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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
<html xml:lang="en" xmlns="http://www.w3.org/1999/xhtml" lang="en">
<head>
<title>
Qiyao Wei
</title>
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<body>
<div class="nav">
<div class="on"><a href="index.html">About</a></div>
<div class="off"><a href="publications.html">Publications</a></div>
<!-- <div class="off"> <a href="data/misc/CV.pdf" target="_blank">CV</a></div> -->
<div class="off"><a href="teaching.html">Teaching</a></div>
<div class="off"><a href="personal.html">Personal</a></div>
</div>
<div id="empty"></div>
<div id="contentpane">
<div id="content">
<div id="pagetitle">
<div id="name"> <h1>Qiyao Wei</h1>
<p>Undergraduate Student<br/>
Computer Engineering, University of Toronto<br/>
<!-- q.wei@mail.utoronto.ca<br/> -->
</p>
<p style="text-align:center">
<a href="mailto:q.wei@mail.utoronto.ca">Email</a>  / 
<a href="data/misc/Awesome_CV.pdf">CV</a>  / 
<a href="https://github.com/QiyaoWei">Github</a>  / 
<a href="https://twitter.com/territorial_ape">Twitter</a>
</p></div>
<div id="headerimg"><img src="images/me.jpg" width="150px"></div>
</div>
<div>
<h4>About Me</h4>
<!-- <p>I am a PhD student at University Name where I am advised by <a href="" target="_blank">Advisor Name</a>. Here I'm talking about my research interests and blah blah.</p> -->
<p>A student whose interest spans from the abstract beauty of mathematics to the science and applications of computer technology---I am so easily enthusiastic about ingenuity and imagination. At the same time I wholeheartedly believe that without solid knowledge any idea is only an intangible dream.</p>
<h4>Newcomers: What if I am just getting started?</h4>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;">
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<!-- <heading>Newcomers: What if I am just getting started?</heading> -->
<p>
<i>Relax. It is always scary to step into a new subfield, let alone a new direction of
research.</i>
</p>
<p>
<a href="https://arxiv.org/pdf/1506.00019.pdf">Here</a> is a nice introductory paper.
</p>
<p>
<a href="http://rail.eecs.berkeley.edu/deeprlcourse/">Here</a> is my favorite deep
reinforcement learning course.
</p>
<p>
<a href="https://www.inference.vc/neural-tangent-kernels-some-intuition-for-kernel-gradient-descent/">Here</a>
is a great visualization of NTK, featuring one of my favorites blogs.
Though if you want to fully understand NTK, I personally find <a
href="https://arxiv.org/pdf/1904.11955.pdf">this</a> the most helpful, and you might
want to start from <a href="http://www.cs.toronto.edu/~radford/ftp/thesis.pdf">NNGP</a>
</p>
<p>
<a href="https://www.youtube.com/watch?v=SfSqLn0j10g">Here</a> is a fantastic introduction
on Bandits, and a gentle touch on concentration inequalities.
</p>
<p>
<a href="https://www.cs.ubc.ca/~nickhar/F18-531/NotesHoeffding.pdf">Here</a> is a great
derivation of Hoeffding's bound, making connection with union bounds.
</p>
<p>
<a href="https://ptolemy.berkeley.edu/projects/embedded/eecsx44/lectures/Spring2013/Picard.pdf">Here</a> is a great
derivation of Banach Fixed Point Theorem and Picard-Lindelof Theorem.
</p>
<p>
<a href="https://distill.pub/2019/visual-exploration-gaussian-processes/#MultipleKernels">Here</a> is a great
visualization of Gaussian processes.
</p>
<p>
<a href="https://ruder.io/optimizing-gradient-descent/">Here</a> is a brilliant summary of all
the variants of gradient descent, plus explanations.
</p>
<p>
<a href="https://lup.lub.lu.se/luur/download?func=downloadFile&recordOId=9031089&fileOId=9031091">Here</a>
is a great explanation of the connection between Anderson acceleration and GMRES
</p>
</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
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