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deep link到信息流广告,魔窗sdk的演变

一. deep link 1.1 支持deep link和deferred deep link 早在2015年下半年就开始做deep link的功能,当时的版本已经支持了deep link以及deferred deep link(场景还原)的功能。 所谓deep link,是解决各个App之间信息孤岛的问题,实现App之间能够像网页一样能够自由跳转。 deferred deep link 是指用户打开一个h5页面的时候并没有安装对应的 app,在安装 app 以后可以直接通过 deep link 到 app 对应的内容。 ? 应用宝跳转原理跟 deferred deep link 是一样的,并不会100%的准确匹配,但绝大多数情况是可以成功跳转的。

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趣谈 iOS Universal Link

1、了解 Universal Link 背后的故事 2、学习 Universal Link 功能的使用 3、总结 Universal Link 产品的思考 一、前言 说起 Universal Link( 出现 Universal Link,那么说明 URL Schemes 有什么不足呢?那可以反过来说,Universal Link 有什么优点呢? 最后的总结,我们说了 URL Scheme 和 Universal Link 的来历,从目前来说,Universal Link 并不能完全替换 URL Scheme,但 Universal Link 因为其优点 ,苹果把越来越多的功能权限验证,放到了 Universal Link 配置上,相信未来还会有更多 Universal Link。 in Universal Links - WWDC20 What's New in Universal Links - WWDC19 Extend Your App’s Presence with Deep

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    深度学习(Deep Learning) 学习资料

    Deep Learning 101 http://markus.com/deep-learning-101/? A Tutorial on Deep Learning - G. Slides link:http://pan.baidu.com/share/link? shareid=136269&uk=2267174042 Video link: 1. VIPL下载Link video/mp4 KaiYu_report.mp4 (519.2 MB) 8.

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    Github项目推荐 | 中文整理的强化学习资料(Reinforcement Learning)

    * 但是没有提到下面的UCL,UCB的课,也没有提到上面sutton的书,结合得看或许会更好 * 在线的文档 link 关于强化学习的基础介绍 link 关于深度强化学习的建议 link 代码部分 link Deep Q Learning. link Deep reinforcement learning continued. link Imitation Learning. link Policy Learning link Explorationlink,link Transfer Learning and Multi-Task Learning link Meta-Learning link Deep NN Architectures for RL link Recitation on Monte Carlo Tree Search link VF approximation, MC, TD with VF approximation, Control with VF approximation link Deep Q Learning : Double Q learning

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    Github项目推荐 | 中文整理的强化学习资料(Reinforcement Learning)

    * 但是没有提到下面的UCL,UCB的课,也没有提到上面sutton的书,结合得看或许会更好 * 在线的文档 link 关于强化学习的基础介绍 link 关于深度强化学习的建议 link 代码部分 link Deep Q Learning. link Deep reinforcement learning continued. link Imitation Learning. link Policy Learning link Explorationlink,link Transfer Learning and Multi-Task Learning link Meta-Learning link Deep NN Architectures for RL link Recitation on Monte Carlo Tree Search link VF approximation, MC, TD with VF approximation, Control with VF approximation link Deep Q Learning : Double Q learning

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    Github项目推荐 | 中文整理的强化学习资料(Reinforcement Learning)

    * 但是没有提到下面的UCL,UCB的课,也没有提到上面sutton的书,结合得看或许会更好 * 在线的文档 link 关于强化学习的基础介绍 link 关于深度强化学习的建议 link 代码部分 link Deep Q Learning. link Deep reinforcement learning continued. link Imitation Learning. link Policy Learning link Explorationlink,link Transfer Learning and Multi-Task Learning link Meta-Learning link Deep NN Architectures for RL link Recitation on Monte Carlo Tree Search link VF approximation, MC, TD with VF approximation, Control with VF approximation link Deep Q Learning : Double Q learning

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    POJ 3694 Network(Tarjan求割边+LCA)

    He call such a link a bridge. You are to help the administrator by reporting the number of bridges in the network after each new link dfn[edge[i].v]) { deep[edge[i].v]=deep[now]+1; f[edge[i].v]=now; =fa) low[now]=min(low[now],dfn[edge[i].v]); } } int Solve(int x,int y) { if(deep[x]<deep[y]) swap(x,y); while(deep[x]!

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    【论文推荐】了解《通信强化学习》必看的6篇论文(附打包下载地址)

    They proposed to use deep reinforcement learning to determine the balancing coefficients of selfish and They proposed a distributed multi-agent double deep Q-learning network (DDQN) solution for beamfoming They use policy-based deep deterministic policy gradient (DDPG) derived from Markov decision process A two-hops state-aware routing strategy based on deep reinforcement learning (DRL-THSA) was proposed Novel link state. A setup and update method of link state was proposed.

