{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/modelling-long-range-dependencies-in-n-d-from","title":"Modelling Long Range Dependencies in $N$D: From Task-Specific to a General Purpose CNN","arxiv_id":"2301.10540","date":"2023-01-25","proceeding":null,"authors":["David M. Knigge","David W. Romero","Albert Gu","Efstratios Gavves","Erik J. Bekkers","Jakub M. Tomczak","Mark Hoogendoorn","Jan-Jakob Sonke"],"abstract":"Performant Convolutional Neural Network (CNN) architectures must be tailored to specific tasks in order to consider the length, resolution, and dimensionality of the input data. 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