{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T01:00:50Z","timestamp":1730250050527,"version":"3.28.0"},"reference-count":28,"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\/icme46284.2020.9102751","type":"proceedings-article","created":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T21:40:07Z","timestamp":1591738807000},"page":"1-6","source":"Crossref","is-referenced-by-count":2,"title":["M-ARY Quantized Neural Networks"],"prefix":"10.1109","author":[{"given":"Jen-Tzung","family":"Chien","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Su-Ting","family":"Chang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","first-page":"806","article-title":"Sparse convolutional neural networks","author":"liu","year":"2015","journal-title":"Proc of IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.155"},{"key":"ref12","article-title":"Dropout is a special case of the stochastic delta rule: Faster and more accurate deep learning","author":"frazier-logue","year":"2018","journal-title":"arXiv preprint arXiv 1808 03578"},{"key":"ref13","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"Journal of Machine Learning Research"},{"key":"ref14","article-title":"DoReFa-Net: Training low bitwidth convolutional neural networks with low bitwidth gradients","author":"zhou","year":"2016","journal-title":"arXiv preprint arXiv 1606 06160"},{"key":"ref15","first-page":"4107","article-title":"Binarized neural networks","author":"hubara","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"ref16","article-title":"Trained ternary quantization","author":"zhu","year":"2017","journal-title":"International Conference on Learning Representations"},{"key":"ref17","first-page":"6869","article-title":"Quantized neural networks: Training neural networks with low precision weights and activations","volume":"18","author":"hubara","year":"2017","journal-title":"The Journal of Machine Learning Research"},{"key":"ref18","article-title":"Variational network quantization","author":"achterhold","year":"2018","journal-title":"International Conference on Learning Representations"},{"key":"ref19","article-title":"Ternary weight networks","author":"li","year":"2016","journal-title":"arXiv preprint arXiv 1605 04711"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2677439"},{"key":"ref27","first-page":"13","article-title":"Deep Bayesian learning and understanding","author":"chien","year":"2018","journal-title":"Proc of International Conference on Computational Linguistics Tutorial Abstracts"},{"key":"ref3","first-page":"2042","article-title":"Convolutional neural network architectures for matching natural language sentences","author":"hu","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref6","first-page":"3288","article-title":"Bayesian compression for deep learning","author":"louizos","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2690379"},{"key":"ref8","first-page":"1135","article-title":"Learning both weights and connections for efficient neural network","author":"han","year":"2015","journal-title":"Advances in neural information processing systems"},{"key":"ref7","article-title":"Compressing deep convolutional networks using vector quantization","author":"gong","year":"2014","journal-title":"arXiv preprint arXiv 1412 6115"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TASL.2011.2173371"},{"key":"ref9","article-title":"Tensorizing neural networks","author":"novikov","year":"2015","journal-title":"Advances in neural information processing systems"},{"key":"ref1","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref20","article-title":"Towards the limit of network quantization","author":"choi","year":"2016","journal-title":"arXiv preprint arXiv 1612 01543"},{"key":"ref22","article-title":"Estimating or propagating gradients through stochastic neurons for conditional computation","author":"bengio","year":"2013","journal-title":"arXiv preprint arXiv 1308 3432"},{"key":"ref21","first-page":"525","article-title":"XNOR-Net: Imagenet classification using binary convolutional neural networks","author":"rastegari","year":"2016","journal-title":"Proc of European Conference on Computer Vision"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2499302"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781107295360"},{"key":"ref25","article-title":"Auto-encoding variational Bayes","author":"kingma","year":"2014","journal-title":"International Conference on Learning Representations"}],"event":{"name":"2020 IEEE International Conference on Multimedia and Expo (ICME)","start":{"date-parts":[[2020,7,6]]},"location":"London, United Kingdom","end":{"date-parts":[[2020,7,10]]}},"container-title":["2020 IEEE International Conference on Multimedia and Expo (ICME)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9099125\/9102711\/09102751.pdf?arnumber=9102751","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T00:26:19Z","timestamp":1656375979000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9102751\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":28,"URL":"https:\/\/doi.org\/10.1109\/icme46284.2020.9102751","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}