【论文笔记】QANET:Combining Local Convolution With Global Self-attention for Reading Comprehension 目录1. 简要介绍 2. 模型 3. data augmentation by backtranslation 4. 实验 1. 简要介绍 模型创新点: (一)移除了
Besides the backbone network consisting of spectral hypergraph convolution blocks, a hyperedge attention module is learned to adjust the weights of hyperedges in the WHCN. Finally, a segmentation network is trained by these pseudo point cloud labels. Experimental results on the scanNet, S3DIS, ...
This work develops a based on Heterogeneous hypergraph convolution and multi-order convolution of heterogeneous graph model, namely HHMDA to perform a MiRNA-Disease Association prediction task. Example To run HHMDAon your data, execute the following command from the project home directory: 'python ...
PyTorch Geometric:https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#convolutional-layers(Hypergraph Convolution Network) DeepHypergraph:https://github.com/iMoonLab/DeepHypergraph(Hypergraph Neural Networks) OpenHGNN:https://github.com/BUPT-GAMMA/OpenHGNN(Heterogeneous Graph Neural Network)...
This work has been published in IJCAI 2019. Dynamic Hypergraph Neural Networks (DHGNN) is a kind of neural networks modeling dynamically evolving hypergraph structures, which is composed of the stacked layers of two modules: dynamic hypergraph construction (DHG) and hypergrpah convolution (HGC). ...
Building the Convolution Layer of HGNN+ classHGNNPConv(nn.Module):def__init__(self,):super().__init__() ...self.reset_parameters()defforward(self,X:torch.Tensor,hg:dhg.Hypergraph)->torch.Tensor:# apply the trainable parameters ``theta`` to the input ``X``X=self.theta(X)# perform...
In this way, traditional hypergraph learning procedure can be conducted using hyperedge convolution operations efficiently. HGNN is able to learn the hidden layer representation considering the high-order data structure, which is a general framework considering the complex data correlations. In this ...
to alleviate the issue of data scarcity, we incorporate an external knowledge graph and construct aknowledge-based hypergraphconsidering fine-grained, entity-level semantics. We further conduct multi-grained hypergraph convolution on the two kinds of hypergraphs, and utilize the enhanced representations to...
这里分为 Interest-based User和 Item两种构建方式,如上图的下半部分。...超图卷积(Hypergraph Convolution Network (HGCN))。构完超图之后,学习表示就套公式就好: 预测模块。...总结来说HyperCTR关键词是多模态+时序+组,通过基于兴趣的用户超图和项超图这两个Hypergraph来丰富每个用户和项的表示。
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