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pro vyhledávání: '"Xuerun Chen"'
Autor:
David Zimmerer, Peter M. Full, Fabian Isensee, Paul Jager, Tim Adler, Jens Petersen, Gregor Kohler, Tobias Ross, Annika Reinke, Antanas Kascenas, Bjorn Sand Jensen, Alison Q. O'Neil, Jeremy Tan, Benjamin Hou, James Batten, Huaqi Qiu, Bernhard Kainz, Nina Shvetsova, Irina Fedulova, Dmitry V. Dylov, Baolun Yu, Jianyang Zhai, Jingtao Hu, Runxuan Si, Sihang Zhou, Siqi Wang, Xinyang Li, Xuerun Chen, Yang Zhao, Sergio Naval Marimont, Giacomo Tarroni, Victor Saase, Lena Maier-Hein, Klaus Maier-Hein
Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deployment of machine learning algorithms in medicine. When the algorithms encounter cases that deviate from the distribution of the training data, they oft
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4bd09f323a99e76832914cf9e01598d2
http://hdl.handle.net/10044/1/96881
http://hdl.handle.net/10044/1/96881
Autor:
Xuerun Chen
In agent control issues, the idea of combining reinforcement learning and planning has attracted much attention. Two methods focus on micro and macro action respectively. Their advantages would show together if there is good cooperation between them.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b888430c8739bad745aeb0649124146
http://arxiv.org/abs/2003.08554
http://arxiv.org/abs/2003.08554