{"id":118471,"date":"2021-10-11T21:56:29","date_gmt":"2021-10-11T12:56:29","guid":{"rendered":"http:\/\/175.125.95.178\/ai-in-communication\/18471\/"},"modified":"2026-05-03T06:02:43","modified_gmt":"2026-05-02T21:02:43","slug":"18471","status":"publish","type":"ai-in-communication","link":"http:\/\/ee.presscat.kr\/en\/ai-in-communication\/18471\/","title":{"rendered":"From learning to meta-learning: Reduced training overhead and complexity for communication systems"},"content":{"rendered":"<p style=\"text-indent:18.0pt\"><span style=\"font-size:12pt\"><span style=\"line-height:109%\"><span><span style=\"font-family:Calibri,sans-serif\"><span lang=\"EN-GB\" style=\"font-size:13.0pt\"><span style=\"line-height:109%\"><span>Authors: Osvaldo Simeone, <b>Sangwoo Park<\/b>, <b>Joonhyuk Kang<\/b><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"text-indent:18.0pt\"><span style=\"font-size:12pt\"><span style=\"line-height:109%\"><span><span style=\"font-family:Calibri,sans-serif\"><span lang=\"EN-GB\" style=\"font-size:13.0pt\"><span style=\"line-height:109%\"><span>Conference: 2020 2nd 6G Wireless Summit (6G SUMMIT)<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"text-indent:18.0pt\"><span style=\"font-size:12pt\"><span style=\"line-height:109%\"><span><span style=\"font-family:Calibri,sans-serif\"><span lang=\"EN-GB\" style=\"font-size:13.0pt\"><span style=\"line-height:109%\"><span>Abstract: We emphasise usefulness of meta-learning for communication systems to save communication resources. In this summit, we organize the reason why we believe meta-learning would be the key ingredient for future (6G) communication systems. <\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"text-indent:18.0pt\">&nbsp;<\/p>\n<p style=\"text-indent:18.0pt\">\n<div class=\"\"><img decoding=\"async\" class=\"\" src=\"\/wp-content\/uploads\/drupal\/\uac15\uc8fc\ud601\uad50\uc218\ub2d84.png\" alt=\"\" title=\"\"><\/div>\n<\/p>\n<p class=\"rtejustify\" style=\"text-indent: 18pt\"><span style=\"font-size:12pt\"><span style=\"line-height:109%\"><span><span style=\"font-family:Calibri,sans-serif\"><span lang=\"EN-GB\" style=\"font-size:11.0pt\"><span style=\"line-height:109%\"><span>Fig. 4: Overall description of meta-learning. Based on data from multiple meta-training tasks (left part), inductive bias, or model class, is determined and applied to train a new meta-test task (right part).<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>728<\/p>\n","protected":false},"featured_media":0,"template":"","class_list":["post-118471","ai-in-communication","type-ai-in-communication","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/ai-in-communication\/118471","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/ai-in-communication"}],"about":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/types\/ai-in-communication"}],"wp:attachment":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/media?parent=118471"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}