basic test

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+<!DOCTYPE html> <html lang=en><!--
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+ <h2 style="color:var(--primary);border-bottom:1px solid #ddd;padding-bottom:15px;margin-top:0">Deep Residual Learning for Image Recognition</h2>
+ <div style=background:#f9f9f9;padding:15px;border-radius:4px;margin-bottom:20px>
+ <p><strong>Type:</strong> Conference Paper</p>
+ <p><strong>Authors:</strong> He, K., Zhang, X., Ren, S., &amp; Sun, J.</p>
+ <p><strong>Source:</strong> CVPR 2016</p>
+ <p><strong>Year:</strong> 2016</p>
+ <p><strong>Citations:</strong> 150000</p>
+ <p><strong>DOI:</strong> 10.1145/nmacgklsm</p>
+ <p><strong>Language:</strong> English</p>
+ </div>
+ 
+ <h3>Abstract</h3>
+ <p style=text-align:justify;line-height:1.8;font-size:16px>We present a residual learning framework to ease the training of networks that are substantially deeper.</p>
+ <p style=text-align:justify;line-height:1.8;color:#666>
+ (Simulated Full Text Content)<br><br>
+ 1. <strong>Introduction</strong><br>
+ The rapid development of Deep Residual Learning for Image Recognition has revolutionized the field. This paper explores the fundamental principles...<br><br>
+ 2. <strong>Methodology</strong><br>
+ We employed a dataset consisting of over 50,000 samples... The algorithm was optimized using stochastic gradient descent...<br><br>
+ 3. <strong>Results</strong><br>
+ Our experiments demonstrate a significant improvement over baseline models (see Table 1). The 2016 publication date marks a significant milestone in this domain.
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