Neural Turing Machines
Neural Turing Machines
复制标题
DOI：
--
复制
发表时间：
2014-10
期刊：
影响因子：
--
通讯作者：
Alex Graves;Greg Wayne;Ivo Danihelka
中科院分区：
文献类型：
--
作者：
Alex Graves;Greg Wayne;Ivo Danihelka
我们通过将神经网络耦合到外部记忆资源来扩展神经网络的能力，这些外部记忆资源可以通过注意过程与之交互。该组合系统类似于图灵机或冯·诺伊曼体系结构，但端到端是可区分的，允许它通过梯度下降进行有效训练。初步结果表明，神经图灵机可以从输入和输出示例中推断出简单的算法，如复制、排序和联想回忆。
We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-toend, allowing it to be efficiently trained with gradient descent. Preliminary results demonstrate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples.