## 计算机代写|神经网络代写neural networks代考|NIT6004

statistics-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

Traditional graph embedding methods are originally studied as dimension reduction techniques. A graph is usually constructed from a feature represented data set, like image data set. As mentioned before, graph embedding usually has two goals, i.e. reconstructing original graph structures and support graph inference. The objective functions of traditional graph embedding methods mainly target the goal of graph reconstruction.

Specifically, Tenenbaum et al (2000) first constructs a neighborhood graph $G$ using connectivity algorithms such as $K$ nearest neighbors (KNN). Then based on $G$, the shortest path between different data can be computed. Consequently, for all the $N$ data entries in the data set, we have the matrix of graph distances. Finally, the classical multidimensional scaling (MDS) method is applied to the matrix to obtain the coordinate vectors. The representations learned by Isomap approximately preserve the geodesic distances of the entry pairs in the low-dimensional space. The key problem of Isomap is its high complexity due to the computing of pair-wise shortest pathes. Locally linear embedding (LLE) (Roweis and Saul, 2000) is proposed to eliminate the need to estimate the pairwise distances between widely separated entries. LLE assumes that each entry and its neighbors lie on or close to a locally linear patch of a mainfold. To characterize the local geometry, each entry can be reconstructed from its neighbors. Finally, in the low-dimensional space, LLE constructs a neighborhood-preserving mapping based on locally linear reconstruction. Laplacian eigenmaps (LE) (Belkin and Niyogi, 2002) also begins with constructing a graph using $\varepsilon$-neighborhoods or $\mathrm{K}$ nearest neighbors. Then the heat kernel (Berline et al, 2003) is utilized to choose the weight of two nodes in the graph. F1nally, the node representations can be obtained by based on the Laplacian matrix regularization. Furthermore, the locality preserving projection (LPP) (Berline et al, 2003), a linear approximation of the nonlinear LE, is proposed.

## 计算机代写|神经网络代写neural networks代考|Structure Preserving Graph Representation Learning

Graph structures can be categorized into different groups that present at different granularities. The commonly exploited graph structures in graph representation learning include neighborhood structure, high-order node proximity and graph communities.

How to define the neighborhood structure in a graph is the first challenge. Based on the discovery that the distribution of nodes appearing in short random walks is similar to the distribution of words in natural language, DeepWalk (Perozzi et al, 2014) employs the random walks to capture the neighborhood structure. Then for each walk sequence generated by random walks, following Skip-Gram, DeepWalk aims to maximize the probability of the neighbors of a node in a walk sequence. Node2vec defines a flexible notion of a node’s graph neighborhood and designs a second order random walks strategy to sample the neighborhood nodes, which can smoothly interpolate between breadth-first sampling (BFS) and depth-first sampling (DFS). Besides the neighborhood structure, LINE (Tang et al, 2015b) is proposed for large scale network embedding. which can preserve the first and second order proximities. The first order proximity is the observed pairwise proximity between two nodes. The second order proximity is determined by the similarity of the “contexts” (neighbors) of two nodes. Both are important in measuring the relationships beetween two nodess. Essentially, LINE is based on the shallow model, consequently, the representation ability is limited. SDNE (Wang et al, 2016) proposes a deep model for network embedding, which also aims at capturing the first and second order proximites. SDNE uses the deep auto-encoder architecture with multiple non-linear layers to preserve the second order proximity. To preserve the first-order proximity, the idea of Laplacian eigenmaps (Belkin and Niyogi, 2002) is adopted. Wang et al (2017g) propose a modularized nonnegative matrix factorization (M-NMF) model for graph representation learning, which aims to preserve both the microscopic structure, i.e., the first-order and second-order proximities of nodes, and the mesoscopic community structure (Girvan and Newman, 2002).

