the%20response%20variable%20is%20modeled%20as%20a%20monotonic%20function%20of%20a%20linear%20combination%20of%20features.%20Estimation%20in%20this%20context%20requires%20learning%20both%20the%20feature%20weights%20and%20the%20nonlinear%20function%20that%20relates%20features%20to%20observations.%20While%20methods%20have%20been%20described%20to%20learn%20SIMs%20in%20the%20low%20dimensional%20regime%2C%20a%20method%20that%20can%20efficiently%20learn%20SIMs%20in%20high%20dimensions%2C%20and%20under%20general%20structural%20assumptions%2C%20has%20not%20been%20forthcoming.%20In%20this%20paper%2C%20we%20propose%20computationally%20efficient%20algorithms%20for%20SIM%20inference%20in%20high%20dimensions%20with%20structural%20constraints.%20Our%20general%20approach%20specializes%20to%20sparsity%2C%20group%20sparsity%2C%20and%20low-rank%20assumptions%20among%20others.%20Experiments%20show%20that%20the%20proposed%20method%20enjoys%20superior%20predictive%20performance%20when%20compared%20to%20generalized%20linear%20models%2C%20and%20achieves%20results%20comparable%20to%20or%20better%20than%20single%20layer%20feedforward%20neural%20networks%20with%20significantly%20less%20computational%20cost.%22%2C%22proceedingsTitle%22%3A%22Thirty-First%20AAAI%20Conference%20on%20Artificial%20Intelligence%22%2C%22conferenceName%22%3A%22Thirty-First%20AAAI%20Conference%20on%20Artificial%20Intelligence%22%2C%22date%22%3A%222017%5C%2F02%5C%2F13%22%2C%22eventPlace%22%3A%22%22%2C%22DOI%22%3A%22%22%2C%22ISBN%22%3A%22%22%2C%22citationKey%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fwww.aaai.org%5C%2Focs%5C%2Findex.php%5C%2FAAAI%5C%2FAAAI17%5C%2Fpaper%5C%2Fview%5C%2F14480%22%2C%22ISSN%22%3A%22%22%2C%22language%22%3A%22en%22%2C%22collections%22%3A%5B%22DZFDBB6V%22%2C%22F7QF4T2Q%22%2C%22ZA8QMDGD%22%5D%2C%22dateModified%22%3A%222018-02-16T18%3A47%3A48Z%22%7D%7D%2C%7B%22key%22%3A%229EJHAU95%22%2C%22library%22%3A%7B%22id%22%3A1399621%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Ganti%20et%20al.%22%2C%22parsedDate%22%3A%222015%22%2C%22numChildren%22%3A2%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BGanti%2C%20Ravi%20Sastry%2C%20Laura%20Balzano%2C%20and%20Rebecca%20Willett.%202015.%20%26%23x201C%3BMatrix%20Completion%20Under%20Monotonic%20Single%20Index%20Models.%26%23x201D%3B%20%26lt%3Bi%26gt%3BProceedings%20of%20the%20Conference%20for%20Advances%20in%20Neural%20Information%20Processing%20Systems%26lt%3B%5C%2Fi%26gt%3B%2C%201864%26%23x2013%3B72.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-ItemURL%26%23039%3B%20href%3D%26%23039%3Bhttp%3A%5C%2F%5C%2Fpapers.nips.cc%5C%2Fpaper%5C%2F5916-matrix-completion-under-monotonic-single-index-models%26%23039%3B%26gt%3Bhttp%3A%5C%2F%5C%2Fpapers.nips.cc%5C%2Fpaper%5C%2F5916-matrix-completion-under-monotonic-single-index-models%26lt%3B%5C%2Fa%26gt%3B.%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22conferencePaper%22%2C%22title%22%3A%22Matrix%20Completion%20Under%20Monotonic%20Single%20Index%20Models%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Ravi%20Sastry%22%2C%22lastName%22%3A%22Ganti%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Laura%22%2C%22lastName%22%3A%22Balzano%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Rebecca%22%2C%22lastName%22%3A%22Willett%22%7D%5D%2C%22abstractNote%22%3A%22Eletronic%20Proceedings%20of%20Neural%20Information%20Processing%20Systems%22%2C%22proceedingsTitle%22%3A%22Proceedings%20of%20the%20conference%20for%20Advances%20in%20Neural%20Information%20Processing%20Systems%22%2C%22conferenceName%22%3A%22Advances%20in%20Neural%20Information%20Processing%20Systems%22%2C%22date%22%3A%222015%22%2C%22eventPlace%22%3A%22%22%2C%22DOI%22%3A%22%22%2C%22ISBN%22%3A%22%22%2C%22citationKey%22%3A%22%22%2C%22url%22%3A%22http%3A%5C%2F%5C%2Fpapers.nips.cc%5C%2Fpaper%5C%2F5916-matrix-completion-under-monotonic-single-index-models%22%2C%22ISSN%22%3A%22%22%2C%22language%22%3A%22%22%2C%22collections%22%3A%5B%2284TPD646%22%2C%22DZFDBB6V%22%2C%22F7QF4T2Q%22%2C%22WUTP7CH6%22%2C%22ZA8QMDGD%22%5D%2C%22dateModified%22%3A%222015-12-29T15%3A07%3A01Z%22%7D%7D%5D%7D<\/span>\n\n\t\t\t\t<div id=\"zp-ID-536-1399621-VJTA8NLZ\" data-zp-author-date='Ongie-et-al.-2021-01-01' data-zp-date-author='2021-01-01-Ongie-et-al.' data-zp-date='2021-01-01' data-zp-year='2021' data-zp-itemtype='journalArticle' class=\"zp-Entry zpSearchResultsItem\">\n<div class=\"csl-bib-body\" style=\"line-height: 1.35; padding-left: 1em; text-indent:-1em;\">\n  <div class=\"csl-entry\">Ongie, Greg, Daniel Pimentel-Alarc\u00f3n, Laura Balzano, Rebecca Willett, and Robert D. Nowak. 