Recently, neural networks trained as optimizers under the "learning to learn" or meta-learning framework have been shown to be effective for a broad range of optimization tasks including derivative-free black-bo
MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning (https://arxiv.org/abs/2310.08252) - csxrzhang/MetaBox
^Optimization As a Model For Few-Shot Learning. Sachin Ravi and Hugo Larochelle. In ICLR 2017https://openreview.net/forum?id=rJY0-Kcll ^Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. Chelsea Finn, Pieter Abbeel, Sergey Levine. In ICML 2017http://proceedings.mlr.press/v7...
We also introduce a new method for accessing an external memory that focuses on memory content, unlike previous methods that additionally use memory location-based focusing mechanisms. Optimization as a model for few-shot learning. ICLR 2017 LSTM-based meta learning [by authors] include the LSTM-...
for optimizingNN(θi) in a multi-task learning setting. Earlier work on multi-task learning [166] assumed that we already have a set of ‘similar’ source taskstj. It transfers information between thesetjandtnewby building a joint GP model for Bayesian optimization that learns and exploits ...
A variety of approaches have been proposed that vary based on how the adaptation portion of the training process is performed. These can broadly be classified into three categories: “black-box” or model-based, metric-based, and optimization-based approaches. ...
Unlike the black-box and optimization-based approaches, we no longer have the task-specific parameters ϕ, which is not required for the comparison between training and test data. 5.2 — Architectures Now let’s go over the different architectures used in non-parametric meta-learning methods. ...
基于优化的方法 Optimization-Based 本文为系列文章第一篇,主要介绍Meta Learning是什么,以及基于度量的方法中最经典的孪生网络。 什么是Meta Learning Meta Learning,通常称为“learning to learn”,可以理解为掌握学习的方法。 普通的机器学习通常是让模型学习做一件具体的事,比如分辨车主是否需要导航;而Meta Learning想...
Optimization-based inference:主要就是 Chelsea Finn 提出的 Model Agnostic Meta-Learning (MAML),下一节就详细地讲一讲 MAML。 MAML 之前已经讲了 MAML 提出的动机,就是希望 Model 能够具备快速学习的能力,甚至是在没有见过的样本类别、并且只有少数几个样例的情况下。Chelsea Finn 给出的解决方案就是: 一方面,...
Unser. Monte-carlo sure: A black- box optimization of regularization parameters for general de- noising algorithms. IEEE Transacitons on Image Processing, 17(9):1540–1554, 2008. 3 [36] Jae Woong Soh, Sunwoo Cho, and Nam Ik Cho. Meta- transfer learning for zero-...