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

## 计算机代写|神经网络代写neural networks代考|STAT3007

statistics-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|Representation Learning for Networks

Beyond popular data like images, texts, and sounds, network data is another important data type that is becoming ubiquitous across a large scale of real-world applications ranging from cyber-networks (e.g., social networks, citation networks, telecommunication networks, etc.) to physical networks (e.g., transportation networks, biological networks, etc). Networks data can be formulated as graphs mathematically, where vertices and their relationships jointly characterize the network information. Networks and graphs are very powerful and flexible data formulation such that sometimes we could even consider other data types like images, and texts as special cases of it. For example, images can be considered as grids of nodes with RGB attributes which are special types of graphs, while texts can also be organized into sequential-, tree-, or graph-structured information. So in general, representation learning for networks is widely considered as a promising yet more challenging tasks that require the advancement and generalization of many techniques we developed for images, texts, and so forth. In addition to the intrinsic high complexity of network data, the efficiency of representation learning on networks is also an important issues considering the large-scale of many real-world networks, ranging from hundreds to millions or even billions of vertices. Analyzing information networks plays a crucial role in a variety of emerging applications across many disciplines. For example, in social networks, classifying users into meaningful social groups is useful for many important tasks, such as user search, targeted advertising and recommendations; in communication networks, detecting community structures can help better understand the rumor spreading process; in biological networks, inferring interactions between proteins can facilitate new treatments for diseases. Nevertheless, efficient and effective analysis of these networks heavily relies on good representations of the networks.

## 计算机代写|神经网络代写neural networks代考|Graph Representation Learning: An Introduction

Many complex systems take the form of graphs, such as social networks, biological networks, and information networks. It is well recognized that graph data is often sophisticated and thus is challenging to deal with. To process graph data effectively, the first critical challenge is to find effective graph data representation, that is, how to represent graphs concisely so that advanced analytic tasks, such as pattern discovery, analysis, and prediction, can be conducted efficiently in both time and space.

Traditionally, we usually represent a graph as $\mathscr{G}=(\mathscr{V}, \mathscr{E})$, where $\mathscr{V}$ is a node set and $\mathscr{E}$ is an edge set. For large graphs, such as those with billions of nodes, the traditional graph representation poses several challenges to graph processing and analysis.
(1) High computational complexity. These relationships encoded by the edge set $E$ take most of the graph processing or analysis algorithms either iterative or combinatorial computation steps. For example, a popular way is to use the shortest or average path length between two nodes to represent their distance. To compute such a distance using the traditional graph representation, we have to enumerate many possible paths between two nodes, which is in nature a combinatorial problem. Such methods result in high computational complexity that prevents them from being applicable to large-scale real-world graphs.
(2) Low parallelizability. Parallel and distributed computing is de facto to process and analyze large-scale data. Graph data represented in the traditional way, however, casts severe difficulties to design and implementat of parallel and distributed algorithms. The bottleneck is that nodes in a graph are coupled to each other explicitly reflected by $E$. Thus, distributing different nodes in different shards or servers often causes demandingly high communication cost among servers, and holds back speed-up ratio.

## 计算机代写|神经网络代写neural networks代考|Graph Representation Learning: An Introduction

(1) 计算复杂度高。这些由边集编码的关系和采用大多数图形处理或分析算法迭代或组合计算步骤。例如，一种流行的方法是使用两个节点之间的最短或平均路径长度来表示它们的距离。为了使用传统的图形表示来计算这样的距离，我们必须枚举两个节点之间的许多可能路径，这本质上是一个组合问题。这样的方法导致高计算复杂性，从而阻止它们适用于大规模的真实世界图。
(2) 并行性低。并行和分布式计算实际上是处理和分析大规模数据。然而，以传统方式表示的图形数据给并行和分布式算法的设计和实现带来了严重的困难。瓶颈是图中的节点相互耦合，显式反映为和. 因此，将不同的节点分布在不同的分片或服务器中往往会导致服务器之间的通信成本很高，并且会阻碍加速比。

