{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T21:51:56Z","timestamp":1766267516485,"version":"3.37.3"},"reference-count":59,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T00:00:00Z","timestamp":1588291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T00:00:00Z","timestamp":1588291200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T00:00:00Z","timestamp":1588291200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T00:00:00Z","timestamp":1588291200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS 1751636"],"award-info":[{"award-number":["DMS 1751636"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"United States - Israel Binational Science Foundation, Jerusalem, Israel","award":["2018209"],"award-info":[{"award-number":["2018209"]}]},{"name":"Kreitman High-Tech Scholarship"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Sel. Top. Signal Process."],"published-print":{"date-parts":[[2020,5]]},"DOI":"10.1109\/jstsp.2020.2972775","type":"journal-article","created":{"date-parts":[[2020,2,11]],"date-time":"2020-02-11T02:19:02Z","timestamp":1581387542000},"page":"894-904","source":"Crossref","is-referenced-by-count":9,"title":["LeanConvNets: Low-Cost Yet Effective Convolutional Neural Networks"],"prefix":"10.1109","volume":"14","author":[{"given":"Jonathan","family":"Ephrath","sequence":"first","affiliation":[{"name":"Department of Computer Sciences, the Ben-Gurion University of the Negev, Be'er Sheva, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Moshe","family":"Eliasof","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, the Ben-Gurion University of the Negev, Be'er Sheva, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lars","family":"Ruthotto","sequence":"additional","affiliation":[{"name":"Departments of Mathematics and Computer Science, Emory University, Atlanta, GA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eldad","family":"Haber","sequence":"additional","affiliation":[{"name":"Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver, BC, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5351-0966","authenticated-orcid":false,"given":"Eran","family":"Treister","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, the Ben-Gurion University of the Negev, Be'er Sheva, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","first-page":"3276","article-title":"Beyond finite layer neural networks: Bridging deep architectures and numerical differential equations","author":"lu","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/s40687-018-0148-y"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729586"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref31","first-page":"630","article-title":"Identity mappings in deep residual networks","author":"he","year":"0","journal-title":"Proc Eur Conf Comput Vision"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40304-017-0103-z","article-title":"A proposal on machine learning via dynamical systems","volume":"5","author":"weinan","year":"2017","journal-title":"Commun Math Stat"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11668"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6420\/aa9a90"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1137\/16M1080173"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00353"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00951"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2018.8489312"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553486"},{"key":"ref1","first-page":"396","article-title":"Handwritten digit recognition with a back-propagation network","author":"lecun","year":"0","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref20","article-title":"Pruning convolutional neural networks for resource efficient transfer learning","author":"molchanov","year":"0","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","year":"2017","author":"howard","key":"ref21"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00716"},{"article-title":"Design of efficient convolutional layers using single intra-channel convolution, topological subdivisioning and spatial “bottleneck“ structure","year":"2016","author":"wang","key":"ref23"},{"key":"ref26","article-title":"MixConv: Mixed depthwise convolutional Kernels","author":"tan","year":"0","journal-title":"Proc British Mach Vision Conf"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_8"},{"year":"0","key":"ref50"},{"key":"ref51","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"0","journal-title":"Proc Int Conf Med Image Comput Comput Assisted Intervention"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1145\/2727040.2727041"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1145\/3240765.3243479"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref56","first-page":"2999","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2017","journal-title":"IEEE Int Conf Comput Vision"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126343"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.353"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref52","article-title":"Rethinking atrous convolution for semantic image segmentation","author":"chen","year":"2017","journal-title":"arXiv 1706 05587"},{"key":"ref10","first-page":"1379","article-title":"Dynamic network surgery for efficient dnns","author":"guo","year":"0","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref11","article-title":"Pruning filters for efficient ConvNets","author":"li","year":"0","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref40","first-page":"6571","article-title":"Neural ordinary differential equations","author":"chen","year":"0","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.541"},{"article-title":"Layer-compensated pruning for resource-constrained convolutional neural networks","year":"2018","author":"chin","key":"ref13"},{"key":"ref14","first-page":"2074","article-title":"Learning structured sparsity in deep neural networks","author":"wen","year":"0","journal-title":"Proc Advances Neural Inf Process Syst"},{"article-title":"The power of sparsity in convolutional neural networks","year":"2017","author":"changpinyo","key":"ref15"},{"key":"ref16","article-title":"DSD: Dense-sparse-dense training for deep neural networks","author":"han","year":"0","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref17","first-page":"4107","article-title":"Binarized neural networks","author":"hubara","year":"0","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref18","first-page":"5811","article-title":"Training quantized nets: A deeper understanding","author":"li","year":"0","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref19","first-page":"5145","article-title":"Scalable methods for 8-bit training of neural networks","author":"banner","year":"0","journal-title":"Proc Advances Neural Inf Process Sys"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2019.2908700"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2019.2913965"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.10.013"},{"key":"ref5","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"61","author":"krizhevsky","year":"2012","journal-title":"Advances Neural Inf Process Syst"},{"key":"ref8","first-page":"164","article-title":"Second order derivatives for network pruning: Optimal brain surgeon reconstruction","volume":"5","author":"hassibi","year":"1992","journal-title":"Int J Comput Vision"},{"journal-title":"Deep Learning","year":"2016","author":"goodfellow","key":"ref7"},{"year":"0","key":"ref49"},{"key":"ref9","first-page":"1135","article-title":"Learning both weights and connections for efficient neural network","volume":"5","author":"han","year":"2015","journal-title":"Int J Comput Vision"},{"key":"ref46","article-title":"Automatic differentiation in pytorch","author":"paszke","year":"0","journal-title":"Proc Advances Neural Inf Process Syst"},{"year":"0","key":"ref45"},{"key":"ref48","first-page":"215","article-title":"An analysis of single-layer networks in unsupervised feature learning","author":"coates","year":"0","journal-title":"Proc 14th AISTATS"},{"article-title":"Learning multiple layers of features from tiny images","year":"2009","author":"krizhevsky","key":"ref47"},{"key":"ref42","first-page":"1","article-title":"Deep neural networks motivated by partial differential equations","author":"ruthotto","year":"2018","journal-title":"J Math Imag Vision"},{"key":"ref41","article-title":"IMEXnet: A forward stable deep neural network","author":"haber","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"year":"0","key":"ref44"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.11231"}],"container-title":["IEEE Journal of Selected Topics in Signal Processing"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/4200690\/9163420\/8989808-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/4200690\/9163420\/08989808.pdf?arnumber=8989808","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T19:41:04Z","timestamp":1657827664000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8989808\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5]]},"references-count":59,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/jstsp.2020.2972775","relation":{},"ISSN":["1932-4553","1941-0484"],"issn-type":[{"type":"print","value":"1932-4553"},{"type":"electronic","value":"1941-0484"}],"subject":[],"published":{"date-parts":[[2020,5]]}}}