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    paper:Multi-Level Discovery of Deep Options

    Multi-Level Discovery of Deep Options Abstract Augmenting an agent’s control with useful higher-level We demonstrate that using the discov- ered options to augment the action space of Deep Q-Network agents In prior work, we proposed an algorithm called Deep Discovery of Options (DDO) to discover options to We evaluate DDCO in simulation of a 3-link robot in the vertical plane pushing a block with friction In the 3-link arm simulation, results suggest that DDCO can take 3x fewer demonstrations to achieve the

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    【译】React Native URL 路由库

    react-native-deep-linking 一个 React Native 路由匹配用于处理 deep links 的库 安装 这个包已经发布到 npm 上面去了: npm install react-native-deep-linking 在你的项目中添加 deep link 支持 对于 iOS: 确保你已经在你 app 中的 Info.plist 文件中注册了 URL Schemes 项目 把下面的代码加到你的 AppDelegate.m Button, Linking, StyleSheet, Text, View } from 'react-native'; import DeepLinking from 'react-native-deep-linking center', alignItems: 'center', }, text: { fontSize: 18, margin: 10, }, }); Routes 正常的 deep link URL 的格式如下:<scheme>://<host>/<path-component> 举个栗子:facebook://profile // The following route matches

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    KDD2021 | 最新GNN官方教程

    Learning的教程,顶级学者的在线教学,值得一听~ Graph Representation Learning:Foundations, Methods, Applications and Systems Deep Challenge (OGB-LSC) Heterogeneous Information Network Analysis and Applications Heterogenous Graph Deep Applications and Systems Time and Location Time: Aug 14: 9 am - 12 pm, 1 pm - 4 pm (Singapore Time) Zoom Link : Please use the link on KDD virtual platform Abstract Graphs such as social networks and molecular graphs It has achieved tremendous success in various tasks such as node classification, link prediction, and

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    Android DeepLink介绍与使用

    基本概念 Deep Link,又叫deep linking,中文翻译作深层链接。 简单地从用户体验来讲,Deep Link,就是可以让你在手机的浏览器/Google Search上点击搜索的结果,便能直接跳转到已安装的应用中的某一个页面的技术。 [query]">启动应用程序 示例: 立即打开(直接打开)>> AndroidManifest.xml <! 的区别 Deep Links 是一种允许用户进入应用某个特定Activity的intent filter。 q=[prompt] 淘宝店铺搜索:taobao://http://shopsearch.t 参考链接 Deep Link是什么 深度链接(Deeplink)技术助力APP运营,实现病毒式用户增长 现在很火的

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    Android Handler机制3之SystemClock类

    This clock stops when the system enters deep * sleep (CPU off, display dark, device waiting for since the system was booted, and include deep sleep which occur even when the device is in deep sleep * or your application is not running. return false; } /** * Returns milliseconds since boot, not counting time spent in deep (三)、uptimeMillis()方法 /** * Returns milliseconds since boot, not counting time spent in deep

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    【论文推荐】最新七篇知识图谱相关论文—知识表示学习、增强神经网络、链接预测、关系预测与提取、综述、递归特性生成、深度知识感知网络

    SimplE Embedding for Link Prediction in Knowledge Graphs(基于SimplE Embedding的知识图中链接预测) ---- ---- 作者:Seyed To address the incompleteness of the knowledge graphs, link prediction approaches have been developed Tensor factorization approaches have proven promising for such link prediction problems. knowledge bases are typically incomplete, it is useful to be able to perform knowledge base completion or link DKN is a content-based deep recommendation framework for click-through rate prediction.

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    SmartCon:面向5G和B5G的窄带物联网中基于深度概率学习的智能链接配置

    原文题目:SmartCon: Deep Probabilistic Learning Based Intelligent Link-Configuration in Narrowband-IoT Towards Therefore, in NB-IoT systems, dynamic adaptation of repetitions, MCS, and radio resources, known as auto link-configuration Accordingly, in this paper, we propose SmartCon which is a Generative Adversarial Network (GAN)-based deep learning approach for auto link-configuration during uplink or downlink scheduling, such that the packet

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    学界 | KDD 2018:滴滴提出WDR模型显著提升ETA预测精度

    ,而一段行程对应的路段(以下称为 link)数变动范围很大,因此在实际使用时,舍去了 link 级的特征,取而代之使用整体统计值。 而它的 Deep 分支就是传统的多层感知机结构,有较好的泛化能力。两个分支强强联手,能互取所长。 WD 模型的大体结构如下图所示: ? 将它和 WD 模型组合之后,就得到了 Wide-Deep-Recurrent model (WDR)。结构见下图: ? 注意,这里不再是每个行程一个特征向量,而是行程中每条 link 都有一个特征向量。比如,link 的长度、车道数、功能等级、实时通行速度等。 其中,Wide 和 Deep 模块对行程的整体信息进行建模,而 Recurrent 模块对行程的轨迹进行细致的建模,可以捕捉到每条 link、每个路口的信息。

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    Call For Papers# IJCNN2020 Special Session: Method of DRL to AS

    Recent developments in deep learning have been integrated into conventional RL, known as deep RL, for This special session aims to bring together the recent developments in the theory and application of deep The topics include, but are not limited to: Advances in deep RL theory Applications of deep RL-based To submit your paper to this special session, you will use the IJCNN upload link and choose our SPECIAL Methods and Applications of Deep Reinforcement Learning to Autonomous Systems" in the research topic

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