为了完成这个目标,我们通过引入一种“diffusion-convolution”的操作,拓展 CNN 到 general graph-structure data。简单的说,不像传统的卷积操作那样(scanning a "square" of parameters across a grid-structured input),the diffusion-convolution operation 通过在一个 graph-structured input 上处理每一个节点,来扫描一...
论文解读:RECURRENT NEURAL NETWORK REGULARIZATION 论文地址:https://arxiv.org/pdf/1409.2329.pdf 一、RNN简介 RNN(Recurrent Neural Network)是一类用于处理序列数据的神经网络。神经网络包含输入层、隐层、输出层,通过**函数控制输出,层与层之间通过权值连接。下图一个标准的RNN结构图,图中每个箭头代表做一次变换,...
这个结果来自于《Scalable Algorithms for Data and Network Analysis》p46 下面给出Diffusion Convolution的形式:这个形式考虑了bidirectional diffusion 下面给出Diffusion Convolutional Layer: 下面说明Diffusion Conv和Spectral Graph Conv的关系: Diffusion Conv是定义在directed和undirected图上的, 对于undirected图,可以用spec...
论文链接: Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting 简介 时空数据的预测目前来说是有一定难度, 本文通过将GCN和RNN相结合, 对道路上的交通流量问题进行预测, 并取得了比较好的效果. 数据 采用一系列传感器采集道路上的交通流量, 那么这些传感器所在的位置可以看作图论里的一个...
我们使用有向图表示交通传感器之间的成对空间关系,该有向图的节点是传感器,边缘权重表示通过路网距离测量的传感器对之间的接近度。我们将交通流的动力学建模为扩散过程,并提出扩散卷积操作以捕获空间依赖性。我们进一步提出了扩散卷积递归神经网络(DCRNN),它集成了扩散卷积,序列到序列的体系结构和调度的采样技术。
To solve this challenge, in this paper we propose a novel spammer detecting method using DCNN (Diffusion Convolution Neural Network) which is a graph-based model. And DCNN model can learn behavior information from other users through the graph structure (i.e., social network relationships). ...
However, the convolution operation and the robust feature detection along with the architecture of U-Net which is an encoder-decoder network made it possible to create a surrogate model using neural networks which is faster than conventional FEM models. Our work also has its limitation which we ...
MDM15, we devise two equivariant kernels to simulate the local chemical bonded graph and the global distant graph. In order to ensure the relative distance between the ligand and the protein, we employ an equivariant graph neural network EGNN to handle the whole pocket which can treat the ...
Each residual block will have a sequence of group-norm, the ReLU activation, a 3×3 "same" convolution, dropout, and a skip-connection.# Residual Blocks class ResBlock(nn.Module): def __init__(self, C: int, num_groups: int, dropout_prob: float): super().__init__() self.relu ...
The trainable and locked neural network blocks are connected with an unique type of convolution layer called "zero convolution", where the convolution weights progressively grow from zeros to optimized parameters in a learned manner. 怎么使用?