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index.xml
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<rss xmlns:atom="http://www.w3.org/2005/Atom" version="2.0">
<channel>
<title>Hzb's Study Blog</title>
<link>http://example.org/</link>
<description>Hzb's Blog</description>
<generator>Hugo -- gohugo.io</generator><language>zh-CN</language><lastBuildDate>Tue, 03 Oct 2023 10:51:29 +0800</lastBuildDate>
<atom:link href="http://example.org/index.xml" rel="self" type="application/rss+xml" />
<item>
<title>【论文阅读】ReAct</title>
<link>http://example.org/posts/react/</link>
<pubDate>Tue, 03 Oct 2023 10:51:29 +0800</pubDate>
<author>huzhanbin</author>
<guid>http://example.org/posts/react/</guid>
<description><![CDATA[将推理（Reson）和行动（Acting）相结合，使得语言模型能够处理多种语言推理和决策任务。]]></description>
</item>
<item>
<title>【论文阅读】Emergent Abilities of Large Language Models</title>
<link>http://example.org/posts/emergent_abilities_of_llm/</link>
<pubDate>Thu, 14 Sep 2023 10:19:10 +0800</pubDate>
<author>huzhanbin</author>
<guid>http://example.org/posts/emergent_abilities_of_llm/</guid>
<description><![CDATA[量变引起质变：涌现的能力指在小模型时并不具有，表现接近随机，而在模型规模越过一个阈值时，表现突然提升]]></description>
</item>
<item>
<title>【论文阅读】LLM+P</title>
<link>http://example.org/posts/llm+p/</link>
<pubDate>Tue, 05 Sep 2023 10:51:29 +0800</pubDate>
<author>huzhanbin</author>
<guid>http://example.org/posts/llm+p/</guid>
<description><![CDATA[LLM+P将传统规划方法的优点整合到LLMs的框架中]]></description>
</item>
<item>
<title>搭建ChatGPT微信对话机器人教程</title>
<link>http://example.org/posts/chatgpt/</link>
<pubDate>Mon, 12 Dec 2022 10:52:12 +0800</pubDate>
<author>huzhanbin</author>
<guid>http://example.org/posts/chatgpt/</guid>
<description><![CDATA[0行代码、1.5元成本，基于全世界最先进的对话ai模型ChatGPT搭建属于你自己的微信对话机器人！]]></description>
</item>
<item>
<title>3D Human Pose Estimation</title>
<link>http://example.org/posts/hpe/</link>
<pubDate>Wed, 07 Dec 2022 10:52:12 +0800</pubDate>
<author>huzhanbin</author>
<guid>http://example.org/posts/hpe/</guid>
<description><![CDATA[description]]></description>
</item>
<item>
<title>Self-Supervised PreTrain</title>
<link>http://example.org/posts/self-supervised+pretrain/</link>
<pubDate>Sun, 20 Nov 2022 11:23:46 +0800</pubDate>
<author>huzhanbin</author>
<guid>http://example.org/posts/self-supervised+pretrain/</guid>
<description><![CDATA[Self-Supervised PreTrain Pretext task Pretext task也叫surrogate task，也称作代理任务。 Pretext可以理解为是一种为达到特定训练任务而设计的间接任务。比如，我]]></description>
</item>
<item>
<title>FCN & U-Net</title>
<link>http://example.org/posts/fcn+ampamp+u-net/</link>
<pubDate>Thu, 27 Oct 2022 19:16:59 +0800</pubDate>
<author>作者</author>
<guid>http://example.org/posts/fcn+ampamp+u-net/</guid>
<description><![CDATA[FCN 简介 FCN（Fully Convolutional Networks，全卷积网络）是Jonathan Long等人于2015年在Fully Convolutional Networks for Semantic Segmentatio]]></description>
</item>
<item>
<title>FPN & SSD & YOLO</title>
<link>http://example.org/posts/fpn+ampamp+ssd+ampamp+yolo/</link>
<pubDate>Wed, 26 Oct 2022 15:42:33 +0800</pubDate>
<author>作者</author>
<guid>http://example.org/posts/fpn+ampamp+ssd+ampamp+yolo/</guid>
<description><![CDATA[FPN 提出原因 卷积网络中，深层网络容易响应语义特征，浅层网络容易响应图像特征。然而，在目标检测中往往因为卷积网络的这个特征带来了不少麻烦：高层网]]></description>
</item>
<item>
<title>ShuffleNet & EfficientNet</title>
<link>http://example.org/posts/shufflenet+ampamp+efficientnet/</link>
<pubDate>Mon, 12 Sep 2022 15:05:52 +0800</pubDate>
<author>作者</author>
<guid>http://example.org/posts/shufflenet+ampamp+efficientnet/</guid>
<description><![CDATA[ShuffleNet 简介 ShuffleNet是旷视科技提出的一种计算高效的CNN模型，其和MobileNet和SqueezeNet等一样主要是想应用在移动端。]]></description>
</item>
<item>
<title>SENet & MobileNet</title>
<link>http://example.org/posts/senet+ampamp+mobilenet/</link>
<pubDate>Fri, 02 Sep 2022 22:27:28 +0800</pubDate>
<author>作者</author>
<guid>http://example.org/posts/senet+ampamp+mobilenet/</guid>
<description><![CDATA[SENet 简介 SENet是2017ILSVRC2017（ImageNet Large Scale Visual Recognition Challenge）竞赛的冠军网络，在CVPR2018引用量第一。在]]></description>
</item>
</channel>
</rss>