基于这种机制也就引出了我们今天所要讨论的一类方法---Memory based方法。 RL2: Fast Reinforcement Learning Via Slow Reinforcement Learning RNN对时序数据的天然优势让其能够更加有效地进行长期记忆,所以在Meta Learning的研究中RNN被广泛地应用。 这篇paper的工作很直接:把Agent直接建模为一个RNN模型,在每个time step...
给定这种两层分布的结构,这种元学习被描述为 learning to learn。(快速:within,慢速:across) 我们的方法组合了最好的两个世界(...):缓慢的学习为了获得新数据的有用表示的抽样方法的能力,通过梯度下降;迅速的在看见一个新样本后连接从没见过的信息的能力,通过一个外部的记忆模块。 meta-learning task methodology ...
Meta-Learning Deep Energy-Based Memory ModelsSergey BartunovJack RaeSimon OsinderoTimothy LillicrapInternational Conference on Learning Representations
The idea of using a neural network as a memory store is not entirely novel. It goes back at least as far asJohn J. Hopfield’s 1988 work in associative memory(opens in new tab). To our knowledge, though, we’re the first to adopt metalearning techniques to ...
meta_sweeper azureml.automl.runtime.sweeping.sweepers azureml.automl.runtime.sweeping.weight_sweeper azureml.automl.runtime.timeseries azureml.automl.runtime.training_utilities azureml.automl.runtime.voting_ensemble_base azureml.automl.runtime azureml.automl.runtime.featurizer.transformer...
Continual Learning:持续学习研究了从随时间变化的数据分布的数据流中学习的问题。持续学习的瓶颈就是灾难性遗忘,目前减轻灾难性遗忘的方法主要有:adding regularization , separating parameters for previous and new data ,replaying examples from memory or a generative model , meta-learning ...
meta_sweeper azureml.automl.runtime.sweeping.sweepers azureml.automl.runtime.sweeping.weight_sweeper azureml.automl.runtime.timeseries azureml.automl.runtime.training_utilities azureml.automl.runtime.voting_ensemble_base azureml.automl.runtime azureml.automl.runtime.featurizer...
Frog is an integration of memory-based natural language processing (NLP) modules developed for Dutch. All NLP modules are based on Timbl, the Tilburg memory-based learning software package. Most modules were created in the 1990s at the ILK Research Group (Tilburg University, the Netherlands) and...
为了解决这个问题,我们提出了基于记忆的多源元学习(Memory-based Multi-Source Meta-Learning,M3L)框架来为不可见域训练一个可泛化的模型。具体地,引入元学习策略来模拟领域泛化的训练-测试过程,以学习更泛化的模型。为了克服参数分类器造成的不稳定元优化,我们提出了一种基于记忆的非参数识别损失,并与元学习相协调。
Memory机制(类似于attention)是few-shot中常用的一个机制,可以存储这个task中support set的图像信息,以便于在query图像分类时检索相关信息,从而利于分类。 ADAPTIVE POSTERIOR LEARNING:FEW-SHOT LEARNING WITH A SURPRISE-BASED MEMORY MODULE (ICLR19) motivation:meta-learning的算法可以memory和recall之前见过的知识以便新...