Black-box optimizationLearning to optimizeMeta-learningRecurrent neural networksConstrained optimizationRecently, 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 ...
Recently, Meta-Black-Box Optimization with Reinforcement Learning (MetaBBO-RL) has showcased the power of leveraging RL at the meta-level to mitigate manual fine-tuning of low-level black-box optimizers. However, this field is hindered by the lack of a unified benchmark. To fill this gap, ...
4.1 Black-Box Adaptation 4.2 Optimization-based inference 4.3 Non-parametric methods / Metric learning 4.4 Bayesian meta-learning 5. Meta-Learning Application 5.1 Few-Shot Image Classification 5.2 Few-Shot Image Segmentation 5.3 Others 本文对元学习做一个介绍, 同时给出一些经典的基于元学习的少样本分类...
MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning (https://arxiv.org/abs/2310.08252) - csxrzhang/MetaBox
[17] Chen, Y., Hoffman, M. W., Colmenarejo, S. G., Denil, M., Lillicrap, T. P., & de Freitas, N. (2016). Learning to Learn for Global Optimization of Black Box Functions. arXiv preprint arXiv:1611.03824. [18] Munkhdalai T, Yu H. Meta Networks. arXiv preprint arXiv:1703....
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 ...
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. ...
以Optimization-based的模型为例,因为我们希望能够加快模型训练过程,同时适应多种任务,因此可以通过学习最优初始化,从而达到这个目标。其训练过程不同于传统人工智能算法的单过程,而是包含Inner loop和Outer loop,目标则是学习一个F,使其能够针对不同的task能够快速学习f并完成不同任务。
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-...
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,...