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GAGA: Deciphering Age-path of Generalized Self-paced Regularizer. (arXiv:2209.07063v2 [cs.LG] UPDATED)
Sept. 26, 2022, 1:12 a.m. | Xingyu Qu, Diyang Li, Xiaohan Zhao, Bin Gu
cs.LG updates on arXiv.org arxiv.org
Nowadays self-paced learning (SPL) is an important machine learning paradigm
that mimics the cognitive process of humans and animals. The SPL regime
involves a self-paced regularizer and a gradually increasing age parameter,
which plays a key role in SPL but where to optimally terminate this process is
still non-trivial to determine. A natural idea is to compute the solution path
w.r.t. age parameter (i.e., age-path). However, current age-path algorithms are
either limited to the simplest regularizer, or lack solid theoretical …
More from arxiv.org / cs.LG updates on arXiv.org
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