{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,20]],"date-time":"2025-07-20T04:16:20Z","timestamp":1752984980762,"version":"3.41.0"},"reference-count":58,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62103363"],"award-info":[{"award-number":["62103363"]}],"id":[{"id":"10.13039\/501100001809","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":[[2025,6]]},"DOI":"10.1109\/tnnls.2024.3485115","type":"journal-article","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T17:35:32Z","timestamp":1730396132000},"page":"10325-10333","source":"Crossref","is-referenced-by-count":1,"title":["Hyperbolic Binary Neural Network"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6568-8801","authenticated-orcid":false,"given":"Jun","family":"Chen","sequence":"first","affiliation":[{"name":"National Special Education Resource Center for Children with Autism, Zhejiang Normal University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5350-1528","authenticated-orcid":false,"given":"Jingyang","family":"Xiang","sequence":"additional","affiliation":[{"name":"Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3579-371X","authenticated-orcid":false,"given":"Tianxin","family":"Huang","sequence":"additional","affiliation":[{"name":"Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0129-1933","authenticated-orcid":false,"given":"Xiangrui","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4822-8939","authenticated-orcid":false,"given":"Yong","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"25","author":"Krizhevsky"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref7","article-title":"Global sparse momentum SGD for pruning very deep neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Ding"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00160"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00540"},{"key":"ref10","article-title":"Scalable methods for 8-bit training of neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Banner"},{"key":"ref11","article-title":"Latent weights do not exist: Rethinking binarized neural network optimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Helwegen"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2020.2966327"},{"key":"ref13","article-title":"Learning discretized neural networks under Ricci flow","author":"Chen","year":"2023","journal-title":"arXiv:2302.03390"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109780"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3238337"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2980041"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1561\/2200000050"},{"key":"ref18","first-page":"2809","article-title":"Mirror descent view for neural network quantization","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Ajanthan"},{"key":"ref19","first-page":"7474","article-title":"Rotated binary neural network","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Lin"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1017\/cbo9781139019750.003"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1515\/9781400830244"},{"article-title":"Decentralized Riemannian conjugate gradient method on the stiefel manifold","volume-title":"Proc. 12th Int. Conf. Learn. Represent.","author":"Chen","key":"ref22"},{"key":"ref23","first-page":"1969","article-title":"Orthogonal recurrent neural networks with scaled Cayley transform","volume-title":"Proc. Int. Conf. Mach. Learn. (PMLR)","author":"Helfrich"},{"key":"ref24","first-page":"3794","article-title":"Cheap orthogonal constraints in neural networks: A simple parametrization of the orthogonal and unitary group","volume-title":"Proc. Int. Conf. Mach. Learn. (PMLR)","author":"Lezcano-Casado"},{"key":"ref25","article-title":"Trivializations for gradient-based optimization on manifolds","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Casado"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"ref27","article-title":"XNOR-Net++: Improved binary neural networks","author":"Bulat","year":"2019","journal-title":"arXiv:1909.13863"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01227-8"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58580-8_14"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00232"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00515"},{"key":"ref32","article-title":"Weight normalization: A simple reparameterization to accelerate training of deep neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"29","author":"Salimans"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.305"},{"volume-title":"Riemannian Geometry","year":"2006","author":"Petersen","key":"ref34"},{"key":"ref35","article-title":"Binarized neural networks: Training deep neural networks with weights and activations constrained to + or -","author":"Courbariaux","year":"2016","journal-title":"arXiv:1602.02830"},{"issue":"1","key":"ref36","first-page":"2146","article-title":"Neural networks for machine learning","volume":"264","author":"Hinton","year":"2012","journal-title":"Coursera, Video Lectures"},{"key":"ref37","article-title":"Estimating or propagating gradients through stochastic neurons for conditional computation","author":"Bengio","year":"2013","journal-title":"arXiv:1308.3432"},{"volume-title":"Hyperbolic Geometry","year":"2006","author":"Anderson","key":"ref38"},{"key":"ref39","article-title":"Hyperbolic neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Ganea"},{"key":"ref40","article-title":"Poincar\u00e9 embeddings for learning hierarchical representations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Nickel"},{"key":"ref41","first-page":"1646","article-title":"Hyperbolic entailment cones for learning hierarchical embeddings","volume-title":"Proc. Int. Conf. Mach. Learn. (PMLR)","author":"Ganea"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/S0898-1221(01)85012-4"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.2200\/S00175ED1V01Y200901MAS004"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-26654-1"},{"volume-title":"Learning multiple layers of features from tiny images","year":"2009","author":"Krizhevsky","key":"ref45"},{"key":"ref46","first-page":"10717","article-title":"Accelerate CNNs from three dimensions: A comprehensive pruning framework","volume-title":"Proc. Int. Conf. Mach. Learn. (PMLR)","author":"Wang"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20083-0_35"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01564"},{"key":"ref49","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":"ref50","article-title":"DoReFa-Net: Training low bitwidth convolutional neural networks with low bitwidth gradients","author":"Zhou","year":"2016","journal-title":"arXiv:1606.06160"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01167"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00495"},{"key":"ref53","first-page":"4091","article-title":"Searching for low-bit weights in quantized neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Yang"},{"key":"ref54","article-title":"Towards accurate binary convolutional neural network","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Lin"},{"key":"ref55","first-page":"25553","article-title":"Learning frequency domain approximation for binary neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Xu"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26261"},{"key":"ref57","first-page":"4017","article-title":"Training binary neural networks through learning with noisy supervision","volume-title":"Proc. Int. Conf. Mach. Learn. (PMLR)","author":"Han"},{"key":"ref58","article-title":"Visualizing the loss landscape of neural nets","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Li"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/5962385\/11022714\/10740481.pdf?arnumber=10740481","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T17:58:36Z","timestamp":1749059916000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10740481\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":58,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2024.3485115","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"type":"print","value":"2162-237X"},{"type":"electronic","value":"2162-2388"}],"subject":[],"published":{"date-parts":[[2025,6]]}}}