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,...
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 ...
学习如何学习或元学习来获取知识或偏移归纳的方法由来已久[1998]。最近,Lake[2016]有力的论证了它作为人工智能中一个模块的重要性。一般来说,这些想法涉及到两个不同的时间尺度:在任务中快速学习或者在很多不同的任务中更渐进的全局学习。在一些最早的元学习中,Naik[1992]使用以前训练运行的结果来修改反向传播的下...
an learning algorithm to minimize the loss of a deep model an optimization algorithm using learned features instead of hand-designed features a method which transfers knowledge between different problems. Math Gradient Descent Method: θt+1=θt+α⋅g(θt) . ( 1 ) Gradient Descent Method wit...
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, ...
Andrychowicz, Marcin, et al. “Learning to learn by gradient descent by gradient descent.” Advances in neural information processing systems. 2016. 文章目录 1 简介 2 如何对optimizee进行参数更新? 3 如何对opti... 查看原文 [paper] Meta-Learner LSTM ...
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
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θ∗=argminθ∈Θf(θ) 其中,参数 θ 的优化方式为θt+1=θt−α∇f(θt) 上式是...
Learning to learn by gradient descent by gradient descent, Andrychowicz et al.,NIPS 2016 One of the things that strikes me when I read these NIPS papers is just how short some of them are – between the introduction and the evaluation sections you might find only one or two pages! A gen...
We learn recurrent neural network optimizers trained on simple synthetic functions by gradient descent. We show that these learned optimizers exhibit a remarkable degree of transfer in that they can be used to efficiently optimize a broad range of derivative-free black-box functions, including ...