论文标题
GAGA：广义自定为正规器的解密年龄段
GAGA: Deciphering Age-path of Generalized Self-paced Regularizer
论文作者
论文摘要
如今，自定进度的学习（SPL）是模仿人类和动物的认知过程的重要机器学习范式。 SPL制度涉及自定进度的正常化程序和逐渐增加的年龄参数，该参数在SPL中起着关键作用，但最佳地终止此过程的地方仍然是不平凡的。一个自然的想法是计算解决方案路径W.R.T.年龄参数（即年龄 - 路径）。但是，当前的年龄段算法要么仅限于最简单的正常器，要么缺乏牢固的理论理解以及计算效率。为了应对这一挑战，我们提出了一个小说\下划线{g}能量\下划线{ag} e-path \ usewissline {a} lgorithm（gaga），用于具有基于普通微分方程（ODES）的各种自节奏的常规器（ODE）的SPL，并设置了整个解决方案w.r.r.t.t.t.一系列年龄参数。据我们所知，GAGA是第一个确切的路径遵循算法，该算法可以针对一般的自定进度正常器的年龄段。最后，详细描述了经典SVM和Lasso的算法步骤。我们证明了GAGA在实际数据集上的性能，并在算法和竞争基线之间找到相当大的加速。
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 understanding as well as computational efficiency. To address this challenge, we propose a novel \underline{G}eneralized \underline{Ag}e-path \underline{A}lgorithm (GAGA) for SPL with various self-paced regularizers based on ordinary differential equations (ODEs) and sets control, which can learn the entire solution spectrum w.r.t. a range of age parameters. To the best of our knowledge, GAGA is the first exact path-following algorithm tackling the age-path for general self-paced regularizer. Finally the algorithmic steps of classic SVM and Lasso are described in detail. We demonstrate the performance of GAGA on real-world datasets, and find considerable speedup between our algorithm and competing baselines.
