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‎2025/03/04/hexo新建操作并上传流程/index.html‎

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<a href="https://hexo.io" target="_blank" rel="nofollow noopener"><span>Hexo</span></a> <i class="iconfont icon-love"></i> <a href="https://github.com/fluid-dev/hexo-theme-fluid" target="_blank" rel="nofollow noopener"><span>Fluid</span></a>
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<a href="https://runsstudio.github.io" target="_blank" rel="nofollow noopener"><span>Run's studio © 2025</span></a> <i class="iconfont icon-love"></i> <a href="https://github.com/fluid-dev/hexo-theme-fluid" target="_blank" rel="nofollow noopener"><span>Fluid</span></a>
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‎2025/03/16/安装和配置Pytorch和cuda/index.html‎

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<a href="https://hexo.io" target="_blank" rel="nofollow noopener"><span>Hexo</span></a> <i class="iconfont icon-love"></i> <a href="https://github.com/fluid-dev/hexo-theme-fluid" target="_blank" rel="nofollow noopener"><span>Fluid</span></a>
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<a href="https://runsstudio.github.io" target="_blank" rel="nofollow noopener"><span>Run's studio © 2025</span></a> <i class="iconfont icon-love"></i> <a href="https://github.com/fluid-dev/hexo-theme-fluid" target="_blank" rel="nofollow noopener"><span>Fluid</span></a>
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‎2025/03/31/hello-world/index.html‎

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<a href="https://hexo.io" target="_blank" rel="nofollow noopener"><span>Hexo</span></a> <i class="iconfont icon-love"></i> <a href="https://github.com/fluid-dev/hexo-theme-fluid" target="_blank" rel="nofollow noopener"><span>Fluid</span></a>
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<a href="https://runsstudio.github.io" target="_blank" rel="nofollow noopener"><span>Run's studio © 2025</span></a> <i class="iconfont icon-love"></i> <a href="https://github.com/fluid-dev/hexo-theme-fluid" target="_blank" rel="nofollow noopener"><span>Fluid</span></a>
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‎2025/04/02/Pytorch快速上手/index.html‎

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‎2025/04/23/表征学习/index.html‎

