{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:41:49Z","timestamp":1778085709664,"version":"3.51.4"},"reference-count":63,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000183","name":"Army Research Office","doi-asserted-by":"publisher","award":["W911NF2310062"],"award-info":[{"award-number":["W911NF2310062"]}],"id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-21-1-2379"],"award-info":[{"award-number":["N00014-21-1-2379"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NSF Award","award":["CNS-2008824"],"award-info":[{"award-number":["CNS-2008824"]}]},{"name":"6G@UT Center within the Wireless Networking and Communications Group"},{"name":"iMAGiNE Consortium at the University of Texas at Austin"},{"name":"NSF","award":["AF 1901292"],"award-info":[{"award-number":["AF 1901292"]}]},{"name":"NSF","award":["CNS 2148141"],"award-info":[{"award-number":["CNS 2148141"]}]},{"name":"NSF","award":["Grant Tripods CCF 1934932"],"award-info":[{"award-number":["Grant Tripods CCF 1934932"]}]},{"name":"NSF","award":["IFML CCF 2019844"],"award-info":[{"award-number":["IFML CCF 2019844"]}]},{"name":"Research Gifts by Western Digital, Amazon, WNCG IAP, UT Austin Machine Learning Lab (MLL), Cisco and the Stanly P. Finch Centennial Professorship in Engineering"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Sel. Areas Inf. Theory"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/jsait.2024.3412976","type":"journal-article","created":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T17:46:23Z","timestamp":1718387183000},"page":"493-508","source":"Crossref","is-referenced-by-count":9,"title":["Neural Distributed Source Coding"],"prefix":"10.1109","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0084-5933","authenticated-orcid":false,"given":"Jay","family":"Whang","sequence":"first","affiliation":[{"name":"Gemini Team, Google DeepMind, Mountain View, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2419-3830","authenticated-orcid":false,"given":"Alliot","family":"Nagle","sequence":"additional","affiliation":[{"name":"ECE Department, The University of Texas at Austin, Austin, TX, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3518-0510","authenticated-orcid":false,"given":"Anish","family":"Acharya","sequence":"additional","affiliation":[{"name":"ECE Department, The University of Texas at Austin, Austin, TX, USA"}]},{"given":"Hyeji","family":"Kim","sequence":"additional","affiliation":[{"name":"ECE Department, The University of Texas at Austin, Austin, TX, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4244-7033","authenticated-orcid":false,"given":"Alexandros G.","family":"Dimakis","sequence":"additional","affiliation":[{"name":"ECE Department, The University of Texas at Austin, Austin, TX, USA"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1973.1055037"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1975.1055356"},{"key":"ref3","first-page":"171","article-title":"Multiterminal source coding","volume-title":"The Information Theory Approach to Communications","author":"Berger","year":"1978"},{"key":"ref4","article-title":"Multiterminal source coding","author":"Tung","year":"1978"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2011.2145570"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2008.920343"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2013.2288257"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/18.669162"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/18.490552"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2004.1365154"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2005.850110"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139030687"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1976.1055508"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139030687"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/DCC.1999.755665"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ACSSC.2003.1292028"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/18.720531"},{"key":"ref18","first-page":"6309","article-title":"Neural discrete representation learning","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"van den Oord"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ACSSC.2003.1292216"},{"key":"ref20","article-title":"Auto-encoding variational bayes","volume-title":"2nd Int. Conf. Learn. Represent.","author":"Kingma"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1561\/2200000056"},{"key":"ref22","first-page":"1","article-title":"Practical lossless compression with latent variables using bits back coding","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Townsend"},{"key":"ref23","article-title":"End-to-end optimized image compression","author":"Ball\u00e9","year":"2017","journal-title":"arXiv:1611.01704"},{"key":"ref24","first-page":"1","article-title":"Variational image compression with a scale hyperprior","volume-title":"Proc. 6th Int. Conf. Learn. Represent.","author":"Ball\u00e9"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/dcc52660.2022.00026"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00253"},{"key":"ref27","first-page":"1278","article-title":"Stochastic backpropagation and approximate inference in deep generative models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Rezende"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/icassp.2019.8683277"},{"key":"ref29","first-page":"1","article-title":"Generating diverse high-fidelity images with VQ-VAE-2","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Razavi"},{"key":"ref30","first-page":"4364","article-title":"A