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<p class="header-subtitle">人生不如意之事十有八九。常想一二，不思八九，事事如意。</p>
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<p class="header-subtitle"><i class="icon icon-quo-left"></i>人生不如意之事十有八九。常想一二，不思八九，事事如意。<i class="icon icon-quo-right"></i></p>
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<article id="post-GCN原理简略思路" class="article article-type-post article-index" itemscope="" itemprop="blogPost">
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<a class="article-title" href="/afcheng.github.io/2019/10/30/GCN原理简略思路/">GCN原理简略思路</a>
</h1>
<a href="/afcheng.github.io/2019/10/30/GCN原理简略思路/" class="archive-article-date">
<time datetime="2019-10-30T07:24:01.000Z" itemprop="datePublished"><i class="icon-calendar icon"></i>2019-10-30</time>
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<p>写在前面，GCN提出目的、输入输出、方法分类。</p>
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<ol>
<li>GCN提出目的：在Graph这一广泛使用的数据结构上做深度学习。传统CNN无法直接迁移到Graph上，因此提出GCN这一研究体系/方法框架。</li>
<li>GCN方法分类：时域、频域（有理论保证是目前的主流方法，这里的总结）。</li>
<li>输入：Graph的邻接矩阵 <script type="math/tex">A\in R^{N\times N}</script> ,节点的特征矩阵 <script type="math/tex">X\in R^{N\times F}</script> 。</li>
<li>输出：节点的低维向量表示 <script type="math/tex">F_{output}\in R^{N\times d}</script>，输出 <script type="math/tex">F_{output}</script> 是融合了结构信息的向量表示。在 <script type="math/tex">F_{output}</script> 基础上可以再接对应task的损失函数。</li>
<li>GCN最终干了个啥，就是个根据网络全局节点信息的一个节点表示过程。</li>
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<a class="article-more-a" href="/afcheng.github.io/2019/10/30/GCN原理简略思路/">展开全文 >></a>
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<article id="post-LeetCode-997-Find-the-Town-Judge" class="article article-type-post article-index" itemscope="" itemprop="blogPost">
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<h1 itemprop="name">
<a class="article-title" href="/afcheng.github.io/2019/10/16/LeetCode-997-Find-the-Town-Judge/">LeetCode 997. Find the Town Judge</a>
</h1>
<a href="/afcheng.github.io/2019/10/16/LeetCode-997-Find-the-Town-Judge/" class="archive-article-date">
<time datetime="2019-10-16T06:48:33.000Z" itemprop="datePublished"><i class="icon-calendar icon"></i>2019-10-16</time>
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<h4 id="题目描述"><a href="#题目描述" class="headerlink" title="题目描述"></a>题目描述</h4><p>找到小镇法官</p>
<p>在一个小镇里，按从 1 到 N 标记了 N 个人。传言称，这些人中有一个是小镇上的秘密法官。</p>
<p>如果小镇的法官真的存在，那么：</p>
<p>小镇的法官不相信任何人。<br>每个人（除了小镇法官外）都信任小镇的法官。<br>只有一个人同时满足属性 1 和属性 2 。<br>给定数组 trust，该数组由信任对 trust[i] = [a, b] 组成，表示标记为 a 的人信任标记为 b 的人。</p>
<p>如果小镇存在秘密法官并且可以确定他的身份，请返回该法官的标记。否则，返回 -1。</p>
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<a class="article-more-a" href="/afcheng.github.io/2019/10/16/LeetCode-997-Find-the-Town-Judge/">展开全文 >></a>
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<article id="post-2019-ACL-Improving-Question-Answering-over-Incomplete-KBs-with-Knowledge-Aware-Reader" class="article article-type-post article-index" itemscope="" itemprop="blogPost">
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<a class="article-title" href="/afcheng.github.io/2019/10/16/2019-ACL-Improving-Question-Answering-over-Incomplete-KBs-with-Knowledge-Aware-Reader/">2019 ACL Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader</a>
</h1>
<a href="/afcheng.github.io/2019/10/16/2019-ACL-Improving-Question-Answering-over-Incomplete-KBs-with-Knowledge-Aware-Reader/" class="archive-article-date">
<time datetime="2019-10-16T05:57:06.000Z" itemprop="datePublished"><i class="icon-calendar icon"></i>2019-10-16</time>
