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【前端】vue Failed to resolve component If this is a native custom element, make sure to exclude it from

一、报错      runtime-dom.esm-bundler-daf7327a.js:1555 [Vue warn]: Failed to resolve component: myBtn If this is a native custom element, make sure to exclude it from component resolution via compilerOptions.isCustomElement. at <App>   二、原因 核心原因

springboot配置类@ConfigurationProperties报错Not registered via @EnableConfigurationProperties or marked a

添加一个@Component可以解决此问题,只有这个组件是容器中的组件,才能使用容器提供的@ConfigurationProperties功能。

判断是否声明了某个特性

提出问题 我想判断某个类或者属性是否声明了某特性,该怎么办? 解决问题 使用IsDefined,他比GetCustomAttributes效率更高 xxx.GetType().IsDefined(typeof(XXXAttribute),false) 参考 CLR via C# 379

C语言中的SDL库有啥用?

 Simple DirectMedia Layer (SDL) is a cross-platform development library designed to provide low level access to audio(声音), keyboard(键盘), mouse(鼠标), joystick(操纵杆), and graphics(图形) hardware via OpenGL and Direct3D. It is used by video playback software, em

Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networ

动机 本文是2017年IJCAI上的一篇论文。FM方法通过结合二阶特征交互来增强线性回归模型,它将这些特征交互一视同仁,给予它们一个相同的权重,但是并不是所有特征的交互都是有意义的,更具体的,不同的特征交互之间有不同的重要性。而FM模型忽略了这一点,这可能会带来一些噪声,本文作者提出了A

(开集检测系列)OPEN-VOCABULARY OBJECT DETECTION VIA VISION AND LANGUAGE KNOWLEDGE DISTILLATION

不引入caption数据,使用coco数据集,使用CLIP 作为teacher模型蒸馏出Mask RCNN模型的检测能力(主要是训练出Mask RCNN能提取出类无关的box和该box的特征能和CLIP text embedding能很好的match),novel类检测能力通过伪novel类的框+推理时CLIP text embedding的进行分类 引入 1、动机 1、

FFmpeg介绍与编译

目录FFmpegFFmpeg核心模块FFmpeg编译 FFmpeg FFmpeg是一套可以用来记录、转换数字音频、视频,并能将其转化为流的开源计算机程序。采用LGPL或GPL许可证。它提供了录制、转换以及流化音视频的完整解决方案。它包含了非常先进的音频/视频编解码库libavcodec,为了保证高可移植性和编解

【python】遇到的问题

You should consider upgrading via the 'python -m pip install --upgrade pip' command 报错信息: “You are using pip version 10.0.1, however version 20.0.2 is available. You should consider upgrading via the ‘python -m pip install --upgrade pip’ command

C++ query data from mysql and limit page via key word 'limit startIndex,interval'

//Util.h #ifndef Util_H #define Util_H #include <chrono> #include <ctime> #include <fstream> #include <functional> #include <future> #include <iostream> #include <random> #include <sstream> #include <thre

论文解读(AGC)《Attributed Graph Clustering via Adaptive Graph Convolution》

论文信息 论文标题:Attributed Graph Clustering via Adaptive Graph Convolution论文作者:Xiaotong Zhang, Han Liu, Qimai Li, Xiao-Ming Wu论文来源:2019, IJCAI论文地址:download 论文代码:download  1 Introduction   关于GNN 是低通滤波器的好文。 2 Method 2.1 Graph Co

论文解读(DMVCJ)《Deep Embedded Multi-View Clustering via Jointly Learning Latent Representations and Grap

论文信息 论文标题:Deep Embedded Multi-View Clustering via Jointly Learning Latent Representations and Graphs论文作者:Zongmo Huang、Yazhou Ren、Xiaorong Pu、Lifang He论文来源:2022, ArXiv论文地址:download 论文代码:download 1 Introduction   隶属于多视图聚类(MVC)算

Deep Exploration via Bootstrapped DQN

发表时间:2016(NIPS 2016) 文章要点:这篇文章提出了Bootstrapped DQN算法来做深度探索。作者认为,当前的探索策略比如ϵ-greedy,并没有进行深度探索(temporally-extended (or deep) exploration)。Deep exploration指的是一个探索策略进行多步的探索,而不是像ϵ-greedy那种每步都是一个随

Reclaim space after drop database or tables via deleting the generated binlog files

1. sudo -i; 2. cd /var/lib/mysql/;ls -lct;     3.Delete binlog files rm -rf binlog.*   4. ls -lct;     5.Then check the space via df -h;     As the above snapshot illustrates that /dev/sda5 has emptied more and spare more space.  

Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networ

目录概主要内容Attention network细节代码 Xiao J., Ye H., He X., Zhang H., Wu F. and Chua T. Attentional factorization machines: learning the weight of feature interactions via attention networks. In International Joint Conference on Artificial Intelligence (I

Unsupervised Embedding Learning via Invariant and Spreading Instance Feature

             

Planning to Explore via Self-Supervised World Models

发表时间:2020(ICML 2020) 文章要点:这篇文章提出了一个Plan2Explore的model based方法,通过self-supervised方法来做Task-agnostic的探索,在这个过程中有效学习了world model,然后可以迁移到下游的具体任务上,实现zero or few-shot RL。具体的,world model包含encoder,dynamics,reward,decod

C++ pass function and arguments as parameter via function address * symbol

#include <iostream> #include <functional> using namespace std; int sum41(int x,int y); int multiply42(int x,int y); int passFuncAddress43(int x,int y,int(*func)(int ,int)); void callFuncAddress44(int x,int y); int main(int args, char **argv

论文阅读 Dynamic Graph Representation Learning Via Self-Attention Networks

4 Dynamic Graph Representation Learning Via Self-Attention Networks link:https://arxiv.org/abs/1812.09430 Abstract 提出了在动态图上使用自注意力 Conclusion 本文提出了使用自注意力的网络结构用于在动态图学习节点表示。具体地说,DySAT使用(1)结构邻居和(2)历史节点表示上的自

Discovering and Achieving Goals via World Models

发表时间:2021(NeurIPS 2021) 文章要点:这篇文章提出Latent Explorer Achiever (LEXA)算法,通过学习world model的imagined rollouts来训练一个explorer策略和一个achiever策略,通过unsupervised learning学习策略,最后可以zero-shot迁移到其他任务。这个方式的好处在于之前的探索方法只

论文阅读-Distantly Supervised Named Entity Recognition via Confidence-Based Multi-Class Positive and Unl

题目:基于置信度的多类正无标记学习的远程监督命名实体识别 论文地址:https://openreview.net/pdf?id=0gYkM3fk9Bb 源码地址:https://github.com/kangISU/Conf-MPU-DS-NER 摘要:    本文研究了远程监控下的命名实体识别问题。由于外部词典和/或知识库的不完整性,这种远距离注释的训

论文笔记-语义排序-Fast Semantic Matching via Flexible Contextualized Interaction(WWW2022-yewenwen)

Fast Semantic Matching via Flexible Contextualized Interaction 地址: Fast Semantic Matching via Flexible Contextualized Interaction 代码: 概述:

Ultra-high Temporal Resolution Visual Reconstruction from a Fovea-like Spike Camera via Spiking Neur

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! IEEE transactions on pattern analysis and machine intelligence, (2022) 同组工作   Abstract   神经形态视觉传感器是近年来出现的一种新的仿生成像范式。它使用异步脉冲信号代替传统的基于帧的方式来实现超高速采样

What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation

目录概主要内容一种简便的估计方法被记忆的样本所产生的边际效用不同网络结构下的实验最后一次是否足够用于记忆一些示例 Feldman V. and Zhang C. What neural networks memorize and why: discovering the long tail via influence estimation. In Advances in Neural Informat

论文解读(GRCCA)《 Graph Representation Learning via Contrasting Cluster Assignments》

论文信息 论文标题:Graph Representation Learning via Contrasting Cluster Assignments论文作者:Chun-Yang Zhang, Hong-Yu Yao, C. L. Philip Chen, Fellow, IEEE and Yue-Na Lin论文来源:2022, TKDE论文地址:download 论文代码:download 1 介绍    我们提出了一种新的无监督图

C++ thread pass multiple functions and arguments via lambda expression

#include "Model/Util.h" char *Util::uuidValue = (char *)malloc(40); void Util::threadLambda6(int xx,int yy,string sstr) { thread t1([](int x,int y,string str) { cout<<endl; printNumUuid2(x); cout<<en