论文阅读笔记《Meta-Learning with Memory-Augmented Neural Networks》,程序员大本营,技术文章内容聚合第一站。
We propose shared memory augmented neural network actors as a dynamically scalable alternative. Based on a decomposition of the image into a sequence of local patches, we train such actors to sequentially segment each patch. To further increase the robustness and better capture shape priors, an ...
本文是2016年ICML的会议论文,作者来自谷歌的DeepMind。在论文中作者提出了一种记忆增强神经网络(memory-augmented neural networks,简记MANN)来快速吸收样本中蕴含的信息并利用这些信息对仅提供数个样本的情境做出准确的预测,即少样本学习(Few-Shot Learning)。由于使用了外部记忆部件,因此作者还提出一种有效获取外部记忆部...
本文采用Memory Augmented Neural Network (MANN)来解决轨迹预测问题中的多态性(论文主打点)预测需求,是对传统 NTM1 框架在具体问题中的一次成功应用。 图一、传统NTM框架,由输入、控制器、读写单元、内存单元、输出等几个模拟计算机中基本模块组成。 为了方便理解,我们可以先从代码训练讲起。MANTRA的整体训练步骤分为...
In current implementations of such memory augmented neural networks (MANNs), the content of a network’s memory is typically transferred from the memory to the compute unit (a central processing unit or graphics processing unit) to calculate similarity or distance norms. The processing unit hardware...
具有增强记忆能力的网络结构,例如NTMs具有快速编码新信息的能力,因此能消除传统模型的缺点。这里,我们证明了记忆增强神经网络(memory-augmented neural network)具有快速吸收新数据知识的能力,并且能利用这些吸收了的数据,在少量样本的基础上做出准确的预测。 我们也介绍了一个访问外部记忆存储器的方法,该方法关注于记忆...
不像是GCN依赖于local信息,memory layer依赖于全局信息,因此不用担心过平滑。根据memory layer为基础提出了两种不同的模型:memory-based GNN (MemGNN)以及graph memory network (GMN)。其中,Memory augmented neural networks (MANNs)是模型的基础,所以在看模型之前还是学习一下MANN。
《Meta-Learning with Memory-Augmented Neural Networks》2016 ICML google deepmindproceedings.mlr.press/v Chelsea Finn 在meta-learning 做了许多工作 abstract 证明了记忆增强神经网络迅速吸收新数据的能力,并能利用吸收的数据在仅仅有少量样本的情况下做出精确的预测。我们也引进了一个新的方法去处理聚焦于记忆内容...
Lillicrap. Meta-learning with memory-augmented neural networks. In Proceedings of The 33rd International Conference on Machine Learning, pages 1842–1850, 2016. [3] J. Weston, S. Chopra, and A. Bordes. Memory networks. International Conference on Learning Representations, 2015. [4] A. Miller...
4.1 Memory-augmented neural networks As a first building block, our choice fell on MANNs due to their extensive usage in tasks that present similarities with our approach, such as machine reading comprehension and question answering. We could choose among several MANN architectures of various levels...