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assignmentutor-lab™ 为您的留学生涯保驾护航 在代写神经网络neural networks方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写神经网络neural networks代写方面经验极为丰富，各种代写神经网络neural networks相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 计算机代写|神经网络代写neural networks代考|Graph Attention Networks

In GCNs, for a target node $i$, the importance of a neighbor $j$ is determined by the weight of their edge $A_{i j}$ (normalized by their node degrees). However, in practice, the input graph may be noisy. The edge weights may not be able to reflect the true strength between two nodes. As a result, a more principled approach would be to automatically learn the importance of each neighbor. Graph Attention Networks (a.k.a. GAT(Veličković et al, 2018)) is built on this idea and try to learn the importance of each neighbor based on the Attention mechanism (Bahdanau et al, 2015; Vaswani et al, 2017). Attention mechanism has been wide used in a variety of tasks in natural language understanding (e.g. machine translation and question answering) and computer vision (e.g. visual question answering and image captioning). Next, we will introduce how attention is used in graph neural networks.

Graph Attention Layer. The graph attention layer defines how to transfer the hidden node representations at layer $k-1$ (denoted as $H^{k-1} \in \mathbb{R}^{N \times F}$ ) to the new node representations $H^{k} \in \mathbb{R}^{N \times F^{\prime}}$. In order to guarantee sufficient expressive power to transform the lower-level node representations to higher-level node representations, a shared linear transformation is applied to every node, denoted as $W \in \mathbb{R}^{F \times F^{\prime}}$. Afterwards, self-attention is defined on the nodes, which measures the attention coefficients for any pair of nodes through a shared attentional mechanism $a: \mathbb{R}^{F^{\prime}} \times \mathbb{R}^{F^{\prime}} \rightarrow$ R
$$e_{i j}=a\left(W H_{i}^{k-1}, W H_{j}^{k-1}\right) .$$

## 计算机代写|神经网络代写neural networks代考|Neural Message Passing Networks

Another very popular graph neural network architecture is the Neural Message Passing Network (MPNN) (Gilmer et al, 2017), which is originally proposed for learning molecular graph representations. However, MPNN is actually very general, provides a general framework of graph neural networks, and could be used for the task of node classification as well. The essential idea of MPNN is formulating existing graph neural networks as a general framework of neural message passing among nodes. In MPNNs, there are two important functions including Message and Updating function:
$$\begin{gathered} m_{i}^{k}=\sum_{i \in N(j)} M_{k}\left(H_{i}^{k-1}, H_{j}^{k-1}, e_{i j}\right), \ H_{i}^{k}=U_{k}\left(H_{i}^{k-1}, m_{i}^{k}\right) . \end{gathered}$$
$M_{k}(\cdot,,,$, defines the message between node $i$ and $j$ in the k-th layer, which depends on the two node representations and the information of their edge. $U_{k}$ is the node updating function in the k-th layer which combines the aggregated messages from the neighbors and the node representation itself. We can see that the MPNN framework is very similar to the general framework we introduced in Section 4.2.1. The AGGREGATE function defined here is simply a summation of all the messages from the neighbors. The COMBINE function is the same as the node Updating function.

# 深度学习代写

## 计算机代写|神经网络代写neural networks代考|Graph Attention Networks

$$e_{i j}=a\left(W H_{i}^{k-1}, W H_{j}^{k-1}\right) .$$

## 计算机代写|神经网络代写neural networks代考|Neural Message Passing Networks

$$m_{i}^{k}=\sum_{i \in N(j)} M_{k}\left(H_{i}^{k-1}, H_{j}^{k-1}, e_{i j}\right), H_{i}^{k}=U_{k}\left(H_{i}^{k-1}, m_{i}^{k}\right) .$$
$M_{k}\left(\cdot,,\right.$, 定义节点之间的消息 $i$ 和 $j$ 在第 $\mathrm{k}$ 层，这取决于两个节点的表示及其边缘的信息。 $U_{k}$ 是第 $\mathrm{k}$ 层中的节点更新函数，它结合了来自邻居的聚合消息和节点表 示本身。我们可以看到 MPNN 框架与我们在 4.2.1 节介绍的通用框架非常相似。这里定义的 AGGREGATE 函数只是来自邻居的所有消息的总和。COMBINE功能与 节点更新功能相同。

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

assignmentutor™您的专属作业导师
assignmentutor™您的专属作业导师