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<!DOCTYPE html>
<html lang="en">
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<!-- <meta http-equiv="refresh" content="5"> -->
<title>11-785 Deep Learning</title>
<link href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.5/css/bootstrap.min.css" rel="stylesheet" integrity="sha256-MfvZlkHCEqatNoGiOXveE8FIwMzZg4W85qfrfIFBfYc= sha512-dTfge/zgoMYpP7QbHy4gWMEGsbsdZeCXz7irItjcC3sPUFtf0kuFbDz/ixG7ArTxmDjLXDmezHubeNikyKGVyQ==" crossorigin="anonymous">
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<div class="container-titlebar">
<div class="row">
<div class="title-col vcenter" style="color:#A80000;font-weight:bold;text-align:center;width:100%">
<div class="title"><b>11-785</b> Introduction to Deep Learning</div>
<div class="subtitle"><i>Fall 2019</i></div>
</div>
</div>
</div>
</head>
<body>
<div class="container">
<br> <h1 align="center">Poster Guidelines </h1>
<h2>What Dimension?</h2>
<p> The size of the poster should be <strong>30x40in or 40x30in</strong> as in <a href="https://www.posterpresentations.com/free-poster-templates.html#postertemplate-40x30">template</a>.</p>
<h2>Where to print?</h2>
<ul class="ul">
<li>If you have an SCS account, you may use SCS poster printing. However, please be sure to read through their guidelines to avoid delays. <br> This service is only available to students with an SCS account. <a href="http://www.cs.cmu.edu/~help/printing/poster_printing.html">http://www.cs.cmu.edu/~help/printing/poster_printing.html</a> </li>
<li>If you do not have a SCS account, please upload your poster to Canvas following the instructions that appear there.</li>
</ul>
<h2>Where to submit?</h2>
<p> One pdf poster document per team should be submitted on Canvas on December 3rd by no later than 11:59pm.</p>
<h2>Presentation Date and Location</h2>
<p> Poster Presentatons will be held on Thursday December 5th in GHC4300 Commons and Monday December 9th in GHC7107 Atrium from 2:00pm to 5:00pm</p>
<h2>What to Include in Your Poster? </h2>
<p>Be creative, after all it is a presentation! You should be able to illustrate what your project can do. <br> Overall, your poster should be aligned with these recommendations: </p> <br>
<table class="table table-bordered">
<thead>
<tr>
<th>key point</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>Overall Clarity of poster</td> <!--Assignment-->
<td> Define clear sections in your poster.<br>
It should present the key points of your work, up to where you got with it. <br>
Summarize ideas and consider using figures and tables to make it more illustrative as that will give a better sense of work done.
</td>
</tr>
<tr>
<td>Introduction</td>
<td>What are you trying to solve? Why is it important in the field?</td>
</tr>
<tr>
<td>Related work</td>
<td>Previous work related to your topic that served as a baseline or motivation. <br> This would serve as your backup support in what you have developed.</td>
</tr>
<tr>
<td>Methods</td>
<td>Which techniques did you apply? <br> You are strongly encouraged to add an illustration of your Machine Learning pipeline. <br>(suggested: figures)</td>
</tr>
<tr>
<td>Dataset</td>
<td>Description of the dataset used, number of files, train and test partitions, labels, etc. <br> (suggested: tables)</td>
</tr>
<tr>
<td>Results</td>
<td>Your experimental results.<br> What metrics did you use for evaluation? <br> How do your results compare to prior work or your baseline? </td>
</tr>
<tr>
<td>Conclusions</td>
<td>summary analysis of your model and results.<br> Describe current results in each of the experiments aligned with your expectations. <br> Highlight a few limitations of your approach <br>(e.g., strong assumptions you had to make, constraints, when your method didn’t work in practice, etc.). </td>
</tr>
<tr>
<td>References</td>
<td>Add the most relevant citations only</td>
</tr>
</tbody>
</table>
<h2>Presentation Grading Rubric</h2>
<p>Your project will be scored according to the following rubric: </p>
<table class="table table-bordered">
<thead>
<tr>
<th>Problem</th>
<th>Proposed Methods</th>
<th>Completeness</th>
<th>Results</th>
<th>Publishability</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left">Whether the problem description is clear? <br> Is the motivation strong? <br>Is the problem nontrivial?</td> <!--Assignment-->
<td align="left">Clear enough? Technically sound? Originality?</td>
<td align="left">Is your work complete? <br> Have you obtained results consistent with successful completion of the work? </td>
<td align="left">Are the results reasonable? <br> how do you set up their experiments? <br> Comparisons with others? Can you explain the results? <br> Have you looked into the results and gained some insights on DL models/algorithms?</td>
<td align="left">Is your work potentially publishable?</td>
</tr>
<tr>
<td align="center">10%</td>
<td align="center">10%</td>
<td align="center">10%</td>
<td align="center">10%</td>
<td align="center">10%</td>
</tr>
</tbody>
</table>
</div> <!-- container div -->
</body>
</html>