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<!DOCTYPE html>
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<h1 class="nav-title">Triloon</h1>
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<div class="catalogue">
<a href="https://triloon.space/posts/triplet-loss-0/" class="catalogue-item">
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<time datetime="2022-01-18 19:07:51 +0800 CST" class="catalogue-time">January 18, 2022</time>
<h2 class="catalogue-title">Triplet Loss 与在线难例挖掘（译）</h2>
<div class="catalogue-line"></div>
<p>
<p>虽然 triplet loss 实现非常简单，但是简单的 loss 要想用好也是需要更细致的分析与调试。</p>
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<a href="https://triloon.space/posts/torch-allgather/" class="catalogue-item">
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<time datetime="2022-01-16 18:43:13 +0800 CST" class="catalogue-time">January 16, 2022</time>
<h2 class="catalogue-title">Torch all_gather 的梯度问题</h2>
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<p>pytorch all_gather 计算结果是叶子节点，也就是不会继续向后传递梯度了。</p>
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<a href="https://triloon.space/posts/deepspeed-zero/" class="catalogue-item">
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<time datetime="2021-11-25 10:41:35 +0800 CST" class="catalogue-time">November 25, 2021</time>
<h2 class="catalogue-title">Deepspeed Zero论文</h2>
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<p>DeepSpeed的开山之作 - ZeRO: Memory Optimizations Toward Training Trillion Parameter Models.</p>
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<a href="https://triloon.space/posts/swin-transformer-v2/" class="catalogue-item">
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<time datetime="2021-11-23 12:07:18 +0800 CST" class="catalogue-time">November 23, 2021</time>
<h2 class="catalogue-title">Swin Transformer V2</h2>
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<p>本文围绕如何有效的增加模型参数量、弥补不同任务输入图像尺寸不同时的Windows大小不同导致的相对位置编码变化问题这两个任务提出了解决方案，总的来说，方案简单有效，值得学习。</p>
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<a href="https://triloon.space/posts/dali-intro/" class="catalogue-item">
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<time datetime="2021-11-22 17:20:46 +0800 CST" class="catalogue-time">November 22, 2021</time>
<h2 class="catalogue-title">DALI简单实用案例</h2>
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<p>DALI(NVIDIA Data Loading Library)库是NVIDIA提供的用于加速数据加载过程的代码库，支持在GPU上完成一些数据处理，从而提高加载速度；另一方面是方便多种源数据文件格式的加载，包括MXNet RecordIO / TFRrecord / LMDB 或者 文件目录等形式的数据集加载；第三就是支持多种数据格式，包括图片、视频、音频等。</p>
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<a href="https://triloon.space/posts/mlm-related-2/" class="catalogue-item">
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<time datetime="2021-10-27 22:08:15 +0800 CST" class="catalogue-time">October 27, 2021</time>
<h2 class="catalogue-title">常见掩码生成方式 2</h2>
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<p>这是接着上一篇掩码生成方式写的，主要仅包含SpanBERT & MacBERT的原理与实现。</p>
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<a href="https://triloon.space/posts/moe-original-paper/" class="catalogue-item">
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<time datetime="2021-10-27 11:15:57 +0800 CST" class="catalogue-time">October 27, 2021</time>
<h2 class="catalogue-title">MoE 论文阅读：Sparsely-Gated MoE</h2>
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<p>本文主要是一点 Mixture of Experts (MoE) 网络结构的介绍。</p>
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<a href="https://triloon.space/posts/tokenizers/" class="catalogue-item">
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<time datetime="2021-10-22 19:51:11 +0800 CST" class="catalogue-time">October 22, 2021</time>
<h2 class="catalogue-title">分词算法基础</h2>
<div class="catalogue-line"></div>
<p>
<p>常见的几种分词算法小结，包括BERT用到WordPiece以及Albert用到的Byte-Pair-Encoding。</p>
</p>
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<a href="https://triloon.space/posts/mlm-related/" class="catalogue-item">
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<time datetime="2021-10-19 19:51:11 +0800 CST" class="catalogue-time">October 19, 2021</time>
<h2 class="catalogue-title">常见掩码生成方式</h2>
<div class="catalogue-line"></div>
<p>
<p>主要是几种常见的MLM的改进以及对应的代码实现，包括WWM, SpanBERT, ERNIE这三种。</p>
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<a href="https://triloon.space/posts/swin-transformer/" class="catalogue-item">
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<time datetime="2021-10-15 22:07:45 +0800 CST" class="catalogue-time">October 15, 2021</time>
<h2 class="catalogue-title">ICCV2021 Best Paper - Swin Transformer</h2>
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<p>
<p>重读论文之后，发现还真是一个非常精巧的模型。</p>
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