论文标题
这是一个超级交易 - 噪音数据上的训练反复网络，并获得平稳的预测
It's a super deal -- train recurrent network on noisy data and get smooth prediction free
论文作者
论文摘要
最近的研究表明，基于嘈杂输入的预测复发神经网络对时间序列的预测会产生平稳的预期轨迹。我们检查了训练数据集和输入序列对网络预测质量的影响。我们提出并讨论了预测过程中观察到的噪声压缩的解释。我们还讨论了在神经科学环境中复发网络对生物体进化的重要性。
Recent research demonstrate that prediction of time series by predictive recurrent neural networks based on the noisy input generates a smooth anticipated trajectory. We examine influence of the noise component in both the training data sets and the input sequences on network prediction quality. We propose and discuss an explanation of the observed noise compression in the predictive process. We also discuss importance of this property of recurrent networks in the neuroscience context for the evolution of living organisms.