2021. \u201cTensor Methods for Nonlinear Matrix Completion.\u201d <i>SIAM Journal on Mathematics of Data Science<\/i>, January 1, 253\u201379. <a class='zp-DOIURL' href='https:\/\/doi.org\/10.1137\/20M1323448'>https:\/\/doi.org\/10.1137\/20M1323448<\/a>.<\/div>\n<\/div>\n\t\t\t\t<\/div><!-- .zp-Entry .zpSearchResultsItem -->\t\t\t\t<div id=\"zp-ID-536-1399621-NR6ZND2K\" data-zp-author-date='Zhang-et-al.-2018-04-30' data-zp-date-author='2018-04-30-Zhang-et-al.' data-zp-date='2018-04-30' data-zp-year='2018' data-zp-itemtype='journalArticle' class=\"zp-Entry zpSearchResultsItem\">\n<div class=\"csl-bib-body\" style=\"line-height: 1.35; padding-left: 1em; text-indent:-1em;\">\n  <div class=\"csl-entry\">Zhang, Dejiao, Haozhu Wang, Mario Figueiredo, and Laura Balzano. 2018. \u201cLearning to Share: Simultaneous Parameter Tying and Sparsification in Deep Learning.\u201d <i>International Conference on Learning Representations (ICLR)<\/i>, April 30. <a class='zp-ItemURL' href='https:\/\/openreview.net\/forum?id=rypT3fb0b'>https:\/\/openreview.net\/forum?id=rypT3fb0b<\/a>.<\/div>\n<\/div>\n\t\t\t\t<\/div><!-- .zp-Entry .zpSearchResultsItem -->\t\t\t\t<div id=\"zp-ID-536-1399621-B3LAG3NC\" data-zp-author-date='Ganti-et-al.-2017-02-13' data-zp-date-author='2017-02-13-Ganti-et-al.' data-zp-date='2017-02-13' data-zp-year='2017' data-zp-itemtype='conferencePaper' class=\"zp-Entry zpSearchResultsItem\">\n<div class=\"csl-bib-body\" style=\"line-height: 1.35; padding-left: 1em; text-indent:-1em;\">\n  <div class=\"csl-entry\">Ganti, Ravi, Nikhil Rao, Laura Balzano, Rebecca Willett, and Robert Nowak. 2017. \u201cOn Learning High Dimensional Structured Single Index Models.\u201d Paper presented at Thirty-First AAAI Conference on Artificial Intelligence. <i>Thirty-First AAAI Conference on Artificial Intelligence<\/i>, February 13. <a class='zp-ItemURL' href='https:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI17\/paper\/view\/14480'>https:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI17\/paper\/view\/14480<\/a>.<\/div>\n<\/div>\n\t\t\t\t<\/div><!-- .zp-Entry .zpSearchResultsItem -->\t\t\t\t<div id=\"zp-ID-536-1399621-9EJHAU95\" data-zp-author-date='Ganti-et-al.-2015' data-zp-date-author='2015-Ganti-et-al.' data-zp-date='2015' data-zp-year='2015' data-zp-itemtype='conferencePaper' class=\"zp-Entry zpSearchResultsItem\">\n<div class=\"csl-bib-body\" style=\"line-height: 1.35; padding-left: 1em; text-indent:-1em;\">\n  <div class=\"csl-entry\">Ganti, Ravi Sastry, Laura Balzano, and Rebecca Willett. 2015. \u201cMatrix Completion Under Monotonic Single Index Models.\u201d <i>Proceedings of the Conference for Advances in Neural Information Processing Systems<\/i>, 1864\u201372. <a class='zp-ItemURL' href='http:\/\/papers.nips.cc\/paper\/5916-matrix-completion-under-monotonic-single-index-models'>http:\/\/papers.nips.cc\/paper\/5916-matrix-completion-under-monotonic-single-index-models<\/a>.<\/div>\n<\/div>\n\t\t\t\t<\/div><!-- .zp-Entry .zpSearchResultsItem -->\n\t\t\t<\/div><!-- .zp-zp-SEO-Content -->\n\t\t<\/div><!-- .zp-List -->\n\t<\/div><!--.zp-Zotpress-->\n\n\n","protected":false},"excerpt":{"rendered":"<p>A great deal of work in sparse signal processing focuses on linear measurements. Applications, on the other hand, often have fundamental nonlinearities that need to be modeled. We have work studying algebraic variety models, monotonic nonlinear measurement functions, pairwise comparison or ranking data, and sparsity in deep networks.<\/p>\n","protected":false},"author":3,"featured_media":0,"parent":182,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=\/wp\/v2\/pages\/536"}],"collection":[{"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=536"}],"version-history":[{"count":1,"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=\/wp\/v2\/pages\/536\/revisions"}],"predecessor-version":[{"id":537,"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=\/wp\/v2\/pages\/536\/revisions\/537"}],"up":[{"embeddable":true,"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=\/wp\/v2\/pages\/182"}],"wp:attachment":[{"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=536"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}