{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T15:41:26Z","timestamp":1768837286462,"version":"3.49.0"},"reference-count":37,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Grant-in-Aid from the Japan Society for the Promotion of Science (JSPS), Japan","doi-asserted-by":"publisher","award":["18H04113"],"award-info":[{"award-number":["18H04113"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hong Kong Research Grants Council (RGC) General Research Fund","award":["17301519"],"award-info":[{"award-number":["17301519"]}]},{"name":"Institute of Mathematical Research"},{"DOI":"10.13039\/501100003803","name":"Research Assessment Exercise (RAE) Research fund from Faculty of Science, The University of Hong Kong","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003803","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1109\/tnnls.2021.3104646","type":"journal-article","created":{"date-parts":[[2021,8,24]],"date-time":"2021-08-24T20:05:20Z","timestamp":1629835520000},"page":"921-931","source":"Crossref","is-referenced-by-count":3,"title":["On the Compressive Power of Boolean Threshold Autoencoders"],"prefix":"10.1109","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6832-0102","authenticated-orcid":false,"given":"Avraham A.","family":"Melkman","sequence":"first","affiliation":[{"name":"Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6905-8591","authenticated-orcid":false,"given":"Sini","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Mathematics, The University of Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5785-3210","authenticated-orcid":false,"given":"Wai-Ki","family":"Ching","sequence":"additional","affiliation":[{"name":"Department of Mathematics, The University of Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2056-5757","authenticated-orcid":false,"given":"Pengyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9763-797X","authenticated-orcid":false,"given":"Tatsuya","family":"Akutsu","sequence":"additional","affiliation":[{"name":"Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S0364-0213(85)80012-4"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(89)90014-2"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1126\/science.1127647"},{"key":"ref4","first-page":"37","article-title":"Autoencoders, unsupervised learning, and deep architectures","volume-title":"Proc. JMLR, Workshop Conf.","volume":"27","author":"Baldi"},{"key":"ref5","article-title":"Auto-encoding variational Bayes","author":"Kingma","year":"2013","journal-title":"arXiv:1312.6114"},{"key":"ref6","article-title":"Tutorial on variational autoencoders","author":"Doersch","year":"2016","journal-title":"arXiv:1606.05908"},{"key":"ref7","article-title":"Recent advances in autoencoder-based representation learning","author":"Tschannen","year":"2018","journal-title":"arXiv:1812.05069"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.7b00572"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.cogsys.2018.07.004"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/IWCMC.2018.8450511"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2019.2938345"},{"key":"ref12","first-page":"2514","article-title":"Uncertainty autoencoders: Learning compressed representations via variational information maximization","volume-title":"Proc. 22nd Int. Conf. Artif. Intell. Statist.","author":"Grover"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/MIPR.2019.00087"},{"key":"ref14","first-page":"666","article-title":"Shallow vs. deep sum-product networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Delalleau"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969153"},{"key":"ref16","article-title":"On the compressive power of deep rectifier networks for high resolution representation of class boundaries","author":"An","year":"2017","journal-title":"arXiv:1708.07244"},{"key":"ref17","first-page":"1","article-title":"Understanding deep learning requires rethinking generalization","volume-title":"Proc. ICLR","author":"Zhang"},{"key":"ref18","first-page":"15660","article-title":"Are deep ResNets provably better than linear predictors?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Yun"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1137\/20M1314884"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/0022-5193(69)90015-0"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1142\/10801"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-85729-097-7"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2449274"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2461012"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1049\/iet-cta.2016.1659"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2826075"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3027599"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2442593"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898718539"},{"key":"ref30","volume-title":"Discrete Neural Computation: A Theoretical Foundation","author":"Siu","year":"1995"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/12.106225"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TEC.1961.5219146"},{"key":"ref33","first-page":"3123","article-title":"BinaryConnect: Training deep neural networks with binary weights during propagations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"28","author":"Courbariaux"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"ref35","first-page":"4107","article-title":"Binarized neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"29","author":"Hubara"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107281"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1587\/transinf.2018RCP0002"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10036162\/09521549.pdf?arnumber=9521549","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T23:16:16Z","timestamp":1705014976000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9521549\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2]]},"references-count":37,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2021.3104646","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2]]}}}