{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T07:40:09Z","timestamp":1750750809698,"version":"3.41.0"},"reference-count":22,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T00:00:00Z","timestamp":1746403200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T00:00:00Z","timestamp":1746403200000},"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":[[2025,5,5]]},"DOI":"10.1109\/cscwd64889.2025.11033388","type":"proceedings-article","created":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T17:24:40Z","timestamp":1750699480000},"page":"686-691","source":"Crossref","is-referenced-by-count":0,"title":["ACache: An Importance-Based Cache with Augmentation Cutting for Accelerating DNN Training"],"prefix":"10.1109","author":[{"given":"Long","family":"Zhao","sequence":"first","affiliation":[{"name":"Anhui University,Hefei,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinwei","family":"Zheng","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China,Hefei,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Futian","family":"Wang","sequence":"additional","affiliation":[{"name":"Anhui University,Hefei,China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2014.2339736"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/UEMCON47517.2019.8993089"},{"key":"ref4","article-title":"An image is worth 16\u00d716 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020","journal-title":"arXiv preprint"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2201128119"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2023.120366"},{"key":"ref8","article-title":"An-alyzing and mitigating data stalls in dnn training","author":"Mohan","year":"2020","journal-title":"arXiv preprint"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.14778\/3636218.3636238"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3485730.3485930"},{"key":"ref11","first-page":"283","article-title":"Quiver: An informed storage cache for deep learning","volume-title":"18th USENIX Conference on File and Storage Technologies (FAST 20)","author":"Kumar"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01316-z"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00020"},{"key":"ref14","article-title":"Data augmentation by pairing samples for images classification","author":"Inoue","year":"2018","journal-title":"arXiv preprint"},{"issue":"27","key":"ref15","first-page":"1","article-title":"Importance sampling for minibatches","volume":"19","author":"Csiba","year":"2018","journal-title":"Journal of Machine Learning Research"},{"key":"ref16","first-page":"15630","article-title":"Prioritized training on points that are learnable, worth learning, and not yet learnt","volume-title":"International Conference on Machine Learning. PMLR","author":"Mindermann"},{"key":"ref17","first-page":"135","article-title":"{SHADE}: Enable fundamental cacheability for distributed deep learning training","volume-title":"21st USENIX Conference on File and Storage Technologies (FAST 23)","author":"Khan"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA56546.2023.10070964"},{"key":"ref19","first-page":"341","article-title":"Learning cache replacement with {CACHEUS}","volume-title":"19th USENIX Conference on File and Storage Tech-nologies (FAST 21)","author":"Rodriguez"},{"key":"ref20","article-title":"Squeezenet: Alexnet -level accuracy with 50x fewer parameters and! 0.5 mb model size","author":"Iandola","year":"2016","journal-title":"arXiv preprint"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref22","first-page":"537","article-title":"Refurbish your training data: Reusing partially augmented samples for faster deep neural network training","volume-title":"2021 USENIX Annual Technical Conference (USENIX ATC 21)","author":"Lee"}],"event":{"name":"2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD)","start":{"date-parts":[[2025,5,5]]},"location":"Compiegne, France","end":{"date-parts":[[2025,5,7]]}},"container-title":["2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11033175\/11033221\/11033388.pdf?arnumber=11033388","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T07:00:06Z","timestamp":1750748406000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11033388\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,5]]},"references-count":22,"URL":"https:\/\/doi.org\/10.1109\/cscwd64889.2025.11033388","relation":{},"subject":[],"published":{"date-parts":[[2025,5,5]]}}}