摘要:本文主要为大家讲解基于模型的元学习中的Learning to Learn优化策略和Meta-Learner LSTM。 本文分享自华为云社区《深度学习应用篇-元学习[16]:基于模型的元学习-Learning to Learn优化策略、Meta-Learner LSTM》,作者:汀丶 。 1.Learning to Learn Learning to Learn by Gradient Descent by Gradient Descent ...
1.Learning to Learn Learning to Learn by Gradient Descent by Gradient Descent 提出了一种全新的优化策略, 用LSTM 替代传统优化方法学习一个针对特定任务的优化器。 在机器学习中,通常把优化目标 $f(\theta)$ 表示成 $$ \theta^{*}=\operatorname{argmin}_{\theta \in \Theta} f(\theta) $$ 其中,...
Learning to learn by gradient descent by gradient descent - PyTorch实践 注:上述链接的公式看着更舒服 “浪费75金币买控制守卫有什么用,还不是让人给拆了?我要攒钱!早晚憋出来我的灭世者的死亡之帽!” Learning to learn,即学会学习,是每个人都具备的能力,具体指的是一种在学习的过程中去反思自己的学习行为...
1.Learning to Learn Learning to Learn by Gradient Descent by Gradient Descent 提出了一种全新的优化策略, 用LSTM 替代传统优化方法学习一个针对特定任务的优化器。 在机器学习中,通常把优化目标f(θ) 表示成 其中,参数θ的优化方式为 上式是一种针对特定问题类别的、人为设定的更新规则, 常见于深度学习中,主...
【Meta learning】Learning to learn by gradient descent by gradient descent (Nips2016) 摘要: 特征的寻找已取得巨大成功,从手工设计到机器自己学习。但是优化算法仍依靠人工设计,这篇文章试图将优化算法的设计转换成一个学习问题。借用LSTM实现学习算法,在训练任务上优于通用人工设计方法,且可推广到相似结构的新任务...
Learning to learn by gradient descent by gradient descent 1. Motivation Google DeepMind等[1]发表在NIPS2016年的论文,立意非常高。 在深度学习中,有五花八门的人工设计的优化器,比如传统的SGD,以及后来的Momentum、AdaGrad、RMSProp以及大多数情况下的首选优化器Adam等。
内容提示: Learning to learn by gradient descentby gradient descentMarcin Andrychowicz 1 , Misha Denil 1 , Sergio Gómez Colmenarejo 1 , Matthew W. Hoffman 1 ,David Pfau 1 , Tom Schaul 1 , Brendan Shillingford 1,2 , Nando de Freitas 1,2,31 Google DeepMind 2 University of Oxford 3 ...
In this paper we show how the design of an optimization algorithm can be cast as a learning problem, allowing the algorithm to learn to exploit structure in the problems of interest in an automatic way. Our learned algorithms, implemented by LSTMs, outperform generic, hand-designed competitors ...
深度学习论文Learning to learn by gradient descent by gradient descent_20180118194148.pdf,Learning to learn by gradient descent by gradient descent Marcin Andrychowicz , Misha Denil , Sergio Gómez Colmenarejo , Matthew W. Hoffman , David Pfau , Tom Schau
We present a novel and flexible approach to the problem of feature selection, called S Perkins,K Lacker,J Theiler - 《Journal of Machine Learning Research》 被引量: 489发表: 2003年 Learning to Learn Using Gradient Descent This paper introduces the application of gradient descent methods to meta...