{"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/continuous-spatiotemporal-transformers","title":"Continuous Spatiotemporal Transformers","arxiv_id":"2301.13338","date":"2023-01-31","proceeding":null,"authors":["Antonio H. de O. Fonseca","Emanuele Zappala","Josue Ortega Caro","David van Dijk"],"abstract":"Modeling spatiotemporal dynamical systems is a fundamental challenge in machine learning. Transformer models have been very successful in NLP and computer vision where they provide interpretable representations of data. 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