哦,似乎对算法本身总算有了一点点的理解,实际上MAML就像是在找到一组参数\theta而有益于所有task,不过这么做竟然work实在让本学渣感觉就像黑科技。Chelsea Finn近200页的博士论文《Learning to Learn with Gradients》(嗯。。。假装可以看懂)中从expressive power、consistent等角度对算法进行了论证,如有兴趣请自行查阅。
Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification:EMNLP 19, 长文,这篇文章利用 Meta-Learning 的方式不再是学习初始化,而是之前的方式一:输出模型的参数。具体来说,是输出一个多标签分类器的 threshold 以及各个标签类别对于 loss function 的权重,motivation 在于: 多标签...
Chelsea Finn博士论文赏析Learning to Learn with Gradients 博客 大数据时代的小样本深度学习问题的综述 Be...
10、Meta-Learning with Temporal Convolutions,2017 11、Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks,2017 12、Learning to Learn: Meta-Critic Networks for Sample Efficient Learning 13、Learning to Compare: Relation Network for Few-Shot Learning,2017 14、Few-shot Autoregressive Densit...
allowing the algorithm to learn to exploit structure in s. the problems of interest in an automatic way. Our learned algorithms, implemented c by LSTMs, outperform generic, hand-designed competitors on the tasks for which [ they are trained, and also generalize well to new tasks with similar...
in [236] suggested the first distributed learning system, where participants selectively share small part of the gradients to ensure the privacy of training data. Hao et al. in [232] proposed a communication algorithm called PEFL. In each secure aggregation, PEFL is non-interactive. The ...
MetaInit: Initializing learning by learning to initialize [paper] Yann N. Dauphin, Samuel Schoenholz --NeurIPS 2019 Meta-Learning with Implicit Gradients [paper] Aravind Rajeswaran*, Chelsea Finn*, Sham Kakade, Sergey Levine --NeurIPS 2019 ...
Both of them are designed to learn a new representation of data by trying to reformulate the input data. Encoder is used to perform data compression by mapping input into a hidden layer. Decoder is used to reconstruct the given input. When input data is highly nonlinear, more hidden layers ...
Meta-learning经常被理解为learn to learn，可以分为两个阶段：内循环和外循环。内循环阶段，模型利用...
Blending intermediate meta-gradients. 正则化的元梯度\nabla_\eta(k)通过第K次更新后的参数(即\theta_c(K))导出。在实践中,我们发现将其与通过中间参数导出的元梯度混合是有效的,这导致了元参数的最终更新: \Delta \eta \propto \frac{1}{K} \sum_{k=1}^K \nabla_\eta(k) ...