networks neural bigdataaiml-ibm-a introduction
Guo_AD-NeRF_Audio_Driven_Neural_Radiance_Fields_for_Talking_Head_Synthesis_ICCV_2021_paper
可以看看这个向量场的虚拟人像的效果. 看论文第三章: 3.2: F_theta是一个神经网络, a是声音 d 是view direction, x是3d location. 普通的向量场是 F_theta: d,x > (c,σ) 表示d是一个方向, 表示观看者水平的偏移角度和数值的偏移角度. x是 ......
Graph Neural Networks with Learnable and Optimal Polynomial Bases
目录概符号说明MotivationFavardGNN代码 Guo Y. and Wei Z. Graph neural networks with learnable and optimal polynomial bases. ICML, 2023. 概 自动学多项式基的谱图神经网络. 符号说明 \ ......
[论文速览] R-Drop@ Regularized Dropout for Neural Networks
Pre title: R-Drop: Regularized Dropout for Neural Networks accepted: NeurIPS 2021 paper: https://arxiv.org/abs/2106.14448 code: https://github.com/dro ......
神经网络入门篇：详解深层网络中的前向传播（Forward propagation in a Deep Network）
深层网络中的前向传播 先说对其中一个训练样本\(x\)如何应用前向传播，之后讨论向量化的版本。 第一层需要计算\({{z}^{[1]}}={{w}^{[1]}}x+{{b}^{[1]}}\)，\({{a}^{[1]}}={{g}^{[1]}} {({z}^{[1]})}\)（\(x\)可以看做\({ ......
论文：Predicting the performance of green stormwater infrastructure using multivariate long short-term memory (LSTM) neural network
题目“Predicting the performance of green stormwater infrastructure using multivariate long short-term memory (LSTM) neural network” (Al Mehedi 等, 2023, ......
NS-3源码学习（四）wifi-ent-network.cc
NS-3源码学习（四）wifi-ent-network.cc 设定的参数 bool udp{true};udp/tcp 通信选择 bool downlink{true};AP -> STA : downlink = true / STA -> AP : downlink = false 数据发送方向 ......
论文：FEED-FORWARD NETWORKS WITH ATTENTION CAN SOLVE SOME LONG-TERM MEMORY PROBLEMS
题目：FEED-FORWARD NETWORKS WITH ATTENTION CAN SOLVE SOME LONG-TERM MEMORY PROBLEMS” (Raffel 和 Ellis, 2016, p. 1) “带有注意力的前馈网络可以解决一些长期记忆问题” (Raffel 和 Elli ......
20231128 - 重启Centos后无法远程连接，重启网络服务报错：Error:Failed to start LSB: Bring up/down networking
1.https://blog.csdn.net/m0_74953387/article/details/132914306 2.https://blog.csdn.net/weixin_45894220/article/details/130487066 ......
The Hello World of Deep Learning with Neural Networks
The Hello World of Deep Learning with Neural Networks dlaicourse/Course 1 - Part 2 - Lesson 2 - Notebook.ipynb at master · lmoroney/dlaicourse (github ......
The Hello World of Deep Learning with Neural Networks
The Hello World of Deep Learning with Neural Networks dlaicourse/Course 1 - Part 2 - Lesson 2 - Notebook.ipynb at master · lmoroney/dlaicourse (github ......
CrossEntropyLoss: RuntimeError: expected scalar type Float but found Long neural network
错误分析 这个错误通常指的是期望接受的参数类型是Float， 但是程序员传入的是Int 。 通常会需要我们去检查传入的 input 和 target 的数据类型有没有匹配。在传入的数据中，通常 input 希望是 Float 类型，target 是 Int 类型。 但是通常也许会发现传入的参数是符合 ......
Graph Neural Networks with Diverse Spectral Filtering
目录概符号说明DSF代码 Guo J., Huang K, Yi X. and Zhang R. Graph neural networks with diverse spectral filtering. WWW, 2023. 概 为每个结点赋予不同的多项式系数. 符号说明 \(\mathcal{ ......
Firefox developer tools truncates long network response, Chrome does not show
Firefox developer tools truncates long network response, Chrome does not show Firefox dev tools network inspector still truncates responses to 1MB by ......
Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited
目录概符号说明MotivationChebNetII代码 He M., Wei Z. and Wen J. Convolutional neural networks on graphs with chebyshev approximation, revisited. NIPS, 2022. 概 作 ......
