Bayesian Meta-Learning Approaches 第一个想到的方法是让模型直接输出关于$y\^{ts}$分布的参数值。 好处是简单、能够结合其他的多种方法。 坏处是不能得到模型函数的不确定性原因,如确定数据点之间的不确定性如何关联。只能够表达有限的相对于目标ytsyts的分布类别。倾向于产生低校准的不确定性估计。 The Bayesian...
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsMassimiliano PatacchiolaJack TurnerElliot J. CrowleyMichael F. P. O'BoyleAmos J. StorkeyCurran Associates IncNeural Information Processing Systems
Recently, different machine learning methods have been introduced to tackle the challenging few-shot learning scenario that is, learning from a small labeled dataset related to a specific task. Common approaches have taken the form of meta-learning: learning to learn on the new problem given the ...
Agents that interact with other agents often do not know a priori what the other agents’ strategies are, but have to maximise their own online return while interacting with and learning about others. The optimal adaptive behaviour under uncertainty over the other agents’ strategies ...
13. 贝叶斯元模型（Bayesian Meta-Models）：探索在BDL框架内开发贝叶斯元模型的可能性，以提高模型在多个...
Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020) - BayesWatch/deep-kernel-transfer
之前RL课上讲到Q Learning 的一些缺陷,其中一个很大的问题就是overestimation,因此有了double Q learning以及TD3 一定程度上解决这个问题,我觉得主要原因在于Q function estimation的时候,对于一些没遇到过的state,预测的Q function可能会和实际相差很大,而在Bellman update的时候又是去了max,就会导致overestimate, 使得 ...
-分类器-输出预测值 auto部分如下图绿色方框:在ML framework 左边新增 meta-learning,在右边新增 build-ensemble,对于调超参数,用的是贝叶斯优化。自动学习... auto-sklearn 里,一直出现的bayesianoptimizer就是答案。是利用贝叶斯优化进行自动调参的。 具体的贝叶斯优化原理链接 http://codewithzhangyi.com ...
每天一篇 Efficient,离robot learning落地更进一步。 Bayesian Transfer RL 本文将 off-policy RL 中的 behavior policy 定义为一个 Bayesian posterior distribution,用来结合task-specific的先验。期望以这种方式实现transfer learning以及 meta-learning。基于当前Q函数估计的 behaviour policy 通常采用 softmax 或Boltzmann形...
Learning to infer Bayesian posterior from a few-shot dataset is an important step towards robust meta-learning due to the model uncertainty inherent in the problem. In this paper, we propose a novel Bayesian model-agnostic meta-learning method. The proposed method combines scalable gradient-based ...