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<li><strong>空间不变性</strong>：卷积操作具有平移不变性，即无论图像中的物体在图像中的位置如何变化，比如上下左右平移，最终都能被卷积核扫到，能够提取到相应的特征。</li>
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<p>例如 可以到<a target="_blank" rel="noopener" href="https://poloclub.github.io/cnn-explainer/">卷积可视化网站</a>查看卷积操作都对图片做了什么。可以看到，浅层卷积分辨率高，提取点、颜色等基础特征，随着卷积的深入，逐渐提取到线段、边缘、轮廓、角点等特征，进行多层卷积之后的图像具有分辨率低的特点，从而能够和最终分类的抽象特征强相关联。这就是CNN模型起效的原因。<br><img src="/2025/04/23/%E8%A1%A8%E5%BE%81%E5%AD%A6%E4%B9%A0/image1.gif" srcset="/img/loading.gif" lazyload alt="卷积操作可视化"></p>
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<h2 id="2-2-图像表征改进架构———Transformer结构"><a href="#2-2-图像表征改进架构———Transformer结构" class="headerlink" title="2.2 图像表征改进架构———Transformer结构"></a>2.2 图像表征改进架构———Transformer结构</h2><h2 id="2-3-Transformer架构讨论分析"><a href="#2-3-Transformer架构讨论分析" class="headerlink" title="2.3 Transformer架构讨论分析"></a>2.3 Transformer架构讨论分析</h2><h1 id="3-多模态表征与多模态大模型"><a href="#3-多模态表征与多模态大模型" class="headerlink" title="3. 多模态表征与多模态大模型"></a>3. 多模态表征与多模态大模型</h1><h2 id="3-1-多模态表征的定义、动机和核心问题"><a href="#3-1-多模态表征的定义、动机和核心问题" class="headerlink" title="3.1 多模态表征的定义、动机和核心问题"></a>3.1 多模态表征的定义、动机和核心问题</h2><h2 id="3-2-多模态表征的经典结构"><a href="#3-2-多模态表征的经典结构" class="headerlink" title="3.2 多模态表征的经典结构"></a>3.2 多模态表征的经典结构</h2><h3 id="3-2-1-单塔结构（以VILT为例）"><a href="#3-2-1-单塔结构（以VILT为例）" class="headerlink" title="3.2.1 单塔结构（以VILT为例）"></a>3.2.1 单塔结构（以VILT为例）</h3><h3 id="3-2-2-双塔结构（以CLIP为例）"><a href="#3-2-2-双塔结构（以CLIP为例）" class="headerlink" title="3.2.2 双塔结构（以CLIP为例）"></a>3.2.2 双塔结构（以CLIP为例）</h3><h2 id="3-3-多模态大模型"><a href="#3-3-多模态大模型" class="headerlink" title="3.3 多模态大模型"></a>3.3 多模态大模型</h2><h3 id="3-3-1-多模态大模型的通用结构"><a href="#3-3-1-多模态大模型的通用结构" class="headerlink" title="3.3.1 多模态大模型的通用结构"></a>3.3.1 多模态大模型的通用结构</h3><h1 id="4-时空图表征"><a href="#4-时空图表征" class="headerlink" title="4. 时空图表征"></a>4. 时空图表征</h1><h2 id="4-1-交通场景下的表征学习"><a href="#4-1-交通场景下的表征学习" class="headerlink" title="4.1 交通场景下的表征学习"></a>4.1 交通场景下的表征学习</h2><h2 id="4-2-时空图的定义"><a href="#4-2-时空图的定义" class="headerlink" title="4.2 时空图的定义"></a>4.2 时空图的定义</h2><h2 id="4-3-图表征的经典框架（GCN、GAT）"><a href="#4-3-图表征的经典框架（GCN、GAT）" class="headerlink" title="4.3 图表征的经典框架（GCN、GAT）"></a>4.3 图表征的经典框架（GCN、GAT）</h2><h2 id="4-4-时空图表征经典模型（STGCN）"><a href="#4-4-时空图表征经典模型（STGCN）" class="headerlink" title="4.4 时空图表征经典模型（STGCN）"></a>4.4 时空图表征经典模型（STGCN）</h2><h2 id="4-5-时空图表征与信号控制相结合的案例（CoLight、STLight）"><a href="#4-5-时空图表征与信号控制相结合的案例（CoLight、STLight）" class="headerlink" title="4.5 时空图表征与信号控制相结合的案例（CoLight、STLight）"></a>4.5 时空图表征与信号控制相结合的案例（CoLight、STLight）</h2><h1 id="5-参考文献"><a href="#5-参考文献" class="headerlink" title="5. 参考文献"></a>5. 参考文献</h1>
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<h2 id="2-2-图像表征改进架构———Transformer结构"><a href="#2-2-图像表征改进架构———Transformer结构" class="headerlink" title="2.2 图像表征改进架构———Transformer结构"></a>2.2 图像表征改进架构———Transformer结构</h2><h2 id="2-3-Transformer架构讨论分析"><a href="#2-3-Transformer架构讨论分析" class="headerlink" title="2.3 Transformer架构讨论分析"></a>2.3 Transformer架构讨论分析</h2><h1 id="3-多模态表征与多模态大模型"><a href="#3-多模态表征与多模态大模型" class="headerlink" title="3. 多模态表征与多模态大模型"></a>3. 多模态表征与多模态大模型</h1><h2 id="3-1-多模态表征的定义、动机和核心问题"><a href="#3-1-多模态表征的定义、动机和核心问题" class="headerlink" title="3.1 多模态表征的定义、动机和核心问题"></a>3.1 多模态表征的定义、动机和核心问题</h2><h2 id="3-2-多模态表征的经典结构"><a href="#3-2-多模态表征的经典结构" class="headerlink" title="3.2 多模态表征的经典结构"></a>3.2 多模态表征的经典结构</h2><h3 id="3-2-1-单塔结构（以VILT为例）"><a href="#3-2-1-单塔结构（以VILT为例）" class="headerlink" title="3.2.1 单塔结构（以VILT为例）"></a>3.2.1 单塔结构（以VILT为例）</h3><h3 id="3-2-2-双塔结构（以CLIP为例）"><a href="#3-2-2-双塔结构（以CLIP为例）" class="headerlink" title="3.2.2 双塔结构（以CLIP为例）"></a>3.2.2 双塔结构（以CLIP为例）</h3><h2 id="3-3-多模态大模型"><a href="#3-3-多模态大模型" class="headerlink" title="3.3 多模态大模型"></a>3.3 多模态大模型</h2><h3 id="3-3-1-多模态大模型的通用结构"><a href="#3-3-1-多模态大模型的通用结构" class="headerlink" title="3.3.1 多模态大模型的通用结构"></a>3.3.1 多模态大模型的通用结构</h3><h1 id="4-参考文献"><a href="#4-参考文献" class="headerlink" title="4. 参考文献"></a>4. 参考文献</h1>
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<a href="https://hexo.io" target="_blank" rel="nofollow noopener"><span>Hexo</span></a> <i class="iconfont icon-love"></i> <a href="https://github.com/fluid-dev/hexo-theme-fluid" target="_blank" rel="nofollow noopener"><span>Fluid</span></a>
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‎2025/04/28/STIDGCN学习笔记/index.html‎

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‎2025/05/06/Graph WaveNet学习笔记/index.html‎

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<p><img src="/2025/05/06/Graph%20WaveNet%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/image-11.png" srcset="/img/loading.gif" lazyload alt="时序卷积示意图"></p>
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<h2 id="6-门控TCN："><a href="#6-门控TCN：" class="headerlink" title="6. 门控TCN："></a>6. 门控TCN：</h2><p>门控机制在RNN中特别重要，可以有效的控制信息的流动（在层和层之间流动），在TCN中也是这样。该文章采用的门控TCN采用如下方式：</p>
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<p>g()是激活函数，σ（·）是sigmoid函数，决定了信息传递到下一层的比例</p>
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<h2 id="7-参考文献："><a href="#7-参考文献：" class="headerlink" title="7. 参考文献："></a>7. 参考文献：</h2><p><a target="_blank" rel="noopener" href="http://zhuanlan.zhihu.com/p/594429261">http://zhuanlan.zhihu.com/p/594429261</a></p>
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