Meta Learning,元学习,也叫 Learning to Learn(学会学习)。是继Reinforcement Learning(增强学习)之后又一个重要的研究分支。

知识荟萃

综述

基础算法

元强化学习

应用

视觉应用

其它应用

数据集

视频课程

书籍

代码

领域专家


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最近更新:2019-12-10

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元学习可以让机器学习新的算法。这是一个新兴且快速发展的机器学习研究领域,对所有人工智能研究都有影响。最近的成功案例包括自动模型发现、少枪学习、多任务学习、元强化学习,以及教机器阅读、学习和推理。正如人类不会从头开始学习新任务,而是利用之前所学的知识一样,元学习是高效和稳健学习的关键。本教程将介绍该领域及其应用的重要数学基础,包括这个领域中当前技术水平的关键方法,该领域对众多DSAA参与者来说越来越重要。

https://metalearningacademy.github.io/tutorial/

人类可以从很少的例子中非常有效地学习,因为我们几乎不会从头开始学习新的任务,而是利用我们以前学过的所有东西。元学习在许多不同的方面模仿了这种方法。本教程涵盖了元学习领域中当前技术状态下的关键方法。

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Numerous recent works utilize bi-Lipschitz regularization of neural network layers to preserve relative distances between data instances in the feature spaces of each layer. This distance sensitivity with respect to the data aids in tasks such as uncertainty calibration and out-of-distribution (OOD) detection. In previous works, features extracted with a distance sensitive model are used to construct feature covariance matrices which are used in deterministic uncertainty estimation or OOD detection. However, in cases where there is a distribution over tasks, these methods result in covariances which are sub-optimal, as they may not leverage all of the meta information which can be shared among tasks. With the use of an attentive set encoder, we propose to meta learn either diagonal or diagonal plus low-rank factors to efficiently construct task specific covariance matrices. Additionally, we propose an inference procedure which utilizes scaled energy to achieve a final predictive distribution which can better separate OOD data, and is well calibrated under a distributional dataset shift.

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