论文标题
对称结构卷积神经网络
Symmetry Structured Convolutional Neural Networks
论文作者
论文摘要
我们考虑具有在空间维度中对称的2D结构特征的卷积神经网络（CNN）。此类网络在为顺序推荐问题以及RNA和蛋白质序列的二级结构推理问题以及二级结构推理时产生了对成对关系的建模。我们开发了CNN体系结构，该体系结构生成并保留了网络卷积层中的对称结构。我们提供了卷积内核的参数化，这些卷积内核产生了更新规则，以在整个培训过程中保持对称性。我们将此体系结构应用于顺序推荐问题，RNA二级结构推断问题以及蛋白质触点图预测问题，表明对称结构化网络使用较少数量的机器参数产生了改进的结果。
We consider Convolutional Neural Networks (CNNs) with 2D structured features that are symmetric in the spatial dimensions. Such networks arise in modeling pairwise relationships for a sequential recommendation problem, as well as secondary structure inference problems of RNA and protein sequences. We develop a CNN architecture that generates and preserves the symmetry structure in the network's convolutional layers. We present parameterizations for the convolutional kernels that produce update rules to maintain symmetry throughout the training. We apply this architecture to the sequential recommendation problem, the RNA secondary structure inference problem, and the protein contact map prediction problem, showing that the symmetric structured networks produce improved results using fewer numbers of machine parameters.
