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  • Masked Language Modeling for Proteins via LinearlyScalable Long-Context Transformers2021-03-07 20:59:24

    摘要 transformer模型已在各种领域中取得了最先进的结果。 但是,对训练注意力机制以学习远程输入之间的复杂依存关系的成本的担忧不断增加。利用学习的注意力矩阵的结构和稀疏性的解决方案出现了。 但是,涉及长序列的实际应用(例如生物序列分析)可能无法满足这些假设,从而无法探索

  • aws deployment2021-03-03 06:32:26

    User submit Docker file to Chancellor,  Chanceller sumbit to AWS cloudFormation  and then to ECR for build if needed.  Crate DNS entry via Route 53.  Create ECS task and services.   

  • [论文分享] DHP: Differentiable Meta Pruning via HyperNetworks2021-03-03 02:01:06

    转: [论文分享] DHP: Differentiable Meta Pruning via HyperNetworks authors: Yawei Li1, Shuhang Gu, etc. comments: ECCV2020 cite: [2003.13683] DHP: Differentiable Meta Pruning via HyperNetworks (arxiv.org) code: ofsoundof/dhp: This is the official implementatio

  • DySAT: Deep Neural Representation Learning on Dynamic Graph via Self-Attention Networks2021-03-02 16:59:26

    文章目录 1 前言2 问题定义2.1 dynamic graph 3 DySAT思路3.1 Structural Self-Attention3.2 Temporal Self-Attention 4 方法的优势与局限性4.1 优势4.2 局限性 论文地址:http://yhwu.me/publications/dysat_wsdm20.pdf源码:DySAT来源:WSDM, 2020关键词:self-attention, r

  • 《SCDA:Adapting Object Detectors via Selective Cross-Domain Alignment》论文笔记2021-02-16 14:57:25

    参考代码:SCDA 1. 概述 导读:在之前的Domain Adaption文章中主要是针对分类/分割场景任务,对于检测场景下的挖掘不够,这是由于分类/分割场景关注的是整个特征图范围上的表现,而检测却是具有局部性的,因而直接将分类/分割的域迁移方法引入是不合时宜的。对此文章按照监测网络的特性

  • Spring整合mybatis用阿里连接池的配置错误2021-02-08 19:33:38

    项目场景: 提示:这里简述项目相关背景: Sping整合mybati出现错误 问题描述: . You must configure either the server or JDBC driver (via the serverTimezone configuration property) to use a more specifc time zone value if you want to utilize time zone support.

  • Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks论文理解2021-02-08 15:59:24

    [标题] 《Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks》 [代码地址] https://github.com/CRIPAC-DIG/TextING [知识储备] 什么是GNN(Graph Neural Networks)? 什么是transductive learning 和 inductive learning? 目录

  • 论文笔记(未完)——通过影响函数理解黑盒预测(Understanding Black-box Predictions via Influence Functions)2021-01-23 22:29:03

    论文——Understanding Black-box Predictions via Influence Functions 1. 介绍2. 方法2.1 一些定义2.2 更新(扰动)一个训练点 未完待更新 1. 介绍 《Understanding Black-box Predictions via Influence Functions》 这篇paper是来自2017年的ICML best paper的,其背景在摘

  • 详解Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding2021-01-22 23:59:52

    Markdown Motivation 理解从图像上提取平面。这个是最新的图片的平面提取。 论文序列 3)Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding 论文目标 为什么能够提取平面 它包括三个部分,一个语义提取平面和非平面的mask的encoder(典型的语音分割);一

  • 【论文阅读】Relation classification via multi-level attention CNNs[ACL2016]2021-01-18 20:32:24

    原文链接:https://www.aclweb.org/anthology/P16-1123.pdf  代码实现:https://github.com/dgai91/pytorch-acnn-model   关系抽取中我们会遇到很多表达同一种关系的不同方式。这种具有挑战性的变异性在本质上可以是词汇的lexical、语法的syntactic,甚至是语用pragmatic的。一个有

  • 模型基元2021-01-12 22:34:32

    体素 3、occupancy mapping has been popular in robotics, and represents space using a grid of cells,within each of which a probability of occupancy is accumulated via Bayesian updates every time a new range scan provides an informative observation. ——《

  • 记录一次失败的Nginx via cygwin编译体验2021-01-07 18:01:39

    Generate make file: Linux: ./auto/configure \ --prefix= \ --conf-path=conf/nginx.conf \ --pid-path=logs/nginx.pid \ --http-log-path=logs/access.log \ --error-log-path=logs/error.log \ --with-pcre=lib/pcre-8.44 \ --with-z

