最近看了一篇ICLR2020的文章《Meta-Learning without Memorization》。我感觉非常有意思,所以花了点时间整理了一下。这篇文章主要解决的是:在meta-learning学习框架下,当任务样本中的标签始终不变的情况下,会带来的一个“记忆”的问题。 因为平时主要是研究优化角度的元学习,所以这篇文章涉及大量信息论的内容,我就不...
meta learningwithout memorization。通过引入互信息作为正则项，避免出现任务过拟合的现象。这就好比让他学...
forward_model: PyTorch module with parameters gradient populated backward_model: PyTorch module identical to forward_model (but without gradients) updated at the Parameter level to keep track of the computation graph for meta-backward pass ''' f_model_iter = get_params(forward_model) b_model_ite...
Meta-Learning without Memorization [paper] Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, Chelsea Finn --ICLR 2020 Meta-Amortized Variational Inference and Learning [paper] Mike Wu, Kristy Choi, Noah Goodman, Stefano Ermon --arXiv 2019 ...
[34] utilize implicit memorization of the relationships between different objects and show promising results for navigation efficiently. Quantitative analysis In this part, we show the navigation performance and provide some further analysis. We offer quantitative results for all “ALL” targets as well...
The introduced model allows for the memorization of useful data from past processes, by integrating external storage memory. Moreover, it facilitates the rapid integration of new information without the need for retraining. In particular, the proposed novel architecture is called memory-augmented neural...
1：在很多小样本的标准数据集下，meta learning还是打不过Fine tune（或改进Fine tune++），元训练的...
1：在很多小样本的标准数据集下，meta learning还是打不过Fine tune（或改进Fine tune++），元训练的...
Avoid memory blow-up — Hidden State Memorization 有时我们想学习一个优化器,该优化器可以在具有数千万参数的超大型模型上运行,同时我们想通过大量步骤来展开元训练,以获得高质量的梯度,例如我们在工作中做到了。 实际上,这意味着我们要在元前传过程中包括很长的训练过程,其中包含许多时间步长,并且我们必须将参数...