hierarchical latent vector model for learning long-term structure in music","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Roberts"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2102.12092"},{"key":"ref32","article-title":"Scaling autoregressive models for content-rich text-to-image generation","author":"Yu","year":"2022","journal-title":"arXiv:2206.10789"},{"key":"ref33","first-page":"4797","article-title":"Conditional image generation with PixelCNN decoders","volume-title":"Proc. 30th Int. Conf. Neural Inf. Process. Syst.","author":"Oord"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1147\/rd.282.0135"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58520-4_41"},{"key":"ref37","first-page":"1","article-title":"LDMIC: Learning-based distributed multi-view image coding","volume-title":"Proc. 11th Int. Conf. Learn. Represent.","author":"Zhang"},{"key":"ref38","article-title":"Neural distributed compressor discovers binning","author":"Ozyilkan","year":"2023","journal-title":"arXiv:2310.16961"},{"key":"ref39","first-page":"10794","article-title":"Joint autoregressive and hierarchical priors for learned image compression","volume-title":"Proc. 32nd Int. Conf. Neural Inf. Process. Syst.","author":"Minnen"},{"key":"ref40","article-title":"Lossy image compression with compressive autoencoders","author":"Theis","year":"2017","journal-title":"arXiv:1703.00395"},{"issue":"2","key":"ref41","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1109\/JSTSP.2020.3034501","article-title":"Nonlinear transform coding","volume":"15","author":"Ball\u00e9","year":"2021","journal-title":"IEEE Trans. Special Topics Signal Process."},{"key":"ref42","first-page":"864","article-title":"PixelSNAIL: An improved autoregressive generative model","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.5194\/isprsannals-II-3-W5-427-2015"},{"key":"ref45","first-page":"53","article-title":"SSIM image quality metric for denoised images","volume-title":"Proc. 3rd WSEAS Int. Conf. Vis., Imag. Simul.","author":"Ndajah"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref47","first-page":"10215","article-title":"Glow: Generative flow with invertible 1x1 convolutions","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Kingma"},{"key":"ref48","volume-title":"MNIST Handwritten Digit Database","author":"LeCun","year":"2010"},{"key":"ref49","first-page":"9850","article-title":"ATOMO: Communication-efficient learning via atomic sparsification","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref50","article-title":"Decentralized deep learning with arbitrary communication compression","author":"Koloskova","year":"2019","journal-title":"arXiv:1907.09356"},{"key":"ref51","article-title":"Understanding top-k sparsification in distributed deep learning","author":"Shi","year":"2019","journal-title":"arXiv:1911.08772"},{"key":"ref52","first-page":"4447","article-title":"Sparsified SGD with memory","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Stich"},{"key":"ref53","first-page":"1709","article-title":"QSGD: Communication-efficient SGD via gradient quantization and encoding","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Alistarh"},{"key":"ref54","first-page":"14668","article-title":"Qsparse-local-SGD: Distributed SGD with quantization, sparsification and local computations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Basu"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2019.00220"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref57","first-page":"3310","article-title":"VEEGAN: Reducing mode collapse in GANs using implicit variational learning","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Srivastava"},{"key":"ref58","article-title":"Unrolled generative adversarial networks","author":"Metz","year":"2016","journal-title":"arXiv:1611.02163"},{"key":"ref59","volume-title":"IEEE Draft Standard for Information Technology\u2014Telecommunications and Information Exchange Between Systems Local and Metropolitan Area Networks\u2014Specific Requirements\u2014Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications","year":"2018"},{"key":"ref60","volume-title":"LDPC encoding and LDPC quasicyclicmatrix","year":"2021"},{"key":"ref61","first-page":"1","article-title":"Turbo autoencoder: Deep learning based channel codes for point-to-point communication channels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Jiang"},{"key":"ref62","first-page":"7368","article-title":"KO codes: Inventing nonlinear encoding and decoding for reliable wireless communication via deep-learning","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Makkuva"},{"key":"ref63","first-page":"3898","article-title":"ProductAE: Toward training larger channel codes based on neural product codes","volume-title":"Proc. IEEE Int. Conf. Commun.","author":"Jamali"}],"container-title":["IEEE Journal on Selected Areas in Information Theory"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/8700143\/10461668\/10557705-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/8700143\/10461668\/10557705.pdf?arnumber=10557705","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T21:04:45Z","timestamp":1736543085000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10557705\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":63,"URL":"https:\/\/doi.org\/10.1109\/jsait.2024.3412976","relation":{},"ISSN":["2641-8770"],"issn-type":[{"value":"2641-8770","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}