We propose a model-agnostic pipeline to recover graph signals from an expert system by exploiting the content addressable memory property of restricted Boltzmann machine and the representational ability of a neural network. The proposed pipeline requires the deep neural network that is trained on a downward machine learning task with clean data, data which is free from any form of corruption or incompletion. We show that denoising the representations learned by the deep neural networks is usually more effective than denoising the data itself. Although this pipeline can deal with noise in any dataset, it is particularly effective for graph-structured datasets.

中文翻译：

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使用受限玻尔兹曼机的图形信号恢复

我们提出了一种与模型无关的管道，通过利用受限的Boltzmann机器的内容可寻址存储属性和神经网络的表示能力，从专家系统中恢复图形信号。拟议中的管道需要使用向下的机器学习任务进行训练的深度神经网络，该任务具有干净的数据，这些数据不包含任何形式的损坏或不完整。我们显示，对深度神经网络学习的表示进行去噪通常比对数据本身进行去噪更有效。尽管此管道可以处理任何数据集中的噪声，但它对于图结构数据集特别有效。