BriefGPT.xyz
Feb, 2020
非凸与凸元学习的样本复杂度分离
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning
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Nikunj Saunshi, Yi Zhang, Mikhail Khodak, Sanjeev Arora
TL;DR
这篇论文通过构造一个简单的元学习实例来探讨优化轨迹对元学习成功的作用,对线性回归实例的凸和非凸问题进行了深入分析,并发现在非凸问题中,Reptile和多任务表示学习有重要的应用,且它们能够meta-learn正确的子空间。
Abstract
One popular trend in
meta-learning
is to learn from many training tasks a common initialization for a gradient-based method that can be used to solve a new task with few samples. The theory of
meta-learning
is st
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