该研究以「Generative pretrained autoregressive transformer graph neural network applied to the analysis and discovery of novel proteins 」为题，于 2023 年 8 月 29 日发布在《Journal of Applied Physics》。多尺度建模为分层生物材料的分析和设计提供了强大的基础。特别关注构成众多生物和生物衍生材料基础的蛋白...
Graph TransformerGraph classifications are significant tasks for many real-world applications. Recently, Graph Neural Networks (GNNs) have achieved excellent performance on many graph classification tasks. However, most state-of-the-art GNNs face the challenge of the over-smoothing problem and cannot ...
Hist2ST由三个模块组成:Convmixer、Transformer和Graph Neural Networks。具体而言,在每个测序点,相应的组织学图像被裁剪成图像块。图像块被送到Convmixer模块中,以通过卷积操作捕获图像块内的2D视觉特征。学习的特征被送到Transformer模块中,以通过自注意力机制(self-attention)捕获全局空间相关性。然后,Hist2ST通过图神...
Graph Neural Network: 一般的图神经网络通常通过聚合一阶邻居特征更新节点表达,称为AGGREGATE-COMBINE。节点i通过第 l 层的的信息传递更新后的表达为 hi(l) ,一般第一层为 hi(0)=xi (可以是节点原始特征/随机生成/deepwalk生成) a_{i}^{(l)}=AGGREGATE^{(l)}({h_{j}^{l-1}:j\in N(v_{i})}...
Abstract: The combination of Graph Neural Network (GNN) and Transformer brings innovation to the processing of graphic data and natural language processing tasks. By fusing the graphic representation capability of GNN and the self-attention mechanism of Transformer, this combination not only improves ...
异构图注意力网络 Heterogeneous Graph Attention Network 都是同构图的数据。 Network Embedding网络嵌入或者网络表示学习,是在保留网络结构及其属性的前提下,将网络转换到低维空间以应用。以往的方法有很多,随机游走、 深度神经网络、矩阵分解等,也都是基于同构图的。异构图的embedding主要关注基于meta-path的结构信息。ES...
Implicit Graph Neural Networks Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion Efficient Transformers: A Survey ArXiv Weekly Radiostation：NLP、CV、ML 更多精选论文（附音频） 论文 1：High-frequency Component Helps Explain the Generalization of Convolutional Neural Network ...
此外,Transformer其实可以看成是在一个带有完整有向图(with self-loop)上所定义的图神经网络(Graph Neural Network, GNN),其中每个输入都可视为图中的一个节点。然而,Transformer和GNN之间的主要区别在于Transformer没有引入关于如何构造输入数据的先验知识。【Transformer中的信息传递过程完全取决于内容的相似性度量】 ...
Single image dehazing has received a lot of concern and achieved great success with the help of deep-learning models. Yet, the performance is limited by the local limitation of convolution. To address such a limitation, we design a novel deep learning de
4、E-commerce Search via Content Collaborative Graph Neural Network KDD 2023 标题:基于内容协同的图神经网络在电商搜索中的应用 内容:最近的电商搜索图神经网络模型存在三个问题:(1)缺乏对产品内容的语义表示;(2)大规模图上的计算效率较低;(3)对长尾查询和冷启动产品的准确性较差。为同时解决这三个问题,本文...