如何给vite代理的network中显示代理地址
vite 代理的项目，一般看不到代理的目标地址 如图： 如果要查看代理的目标地址，本文提供两种方式 1，configure配置 如图，通过configure，我们可以拿到proxy代理实例，通过注册on事件，可以在回调函数里面拿到目标地址和请求的路径，从而设置header 2， bypass配置 其 ......
论文阅读笔记：Revisiting Prototypical Network for Cross Domain Few-Shot Learning
标题：重新审视用于跨领域少样本学习的原型网络 研究背景： 问题背景：原型网络是一种流行的小样本学习方法， 其网络简单而直观，对于小样本学习问题有着较好的表现，尤其是在图像分类等领域。 存在问题：然而，当推广到跨领域的少样本分类任务时，其性能出现了大幅度下降，这严重限制了原型网络的实用性。 研究动机： ......
Grafana学习（8）——Introduction to Alerting
Whether you’re just starting out or you’re a more experienced user of Grafana Alerting, learn more about the fundamentals and available features that ......
Grafana学习（6）——Introduction to exemplars及Glossary
An exemplar is a specific trace representative of measurement taken in a given time interval. While metrics excel at giving you an aggregated view of ......
Grafana学习（5）——Introduction to histograms and heatmaps
A histogram is a graphical representation of the distribution of numerical data. It groups values into buckets (sometimes also called bins) and then c ......
Grafana学习（3）——Introduction to time series
Imagine you wanted to know how the temperature outside changes throughout the day. Once every hour, you’d check the thermometer and write down the tim ......
爬虫获取网页开发者模式NetWork信息
using System; using System.Collections.Generic; using System.Linq; using System.Threading; using System.Threading.Tasks; using OpenQA.Selenium; using ......
How Powerful are Spectral Graph Neural Networks?
目录概符号说明Spectral GNNChoice of Basis for Polynomial FiltersJacobiConv代码 Wang X. and Zhang M. How powerful are spectral graph neural networks? ICML, 2022 ......
【略读论文|时序知识图谱补全】Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning
会议：IJCAI，时间：2023，学校：1 中国科学院计算机网络信息中心，北京 2中国科学院大学，北京 3 澳门大学智慧城市物联网国家重点实验室，澳门 4 香港科技大学(广州)，广州 5 佛罗里达大学计算机科学系，奥兰多 摘要： 提出一种新的具有TKG关联特征的体系结构建模方法，即自适应路径-记忆网 ......
神经网络入门篇：神经网络的梯度下降（Gradient descent for neural networks）
神经网络的梯度下降 在这篇博客中，讲的是实现反向传播或者说梯度下降算法的方程组 单隐层神经网络会有\(W^{[1]}\)，\(b^{[1]}\)，\(W^{[2]}\)，\(b^{[2]}\)这些参数，还有个\(n_x\)表示输入特征的个数，\(n^{[1]}\)表示隐藏单元个数，\(n^{[2]} ......
How Attentive are Graph Attention Networks?
目录概符号说明GATv2代码 Brody S., Alon U. and Yahav E. How attentive are graph attention networks? ICLR, 2022. 概 作者发现了 GAT 的 attention 并不能够抓住边的重要性, 于是提出了 GATv2 ......
Optimized Content Caching and User Association for Edge Computing in Densely Deployed Heterogeneous Networks论文阅读
目录Optimized Content Caching and User Association for Edge Computing in Densely Deployed Heterogeneous Networks1、问题背景贡献点：2、系统建模及问题公式化系统建模问题公式化联合内容缓存和用户 ......
Decoupling the Depth and Scope of Graph Neural Networks
目录概符号说明Shadow-GNN代码 Zeng H., Zhang M., Xia Y., Srivastava A., Malevich A., Kannan R., Prasanna V., Jin L. and Chen R. Decoupling the depth and scope o ......
Chen Shuo's Practical Network Programming - TTCP Lecture代码注释
下面是C语言版本的TTCP，主要注释的是void receive(const Options& opt);函数，负责在服务器接收客户端发送的数据： // muduo/examples/ace/ttcp/ttcp_blocking.cc #include ... // 接受新的TCP连接 static ......
study of 'Missing data imputation framework for bridge structural health monitoring based on slim generative adversarial networks'
the Stochastic Gradient Descent (SGD):为了提高鲁棒性，SGAIN框架的优化器采用了随机梯度下降（SGD） 一，SGAIN框架有两个重要目的：鉴别器D的目的是最大化正确预测M矩阵的概率；生成器的目的是最小化D预测M矩阵的概率。此外，利用反向传播算法对发生器和鉴别器 ......