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pro vyhledávání: '"Ren, Qianying"'
Recently, various contrastive learning techniques have been developed to categorize time series data and exhibit promising performance. A general paradigm is to utilize appropriate augmentations and construct feasible positive samples such that the e
Externí odkaz:
http://arxiv.org/abs/2401.18057
Autor:
Nazarovs, Jurijs, Lumezanu, Cristian, Ren, Qianying, Chen, Yuncong, Mizoguchi, Takehiko, Song, Dongjin, Chen, Haifeng
In this paper, we propose an ordered time series classification framework that is robust against missing classes in the training data, i.e., during testing we can prescribe classes that are missing during training. This framework relies on two main c
Externí odkaz:
http://arxiv.org/abs/2201.09907
Akademický článek
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Publikováno v:
IOP Conference Series: Earth and Environmental Science. 153:062040
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::50d35dbdc62ef68e437d8e582849c853
https://doi.org/10.1088/1755-1315/153/6/062040
https://doi.org/10.1088/1755-1315/153/6/062040
Publikováno v:
IOP Conference Series: Earth & Environmental Science; May2018, Vol. 153 Issue 6, p1-1, 1p