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<!DOCTYPE HTML>
<!--
Massively by HTML5 UP
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Free for personal and commercial use under the CCA 3.0 license (html5up.net/license)
-->
<html>
<head>
<title>YuanPortfolio</title>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1, user-scalable=no" />
<link rel="stylesheet" href="assets/css/main.css" />
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</head>
<body class="is-preload">
<!-- Wrapper -->
<div id="wrapper" class="fade-in">
<!-- Intro -->
<div id="intro">
<h1>Yuan Ma/马远<br />
Portfolio</h1>
<p>Deep learning researcher, pianist and critical thinker</p>
<a href="#header" class="button icon solid solo fa-arrow-down scrolly">Continue</a>
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<li><a href="generic.html">Music</a></li>
<li><a href="resume/Yuan_CV.pdf">Resume</a></li>
</ul>
<ul class="icons">
<li><a href="https://www.linkedin.com/in/yuan-ma-010975294/" class="icon brands fa-linkedin"><span class="label">Linkedin</span></a></li>
<li><a href="https://www.instagram.com/yuan_eddy_ma/" class="icon brands fa-instagram"><span class="label">Instagram</span></a></li>
<li><a href="https://github.com/EddyPianist" class="icon brands fa-github"><span class="label">GitHub</span></a></li>
</ul>
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<!-- Main -->
<div id="main">
<!-- Featured Post -->
<article class="post featured">
<header class="major">
<span class="date">August 2023</span>
<h2>Detection Transformer<br />
with Multi-Level DETR</h2>
<p>In this project, we implemented DETR with Multi-Level Decoder(MLD-DETR). Multi-scale features
are fused by a customized feature fusion model and we arrange them cascade in the transformer decoder. It solve the inefficiency issue at the decoder and achieves state-of-the-art performance with less computational cost.
</header>
<a class="image main"><img src="images/MLD-DETR.png" alt="" /></a>
<ul class="actions special">
<li><a href="https://github.com/EddyPianist/detr_mul" class="button large">View Project</a></li>
</ul>
<ul class="actions special">
<li><a href="papers/DETR_with_multi_scale_decoder_second_edition (4).pdf" class="button large"> View Paper </a></li>
</ul>
</article>
<!-- Featured Post -->
<article class="post featured">
<header class="major">
<span class="date">August 2024</span>
<h2>Classical Music Classifier</h2>
<p>The Classical Music Classifier is an AI model fine-tuned on the MAESTRO dataset to classify classical music. Built on the CLAP model,
which excels in general audio tasks, it is specifically optimized for classical piano works, addressing the limitations of CLAP in this genre.
</header>
<a class="image main"><img src="images/pic10.png" alt="" /></a>
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<li><a href="https://github.com/EddyPianist/Classical_Music_Classifer" class="button large">View Project</a></li>
</ul>
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<!-- Posts -->
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<article>
<header>
<span class="date">April 24, 2023</span>
<h2>Adverisal Robustness towards Different Vision Transformer</h2>
</header>
<!-- <a href="#" class="image fit"><img src="images/pic02.jpg" alt="" /></a> -->
<p>There exists numerous different structure of vision transformers at present. In this work, we firstly divide them into different categories, then perform adverisal trainings and adverisal attacks to these categories, to testify which kind of structure is more robust</p>
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<li><a href="papers/ECE572 final project.pdf" class="button">Veiw paper</a></li>
</ul>
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<span class="date">Decemeber 8, 2023</span>
<h2>Easynn<br /><br />
</h2>
</header>
<!-- <a href="#" class="image fit"><img src="images/pic03.jpg" alt="" /></a> -->
<p>Easynn is a C++ library provide basic operations for training machine learning models. It contains C++ implement of basic operators like add and multiple, as well as operations like Relu and 3D Convolution. We can train basic machine learning models with Easynn Library</p>
<ul class="actions special">
<li><a href="https://github.com/EddyPianist/Easynn_IIT_ECE_590" class="button">Veiw Project</a></li>
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<span class="date">April 18, 2017</span>
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<span class="date">April 14, 2017</span>
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<span class="date">April 11, 2017</span>
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<span class="date">April 7, 2017</span>
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