  • sqlserver 无法初始化via支持库[QLVIPL.DLL]2020-12-31 12:57:52

    安装数据库后,在sqlserver configuration manager, sqlserver的网络配置,有将协议 shared memory,named pipes,tcp/ip,via全部启用后     出现SQLSERVER无法启动的问题,错误信息如下: SQL Server 无法初始化 VIA 支持库 [QLVipl.dll]。这通常指示 VIA 支持库不存在或已损坏。请

  • 解决新机器 connect:Network is unreachable的方法2020-12-30 20:00:17

    解决新机器 connect: Network is unreachable的方法 1 给网卡绑定地址 输入  ip a 查看要绑定的网卡名称,且确定其没有绑定地址。 进入并打开 vim /etc/sysconfig/network-scripts/ifconfig+网卡名   IPADDR = 输入分配的ip地址   重启网卡 systemctl restart network   输入i

  • 实战中学习TCPIP模型——互联网层2020-12-19 17:33:33

    文章目录 实战中学习TCP/IP模型——互联网层Why?HOW?隔离广播——划分子网范围寻址——路由表实战:使用三台Linux主机作为路由器,使得两台Linux主机,IP分别为172.16.0.100/16,172.22.0.100/16能够相互通信。环境搭建配置网段配置网卡 路由配置思路主机CentOS6路由器R1路由器R2

  • Linkage Based Face Clustering via Graph Convolution Network2020-12-10 18:32:38

    论文:Linkage Based Face Clustering via Graph Convolution Network 代码:https://github.com/Zhongdao/gcn_clustering 本文使用GCN进行人脸聚类。

  • 图像标注2020-12-09 18:02:38

    图像标注主要有2个工具:via和labelme via http://www.robots.ox.ac.uk/~vgg/software/via/ 暂时没研究 labelme labelme可以做: 图像分类标注:Classification 目标检测标注:Object Detection 语义分割标注:Semantic Segmentation 实例分割标注:Instance Segmentation 视频标注:Video Anno

  • 《ENAS:Efficient Neural Architecture Search via Parameter Sharing》论文笔记2020-12-02 20:03:34

    参考代码:enas 1. 概述 导读:这篇文章是在NAS的基础上提出使用权值共享的方式进行网络搜索,避免了控制器采样得到sample的重复训练,从而压缩整体搜索时间的网络搜索算法ENAS。在NAS中首先由控制器采样出一个网络结构,之后将其训练到收敛,之后将该采样网络的性能作为控制器的reward

  • C# export generic data via Microsoft.Office.Interop.Excel2020-11-30 20:36:58

    using System; using System.Collections.Generic; using System.Linq; using System.Text; using System.Threading.Tasks; using Excel = Microsoft.Office.Interop.Excel; using System.Reflection; using Microsoft.Office.Interop.Excel; using System.Windows.Forms; us

  • C# Serialize object via System.Text.Json2020-11-24 20:31:52

    using System.Text.Json; static void TextJsonDemo() { var obj = new { Id = 1, Name = "Fred", Age = 33, Org = new[] {

  • Suzuki Swift odometer correction via Yanhua digiprog3 , success2020-11-21 12:32:53

    Tutorial: How to adjustment mileage for Suzuki Swift 24C16 with Digiprog III v4.94 via OBD Vehicle: Suzuki Swift 24C16 Tool used: Yanhua Digiprog 3 Work: change mileage from 214208KM to 123200KM Procedures: The first, connect your car and dp3 mileage corr

  • 《CLR via C#》书籍2020-11-15 21:01:30

    目录 转自:CLR via C#--知乎,第三版 第I部分 CLR基础第1章 CLR的执行模型 31.1 将源代码编译成托管模块 31.2 将托管模块合并成程序集 61.3 加载公共语言运行时 81.4 执行程序集的代码 101.4.1 IL和验证 151.4.2 不安全的代码 161.5 本地代码生成器:NGen.exe 181.6 Framework类库 201

  • k8s master2020-11-02 05:31:27

            K8s master include apiserver, schduler, contorller manger. Apiserver provide interface beween others, it runs both server and client when it talk to Node;  and runs as client when it talk to etcd. So it needs certifacate of both client and serve

  • CCS - Digital Transmission via Carrier Modulation - Probability of Error for QAM in an AWGN Channel2020-09-19 17:00:44

      Probability of Error for QAM in an AWGN Channel           Matlab Coding                   1 % MATLAB script for Illustrative Problem 7.6. 2 echo on 3 SNRindB1=0:2:15; 4 SNRindB2=0:0.1:15; 5 M=16; 6 k=log2(M); 7 for i=1:length(SNRind

  • CCS - Digital Transmission via Carrier Modulation - Carrier-Phase Modulation2020-09-17 23:00:54

      Carrier-Phase Modulation                 We observe that binary phase modulation is identical to binary PAM (binary antipodal signals).   Matlab Coding 1 % MATLAB script 2 echo on 3 T=1; 4 M=8; 5 Es=T/2; 6 fc=6/T; % carr

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