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<h4 id="1-论文信息"><a href="#1-论文信息" class="headerlink" title="1. 论文信息"></a>1. 论文信息</h4><p>Title: Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader</p>
<p>Wenhan Xiong † , Mo Yu ∗ , Shiyu Chang ∗ , Xiaoxiao Guo ∗ , William Yang Wang †<br>†University of California, Santa Barbara ∗IBM Research</p>
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<article id="post-Translation-With-a-Sequence-to-Sequence-Network-and-Attention" class="article article-type-post article-index" itemscope="" itemprop="blogPost">
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<a class="article-title" href="/afcheng.github.io/2019/10/10/Translation-With-a-Sequence-to-Sequence-Network-and-Attention/">Translation With a Sequence to Sequence Network and Attention</a>
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<p>Pytorch Tutorial: NLP FROM SCRATCH: TRANSLATION WITH A SEQUENCE TO SEQUENCE NETWORK AND ATTENTION<br>Tutorial 地址: <a href="https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html" target="_blank" rel="noopener">https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html</a><br>任务：Translate French to English SeqtoSeq + Attention<br>  类似name分类每个字符用一个one-hot表示，这里每个单词用一个one-hot。但是因为语料中单词数量很大，因此 only use a few thousand words per language。</p>
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<a class="article-title" href="/afcheng.github.io/2019/10/09/实体消歧/">实体消歧</a>
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<time datetime="2019-10-09T06:05:47.000Z" itemprop="datePublished"><i class="icon-calendar icon"></i>2019-10-09</time>
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<p>  Name Ambiguation   实体识别的结果很难直接存放到知识图谱中，因为实体具有歧义性：同一实体在文本中有不同的指称；相同的实体指称在不同的上下文中可以指不同的实体。<br>  任务定义、任务分类</p>
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<time datetime="2019-10-09T05:44:42.000Z" itemprop="datePublished"><i class="icon-calendar icon"></i>2019-10-09</time>
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<p>实体识别与实体扩展的任务概述、方法以及评价指标。</p>
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<p>知识图谱概述-基础概念</p>
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<ol>
<li>概念</li>
<li>基本单元</li>
<li>图数据结构</li>
<li>知识图谱符号化的表示</li>
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<a class="article-title" href="/afcheng.github.io/2019/10/09/优化器/">优化器</a>
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<p>内容提要</p>
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<li>Batch Gradient Descent (BGD)批梯度下降</li>
<li>Stochastic Gradient Descent (SGD)</li>
<li>Mini-Batch Gradient Descent(MBGD)</li>
<li>改进措施: <ol>
<li>动量Momentum的SGD; </li>
<li>牛顿加速梯度 Nesterov Accelerated Gradient; </li>
<li>AdaDelta算法; </li>
<li>Adam算法 Adaptive Moment Estimate</li>
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<p> 梯度消失（vanishing gradient）与梯度爆炸（exploding gradient）。根源是深度神经网络和反向传播。</p>
<blockquote>
<p>为什么使用梯度更新规则<br>梯度消失和爆炸的原因<br>梯度消失和爆炸的解决方案<br>
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<a class="article-title" href="/afcheng.github.io/2019/10/09/Semi-Supervised-Classfication-with-Graph-Convolutional-Networks/">Semi-Supervised Classfication with Graph Convolutional Networks</a>
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<p>关于论文 Kipf,Thomas N,and M.Welling. “Semi-Supervised Classification with Graph Convolutional Networks.” in ICLR(2017) 的笔记。</p>
<p>第三代GCN，基于Chebyshev多项式，使用最多的GCN。</p>
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