论文标题
使用受限的玻尔兹曼机器的图形信号恢复
Graph Signal Recovery Using Restricted Boltzmann Machines
论文作者
论文摘要
我们通过利用受限玻尔兹曼机器的内容可寻址的内存属性和神经网络的表示能力来从专家系统中恢复图形信号。所提出的管道需要深层神经网络，该网络经过培训，该网络在向下的机器学习任务上进行了干净的数据，该数据没有任何形式的损坏或不完整。我们表明，深层神经网络学到的表示形式通常比确定数据本身更有效。尽管该管道可以在任何数据集中处理噪声，但对于图形结构化数据集特别有效。
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.
