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, ...
StanfordCS330DeepMulti-BlackBoxMetaLearningl2022ILecture4.mp4 01:17:59 StanfordCS330DeepMulti-Optimization-BasedMeta-Learningl2022ILecture5.mp4 01:17:03 StanfordCS330DeepMulti-Non-ParametricFew-ShotLearningl2022ILecture6.mp4 01:17:45 StanfordCS330IUnsupervisedPre-Training_ContrastiveLearningl2022I...
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...
[14] Parisotto, Emilio, Ba, Jimmy Lei, and Salakhutdinov, Ruslan. Actor-mimic: Deep multitask and transfer reinforcement learning. International Conference on Learning Representations (ICLR), 2016. [15] Ravi, Sachin and Larochelle, Hugo. Optimization as a model for few-shot learning. In Interna...
MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning (https://arxiv.org/abs/2310.08252) gmc-drl.github.io/MetaBox/ Resources Readme License BSD-3-Clause license Activity Stars 0 stars Watchers 0 watching Forks 0 forks Report repository Releases ...
(2017). Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. arXiv preprint arXiv:1703.03400. [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 ...
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-...
Meta-Learning. Meta-Reinforcement-Learning. 🎨Different Types Optimization-based meta-learning approaches acquire a collection of optimal initial parameters, facilitating rapid convergence of a model when adapting to novel tasks. Metric-based meta-learning approaches acquire embedding functions that transform...
In this post, I will only pay attention to the deterministic view of meta-learning. In the remaining sections, I focus on the three different approaches to build up the meta-learning algorithm: (1) The black-box approach, (2) The optimization-based approach, and (3) The non-parametric ...