Meta-Learning Method 1)T-Learner 2)S-Learner 3)X-Learner 总结 参考 Uplift Model 概念 顾名思义,uplift 可以理解为增益或者增量,uplift model 即为增量模型。其通常用于评估某种干预对个体的状态/行为的因果效应,也就是 Individual Treatment Effect,即 ITE。 假设在某个 framework 下有 N 个个体,个体 i ...
在因果推断笔记(一) - Uplift Modeling with Meta-Learning Method(T-Learner/S-Learner/X-Learner) 这篇文章中,我们介绍了几种经典的元学习方法用于构建 uplift model。但是无论是 S-Learner,T-Learner 又或者是改进版的 X-Learner,虽然从原理和解释性上都十分的完备,但是它们看起来都不够具有一般性,即每种算...
In a meta-learning method according to an aspect of the present disclosure, a computer executes: an input procedure of inputting a plurality of training datasets made up of training data in which at least a feature of a case example is included, in which the training datasets can include ...
As another powerful metric-based meta-learning method, matching network [161] uses different networks to encode support and query images. For support images embedding, a bidirectional long-short-term memory (LSTM) [198] is used in the context of the support set\({D}_{S}\); for query imag...
A data-augmentation method for meta-learning Abstract Data augmentation is one of the most effective approaches for improving the accuracy of modern machine learning models, and it is also indispensable to train a deep model for meta-learning. In this paper, we introduce a task augmentation method...
Our proposed method learns to encode each task and generate task embeddings that modulate the model’s activations. The resulting modulated model become specialized for the current task and leads to more effective adaptation. Our framework is designed to work in a realistic setting where the mode ...
This is an implementation for our SIGIR 2020 paper:How to Retrain Recommender System? A Sequential Meta-Learning Method. Contributors: Yang Zhang, Chenxu Wang, Fuli Feng, Xiangnan He Requirements pytorch >= 1.2 numpy Parameters --MF_lr: learning rate for(^w)t ...
We approach the problem by learning distributions over functions using Neural Processes (NPs), a recently introduced probabilistic meta-learning method. This allows the treatment of model uncertainty to tackle the exploration/exploitation dilemma. We show that NPs are suitable for sequential decision ...
六、Model base method 以上主要介绍的是基于优化的元学习方式。它们本质上还是选取了模型参数(模型架构、优化方式、初始化参数、数据集蒸馏)之后,用梯度下降去对新的任务进行训练。 本文参考: 火炉课堂 | 元学习(meta-learning)到底是什么鬼?_哔哩哔哩_bilibiliwww.bilibili.com/video/BV1sX4y1w7Tz?from=search...
Method5-way 1-shot5-way 5-shot Vinyals et al. (2016)43.6 $\%$55.3 $\%$ Finn et al. (2017)48.7 $\pm$ 1.84 $\%$63.1 $\pm$ 0.92 $\%$ Ravi $\&$ Larochelle (2017)43.4 $\pm$ 0.77 $\%$60.2 $\pm$ 0.71 $\%$ Snell et al. (2017)46.61 $\pm$ 0.78 $...