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

## 计算机代写|神经网络代写neural networks代考|COMP5329

statistics-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|Representation Learning for Speech Recognition

Nowadays, speech interfaces or systems have become widely developed and integrated into various real-life applications and devices. Services like Siri ${ }^{1}$, Cortana ${ }^{2}$, and Google Voice Search ${ }^{3}$ have become a part of our daily life and are used by millions of users. The exploration in speech recognition and analysis has always been motivated by a desire to enable machines to participate in verbal human-machine interactions. The research goals of enabling machines to understand human speech, identify speakers, and detect human emotion have attracted researchers’ attention for more than sixty years across several distinct research areas, including but not limited to Automatic Speech Recognition (ASR), Speaker Recognition (SR), and Speaker Emotion Recognition (SER).

Analyzing and processing speech has been a key application of machine learning (ML) algorithms. Research on speech recognition has traditionally considered the task of designing hand-crafted acoustic features as a separate distinct problem from the task of designing efficient models to accomplish prediction and classification decisions. There are two main drawbacks of this approach: First, the feature engineering is cumbersome and requires human knowledge as introduced above; and second, the designed features might not be the best for the specific speech recognition tasks at hand. This has motivated the adoption of recent trends in the speech community towards the utilization of representation learning techniques, which can learn an intermediate representation of the input signal automatically that better fits into the task at hand and hence lead to improved performance. Among all these successes, deep learning-based speech representations play an important role. One of the major reasons for the utilization of representation learning techniques in speech technology is that speech data is fundamentally different from two-dimensional image data. Images can be analyzed as a whole or in patches, but speech has to be formatted sequentially to capture temporal dependency and patterns.

## 计算机代写|神经网络代写neural networks代考|Representation Learning for Natural Language Processing

Besides speech recognition, there are many other Natural Language Processing (NLP) applications of representation learning, such as the text representation learning. For example, Google’s image search exploits huge quantities of data to map images and queries in the same space (Weston et al, 2010) based on NLP techniques. In general, there are two types of applications of representation learning in $\mathrm{NLP}$. In one type, the semantic representation, such as the word embedding, is trained in a pre-training task (or directly designed by human experts) and is transferred to the model for the target task. It is trained by using language modeling objective and is taken as inputs for other down-stream NLP models. In the other type, the semantic representation lies within the hidden states of the deep learning model and directly aims for better performance of the target tasks in an end-to-end fashion. For example, many NLP tasks want to semantically compose sentence or document representation, such as tasks like sentiment classification, natural language inference, and relation extraction, which require sentence representation.

Conventional NLP tasks heavily rely on feature engineering, which requires careful design and considerable expertise. Recently, representation learning, especially deep learning-based representation learning is emerging as the most important technique for NLP. First, NLP is typically concerned with multiple levels of language entries, including but not limited to characters, words, phrases, sentences, paragraphs, and documents. Representation learning is able to represent the semantics of these multi-level language entries in a unified semantic space, and model complex semantic dependence among these language entries. Second, there are various NLP tasks that can be conducted on the same input. For example, given a sentence, we can perform multiple tasks such as word segmentation, named entity recognition, relation extraction, co-reference linking, and machine translation. In this case, it will be more efficient and robust to build a unified representation space of inputs for multiple tasks. Last, natural language texts may be collected from multiple domains, including but not limited to news articles, scientific articles, literary works, advertisement and online user-generated content such as product reviews and social media. Moreover, texts can also be collected from different languages, such as English, Chinese, Spanish, Japanese, etc. Compared to conventional NLP systems which have to design specific feature extraction algorithms for each domain according to its characteristics, representation learning enables us to build representations automatically from large-scale domain data and even add bridges among these languages from different domains. Given these advantages of representation learning for NLP in the feature engineering reduction and performance improvement, many researchers have developed efficient algorithms on representation learning, especially deep learning-based approaches, for NLP.

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。