标题： 储层记忆机器 显示英文标题

标题： Reservoir memory machines

摘要： 近年来，神经图灵机通过将神经网络的灵活性与图灵机的计算能力相结合而受到关注。 然而，神经图灵机难以训练，这限制了它们的应用性。 我们提出了水库记忆机，它们仍然能够解决一些神经图灵机的基准测试，但训练速度要快得多，只需要一个对齐算法和线性回归。 我们的模型也可以看作是带有外部记忆的回声状态网络的扩展，能够在没有干扰的情况下实现任意长度的存储。 显示英文摘要

摘要： In recent years, Neural Turing Machines have gathered attention by joining the flexibility of neural networks with the computational capabilities of Turing machines. However, Neural Turing Machines are notoriously hard to train, which limits their applicability. We propose reservoir memory machines, which are still able to solve some of the benchmark tests for Neural Turing Machines, but are much faster to train, requiring only an alignment algorithm and linear regression. Our model can also be seen as an extension of echo state networks with an external memory, enabling arbitrarily long storage without interference.