heterogeneous attention network graph
【NIPS2021】Focal Self-attention for Local-Global Interactions in Vision Transformers
来自微软(*^____^*) 论文地址:[2107.00641] Focal Self-attention for Local-Global Interactions in Vision Transformers (arxiv.org) 代码地址:microsoft/Focal-Transforme ......
yarn 出现 【 info There appears to be trouble with your network connection. Retrying... 】超时问题解决
第一种解决方案 # 调整为taobao镜像源 yarn config set registry https://registry.npm.taobao.org 我用了没用,可以试试 第二种解决方案 要在项目根目录下创建后缀名为 .yarnrc 的文件,并设置 network-timeout 的值为 ......
Sensor Network
题目描述 A wireless sensor network consists of autonomous sensors scattered in an environment where they monitor conditions such as temperature, sound, an ......
Learning Heterogeneous Temporal Patterns of User Preference for Timely Recommendation
目录概符号说明TimelyRecMulti-aspect Time Encoder (MATE)Time-aware History Encoder (TAHE)Prediction代码 Cho J., Hyun D., Kang S. and Yu H. Learning heterogeneou ......
Attention Mixtures for Time-Aware Sequential Recommendation
目录概符号说明MOJITO代码 Tran V., Salha-Galvan G., Sguerra B. and Hennequin R. Attention mixtures for time-aware sequential recommendation. SIGIR, 2023. 概 本文希望 ......
docker network
docker network 常用命令 docker network ls 查看所有的网络模式 docker network create 创建网络模式 docker network rm 删除网络模式 docker network inspect xxx 查看网络模式的信息 网络模式 bridge ......
ACL2022 paper1 CAKE: A Scalable Commonsense-Aware Framework for Multi-View Knowledge Graph Completion
CAKE:用于多视域知识图谱补全的可扩展常识感知框架 ACL2022 Abstract 知识图谱存储大规模事实三元组,然而不可避免的是图谱仍然具有不完整性。(问题)以往的只是图谱补全模型仅仅依赖于事实域数据进行实体之间缺失关系的预测,忽略了宝贵的常识知识。以往的知识图嵌入技术存在无效负抽样和事实域链 ......
CF662B Graph Coloring
很一眼的题 考虑枚举最后所有边的颜色,然后每个点是否变化可以用一个bool变量表示,就是个很典的2-SAT问题,根据当前边和目标的颜色相同与否连边即可 但这题的难点在于要找一个操作次数最少的方案,乍一看很难搞 但如果你对图论和2-SAT那一套理解比较深的话就很容易发现,这道题中所有边都是双向的 这就 ......
DGCF:Disentangled Graph Collaborative Filtering论文解读及代码
VAE和β-VAE解读 https://spaces.ac.cn/archives/5253 https://blog.csdn.net/Wendy_WHY_123/article/details/104711108 论文笔记 https://blog.csdn.net/SeanChau/artic ......
【CVPR2022】Shunted Self-Attention via Multi-Scale Token Aggregation
来自CVPR2022 基于多尺度令牌聚合的分流自注意力 论文地址:[2111.15193] Shunted Self-Attention via Multi-Scale Token Aggregation (arxiv.org) 项目地址:https://github.com/OliverRensu ......
VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE
(VGG)VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION 阅读笔记(22.10.05) 摘要:本文研究在大规模图像识别设置中卷积网络深度对其准确性的影响。主要贡献是对使用(3,3)卷积核的体系结构增加深度的网络进行 ......
VDSR-Accurate Image Super-Resolution Using Very Deep Convolutional Networks阅读笔记
Accurate Image Super-Resolution Using Very Deep Convolutional Networks(VDSR)阅读笔记(22.10.07)使用深度卷积网络的精确图像超分辨率 摘要:使用一个非常深的卷积神经网络,灵感来源于VGG-Net。本文发现,网络深度增加 ......
【学习笔记】Self-attention
最近想学点NLP的东西,开始看BERT,看了发现transformer知识丢光了,又来看self-attention;看完self-attention发现还得再去学学word embedding... 推荐学习顺序是:word embedding、self-attention / transform ......
[VLDB 2012]Efficient Subgraph Matching on Billion Node Graphs
[VLDB 2012]Efficient Subgraph Matching on Billion Node Graphs 重点了解实现star-join的具体过程。 分解query和STwigs排序 文中把star叫做STwigs,每一个STwigs查询为\(q=(r, L)\),其中r是跟节点标 ......
web DevOps / qemu / kvm nat / kvm network / danei network
s [root@euler share]# rpm -qa | grep openssh # 查看ssh openssh-8.8p1-21.oe2203sp2.x86_64 openssh-server-8.8p1-21.oe2203sp2.x86_64 openssh-clients-8.8p1- ......
