{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T11:37:11Z","timestamp":1761824231527,"version":"3.28.0"},"reference-count":34,"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\/ijcnn48605.2020.9207533","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":9,"title":["FasTrCaps: An Integrated Framework for Fast yet Accurate Training of Capsule Networks"],"prefix":"10.1109","author":[{"given":"Alberto","family":"Marchisio","sequence":"first","affiliation":[]},{"given":"Beatrice","family":"Bussolino","sequence":"additional","affiliation":[]},{"given":"Alessio","family":"Colucci","sequence":"additional","affiliation":[]},{"given":"Muhammad Abdullah","family":"Hanif","sequence":"additional","affiliation":[]},{"given":"Maurizio","family":"Martina","sequence":"additional","affiliation":[]},{"given":"Guido","family":"Masera","sequence":"additional","affiliation":[]},{"given":"Muhammad","family":"Shafique","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref33","article-title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms","author":"xiao","year":"2017","journal-title":"CoRR"},{"article-title":"Demystifying learning rate polices for high accuracy training of deep neural networks","year":"2019","author":"wu","key":"ref32"},{"key":"ref31","article-title":"A disciplined approach to neural network hyper-parameters: Part 1 - learning rate, batch size, momentum, and weight decay","author":"smith","year":"2018","journal-title":"CoRR"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2017.58"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/3316781.3323472"},{"key":"ref10","article-title":"Transforming auto-encoders","author":"hinton","year":"2011","journal-title":"ICANN"},{"journal-title":"Three factors influencing minima in sgd","year":"2017","author":"jastrzebski","key":"ref11"},{"article-title":"The mnist database of handwritten digits","year":"1998","author":"lecun","key":"ref12"},{"journal-title":"SGDR Stochastic gradient descent with warm restarts","year":"2016","author":"loshchilov","key":"ref13"},{"key":"ref14","article-title":"Capstore: Energy-efficient design and management of the on-chip memory for capsulenet inference accelerators","author":"marchisio","year":"2019","journal-title":"ArXiv"},{"key":"ref15","article-title":"X-traincaps: Accelerated training of capsule nets through lightweight software optimizations","author":"marchisio","year":"2019","journal-title":"ArXiv"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ISVLSI.2019.00105"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.23919\/DATE.2019.8714922"},{"key":"ref18","article-title":"Capsattacks: Robust and imperceptible adversarial attacks on capsule networks","author":"marchisio","year":"2019","journal-title":"ArXiv"},{"key":"ref19","article-title":"Q-capsnets: A specialized framework for quantizing capsule networks","author":"marchisio","year":"2020","journal-title":"Proceedings of the 57th Annual Design Automation Conference"},{"article-title":"Super-convergence: Very fast training of residual networks using large learning rates","year":"2017","author":"smith","key":"ref28"},{"article-title":"The effects of hyperparameters on sgd training of neural networks","year":"2015","author":"breuel","key":"ref4"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/MDAT.2020.2971217"},{"key":"ref3","first-page":"437","author":"bengio","year":"2012","journal-title":"Practical Recommendations for Gradient-based Training of Deep Architectures"},{"key":"ref6","article-title":"Big batch sgd: Automated inference using adaptive batch sizes","author":"de","year":"2016","journal-title":"ArXiv"},{"article-title":"No more pesky learning rate guessing games","year":"2015","author":"smith","key":"ref29"},{"key":"ref5","first-page":"1463","article-title":"Starting small - learning with adaptive sample sizes","author":"daneshmand","year":"0"},{"article-title":"Accurate, large minibatch sgd: Training imagenet in 1 hour","year":"2017","author":"goyal","key":"ref8"},{"article-title":"Adabatch: Adaptive batch sizes for training deep neural networks","year":"2017","author":"devarakonda","key":"ref7"},{"article-title":"Hot swapping for online adaptation of optimization hyperparameters","year":"2014","author":"bache","key":"ref2"},{"article-title":"Matrix capsules with em routing","year":"2018","author":"hinton","key":"ref9"},{"key":"ref1","article-title":"Stopwasting my gradients: Practical svrg","volume":"28","author":"babanezhad harikandeh","year":"2015","journal-title":"Advances in neural information processing systems"},{"key":"ref20","doi-asserted-by":"crossref","DOI":"10.23919\/DATE48585.2020.9116393","article-title":"Red-cane: A systematic methodology for resilience analysis and design of capsule networks under approximations","author":"marchisio","year":"2020","journal-title":"2020 Design Automation & Test in Europe Conference & Exhibition (DATE)"},{"key":"ref22","article-title":"On the vulnerability of capsule networks to adversarial attacks","author":"michels","year":"2019","journal-title":"ArXiv"},{"journal-title":"Revisiting small batch training for deep neural networks","year":"2018","author":"masters","key":"ref21"},{"journal-title":"On Automatic Differentiation","year":"2017","author":"paszke","key":"ref24"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TENCON.2019.8929465"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.23919\/DATE.2018.8342120"},{"key":"ref25","first-page":"3856","article-title":"Dynamic routing between capsules","volume":"30","author":"sabour","year":"2017","journal-title":"Advances in neural information processing systems"}],"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\/09207533.pdf?arnumber=9207533","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T21:56:49Z","timestamp":1656453409000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9207533\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1109\/ijcnn48605.2020.9207533","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}