{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T05:25:20Z","timestamp":1730265920558,"version":"3.28.0"},"reference-count":30,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7]]},"DOI":"10.1109\/ijcnn48605.2020.9207698","type":"proceedings-article","created":{"date-parts":[[2020,9,30]],"date-time":"2020-09-30T00:40:33Z","timestamp":1601426433000},"page":"1-8","source":"Crossref","is-referenced-by-count":2,"title":["PSO-PS:Parameter Synchronization with Particle Swarm Optimization for Distributed Training of Deep Neural Networks"],"prefix":"10.1109","author":[{"given":"Qing","family":"Ye","sequence":"first","affiliation":[]},{"given":"Yuxuan","family":"Han","sequence":"additional","affiliation":[]},{"given":"Yanan","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Jiancheng","family":"Lv","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"article-title":"Deep gradient compression: Reducing the communication bandwidth for distributed training","year":"0","author":"lin","key":"ref10"},{"article-title":"Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication","year":"2018","author":"sattler","key":"ref11"},{"article-title":"FireCaffe: near-linear acceleration of deep neural network training on compute clusters","year":"2015","author":"iandola","key":"ref12"},{"article-title":"Variance-based gradient compression for efficient distributed deep learning","year":"2018","author":"tsuzuku","key":"ref13"},{"article-title":"DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression","year":"2019","author":"tang","key":"ref14"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0074"},{"key":"ref16","first-page":"3043","article-title":"Asynchronous decentralized parallel stochastic gradient descent","volume":"80","author":"lian","year":"0"},{"article-title":"Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent","year":"2017","author":"lian","key":"ref17"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1080\/03036758.2019.1609052"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2003.818557"},{"key":"ref28","volume":"1","author":"krizhevsky","year":"2009","journal-title":"Learning multiple layers of features from tiny images"},{"article-title":"Parallel training of DNNs with Natural Gradient and Parameter Averaging","year":"2014","author":"povey","key":"ref4"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref3","first-page":"1058","article-title":"1-bit stochastic gradient descent and its application to data-parallel distributed training of speech dnns","author":"seide","year":"2014"},{"article-title":"Staleness-aware Async-SGD for Distributed Deep Learning","year":"2015","author":"zhang","key":"ref6"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref5","first-page":"1","article-title":"Asynchronous parallel stochastic gradient descent - a numeric core for scalable distributed machine learning algorithms","author":"keuper","year":"2015","journal-title":"Computer Science"},{"article-title":"Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations","year":"2016","author":"hubara","key":"ref8"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2019.00220"},{"key":"ref2","first-page":"2374","article-title":"Deep learning with cots hpc systems","author":"coates","year":"2013","journal-title":"30th International Conference on Machine Learning ICML 2013"},{"key":"ref9","first-page":"1509","article-title":"Terngrad: Ternary gradients to reduce communication in distributed deep learning","author":"wen","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref1","article-title":"Large scale distributed deep networks","author":"dean","year":"2012","journal-title":"Advances in neural information processing systems"},{"key":"ref20","article-title":"A particle swarm optimization-based flexible convolutional auto-encoder for image classification","volume":"pp","author":"sun","year":"2017","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"ref22","article-title":"Theory of the backpropagation neural network","author":"hecht-nielsen","year":"2002","journal-title":"1989 International Joint Conference on Neural Networks"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.154"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICNN.1995.488968"},{"key":"ref23","article-title":"A new optimizer using particle swarm theory","author":"eberhart","year":"2002","journal-title":"MHS&#x2019;95 Proceedings of the Sixth International Symposium on Micro Machine and Human Science"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICPP.2014.36"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICEC.1998.699146"}],"event":{"name":"2020 International Joint Conference on Neural Networks (IJCNN)","start":{"date-parts":[[2020,7,19]]},"location":"Glasgow, United Kingdom","end":{"date-parts":[[2020,7,24]]}},"container-title":["2020 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9200848\/9206590\/09207698.pdf?arnumber=9207698","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T21:50:34Z","timestamp":1656453034000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9207698\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":30,"URL":"https:\/\/doi.org\/10.1109\/ijcnn48605.2020.9207698","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}