Graph transduction via alternating minimization
目录概符号说明GTAM交替优化求解 Wang J., Jebara T. and Chang S. Graph transduction via alternating minimization. ICML, 2008. 概 一种对类别不均更鲁棒的半监督算法. 符号说明 \(\mathcal{X}_ ......
linux报错“Failed to start LSB: Bring up/down networking.”
1 简介 虚拟机安装CentOs7完成后,配置静态网络,重启网络失败 执行ststemctl status network.service 发现报错:Failed to start LSB: Bring up/down networking 2 原因 由于centos7中没有70-persisten ......
Graph Construction and b-Matching for Semi-Supervised Learning
目录概符号说明图的构建Graph Sparsification\(\epsilon\)-neighborhood graph\(k\)NN graph\(b\)-MatchingGraph Edge Re-Weighting Jebara T., Wang J. and Chang S. Graph ......
集合论和图论(Graph Theory)的应用
图论〔Graph Theory〕是数学的一个分支。它以图为研究对象。图论中的图是由若干给定的点及连接两点的线所构成的图形,这种图形通常用来描述某些事物之间的某种特定关系,用点代表事物,用连接两点的线表示相应两个事物间具有这种关系。 >>应用1:泰森多边形。 >>应用2:TIN、三维模型(obj、me ......
How Expressive are Graph Neural Networks in Recommendation
[TOC] > [Cai X., Xia L., Ren X. and Huang C. How expressive are graph neural networks in recommendation? CIKM, 2023.](http://arxiv.org/abs/2308.11127) ......
CF1857G Counting Graphs
`2023-08-08 23:00:07 solution` ## 题意: 求有多少个有 $n$ 个节点的无向图,使其满足以下条件: - 无重边自环。 - 有且只有一个最小生成树,且为给定树。 - 最大边权不大于 $S$。 对 $998244353$ 取模。 ## 思路: 其实就是让我们在给定的树加 ......
Proj CDeepFuzz Paper Reading: Testing Deep Neural Networks
## Abstract 本文:DeepCover Github: https://github.com/TrustAI/DeepCover Task: propose 4 novel test criteria to test DNNs Method: inspired by MC/DC cover ......
[network] netcat install in windows os
# Netcat Install in Windows OS + Netcat is a simple `Unix` tool. it uses `UDP` , `TCP` Protocol. > Netcat 是一个可靠的容易被其他程序所启用的后台操作工具,同时它也被用作**网络的测试工具**或* ......
Proj CDeepFuzz Paper Reading: SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks
## Abstract 本文:SparseProp Github: https://github.com/IST-DASLab/sparseprop Task: a back-propagation algo for sparse training data, a fast vectorized i ......
Spikformer: When Spiking Neural Network Meets Transformer
郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! Published as a conference paper at ICLR 2023(同大组工作) ABSTRACT 我们考虑了两种生物学合理的结构,脉冲神经网络(SNN)和自注意机制。前者为深度学习提供了一种节能且事件驱动的范式,而 ......
Proj CDeepFuzz Paper Reading: Decompiling x86 Deep Neural Network Executables
## Abstract 本文: BTD github: https://github.com/monkbai/DNN-decompiler/ Task: a decompiler for DNN models to output DNN specifications including: opera ......
【论文阅读】CAT: Cross Attention in Vision Transformer
论文地址:[2106.05786] CAT: Cross Attention in Vision Transformer (arxiv.org) 项目地址:https://github.com/linhezheng19/CAT 一、Abstract 由于Transformer在NLP中得到了广泛的应 ......
BZOJ3732 Network 题解 Kruskal重构树入门题
题目链接:[https://hydro.ac/d/bzoj/p/3732](https://hydro.ac/d/bzoj/p/3732) 题目大意: 给定一个图,每次询问两个点 $u$ 和 $v$,在 $u$ 到 $v$ 的所有路径中找一条路径,且这条路径上的所有边的边权最大值最小。 解题思路: ......
A Contextualized Temporal Attention Mechanism for Sequential Recommendation
[TOC] > [Wu J., Cai R. and Wang H. D\'ej\`a vu: A contextualized temporal attention mechanism for sequential recommendation. WWW, 2020.](http://arxiv. ......