Skip to content

Commit

Permalink
Added new papers
Browse files Browse the repository at this point in the history
  • Loading branch information
@asteroidhouse
asteroidhouse committed Nov 8, 2024
1 parent 506ed72 commit e5c35d6
Show file tree
Hide file tree
Showing 2 changed files with 243 additions and 0 deletions.
30 changes: 30 additions & 0 deletions index.html
View file
Original file line number Diff line number Diff line change
Expand Up @@ -222,6 +222,36 @@
</div>
</div>

<div class="displaycards touchup-date" id="event-0uwe0z2Hqm">
<div style="width:80%;margin:auto;">
<a class="small-title" href="paper_pages/0uwe0z2Hqm.html">Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs</a>
</div>
<div class="type_display_name_minus_type"></div>
<div class="author-str">Ahmad Naser Eddin &middot; Jacopo Bono &middot; David Oliveira Aparicio &middot; Hugo Ferreira &middot; Pedro Manuel Pinto Ribeiro &middot; Pedro Bizarro</div>
<div class="author-str higher"></div>
<div class="text-muted touchup-date-div" id="touchup-date-event-0uwe0z2Hqm"></div>

<a href="paper_pages/0uwe0z2Hqm.html">
<img src="http://img.youtube.com/vi/PL6HOcHf_w6mrceEUlo0TgW7jioxXV38o6/0.jpg" class="social-img-thumb rounded" alt="thumbnail"/>
</a>

<div class="abstract-section">
<div>
<a id="abstract-link-0uwe0z2Hqm" class="abstract-link" data-toggle="collapse"
href="#collapse-event-abstract-0uwe0z2Hqm" role="button"
aria-expanded="false" aria-controls="collapse-event-abstract-0uwe0z2Hqm">
Abstract <i id="caret-0uwe0z2Hqm" class="fas fa-caret-right"></i>
</a>
</div>
</div>

<div class="collapse" id="collapse-event-abstract-0uwe0z2Hqm">
<div class="abstract-display">
<p>Continuous-time dynamic graphs (CTDGs) are essential for modeling interconnected, evolving systems. Traditional methods for extracting knowledge from these graphs often depend on feature engineering or deep learning. Feature engineering is limited by the manual and time-intensive nature of crafting features, while deep learning approaches suffer from high inference latency, making them impractical for real-time applications. This paper introduces Deep-Graph-Sprints (DGS), a novel deep learning architecture designed for efficient representation learning on CTDGs with low-latency inference requirements. We benchmark DGS against state-of-the-art (SOTA) feature engineering and graph neural network methods using five diverse datasets. The results indicate that DGS achieves competitive performance while inference speed improves between 4x and 12x compared to other deep learning approaches on our benchmark datasets. Our method effectively bridges the gap between deep representation learning and low-latency application requirements for CTDGs.</p>
</div>
</div>
</div>

<div class="displaycards touchup-date" id="event-fJEsas1z8J">
<div style="width:80%;margin:auto;">
<a class="small-title" href="paper_pages/fJEsas1z8J.html">MoCaE: Mixture of Calibrated Experts Significantly Improves Object Detection</a>
Expand Down
213 changes: 213 additions & 0 deletions paper_pages/0uwe0z2Hqm.html
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,213 @@
<!DOCTYPE html>
<html lang="en" style="scroll-padding-top: 70px;">

<head>

<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0, shrink-to-fit=no">
<link rel="stylesheet"
href="https://fonts.googleapis.com/css?family=Open+Sans:300italic,400italic,600italic,700italic,800italic,400,300,600,700,800">
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Lora:400,700,400italic,700italic">
<link href="https://fonts.googleapis.com/css2?family=Exo:wght@400;700&family=Lato:wght@400;700&display=swap" rel="stylesheet">

<link rel="stylesheet" href="/static/expo/fonts/font-awesome.min.css">
<link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.8.1/css/all.css" integrity="sha384-50oBUHEmvpQ+1lW4y57PTFmhCaXp0ML5d60M1M7uH2+nqUivzIebhndOJK28anvf" crossorigin="anonymous">
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap-select.min.css">
<link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.8.1/css/all.css" integrity="sha384-50oBUHEmvpQ+1lW4y57PTFmhCaXp0ML5d60M1M7uH2+nqUivzIebhndOJK28anvf" crossorigin="anonymous">
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css" integrity="sha384-xOolHFLEh07PJGoPkLv1IbcEPTNtaed2xpHsD9ESMhqIYd0nLMwNLD69Npy4HI+N" crossorigin="anonymous">


<script src="https://code.jquery.com/jquery-3.6.1.min.js"
integrity="sha256-o88AwQnZB+VDvE9tvIXrMQaPlFFSUTR+nldQm1LuPXQ=" crossorigin="anonymous"></script>
</script>

<script>
if (typeof jQuery === 'undefined') {
var script = document.createElement('script');
script.type = 'text/javascript';
script.src = "/static/core/js/jquery-3.6.1.min.js";
document.head.appendChild(script);
}
</script>

<script src="https://d3js.org/d3.v5.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/umd/popper.min.js" integrity="sha384-Q6E9RHvbIyZFJoft+2mJbHaEWldlvI9IOYy5n3zV9zzTtmI3UksdQRVvoxMfooAo" crossorigin="anonymous"></script>
<script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bootstrap.bundle.min.js" integrity="sha384-Fy6S3B9q64WdZWQUiU+q4/2Lc9npb8tCaSX9FK7E8HnRr0Jz8D6OP9dO5Vg3Q9ct" crossorigin="anonymous"></script>
<script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bootstrap-select.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/corejs-typeahead/1.3.1/typeahead.bundle.min.js" integrity="sha512-lEb9Vp/rkl9g2E/LdHIMFTqz21+LA79f84gqP75fbimHqVTu6483JG1AwJlWLLQ8ezTehty78fObKupq3HSHPQ==" crossorigin="anonymous"></script>
<script src="https://cdn.jsdelivr.net/npm/[email protected]/min/moment.min.js"
integrity="sha256-4iQZ6BVL4qNKlQ27TExEhBN1HFPvAvAMbFavKKosSWQ="
crossorigin="anonymous"></script>
<script src="https://cdn.jsdelivr.net/npm/js-cookie@2/src/js.cookie.min.js"></script>
<script src="/static/core/js/ajax-csrf-snippet.js" type="text/javascript"></script>
<script src="/static/virtual/js/virtual.js"></script>


<link rel="stylesheet" href="../virtual.css">
<link rel="stylesheet" href="/static/virtual/css/calendar.css">
<link rel="stylesheet" href="/static/virtual/css/calendar-ICML.css">
<link rel="stylesheet" href="/static/virtual/css/calendar-ICML2023.css">
<script src='https://slideslive.com/embed_presentation.js'></script>

</head>

<body>
<!-- NAV -->

<!--<nav class="navbar sticky-top navbar-expand-lg navbar-light bg-light mr-auto" id="main-nav">-->
<nav class="navbar sticky-top navbar-expand-lg mr-auto navbar-light" id="main-nav">
<div class="container-fluid">
<a class="navbar-brand" href="../index.html">
<img src="../tmlr_logo.jpeg" height="40px">
Transactions on Machine Learning Research
</a>
<button class="navbar-toggler" type="button" data-toggle="collapse" data-target="#navbarNav"
aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation">
<span class="navbar-toggler-icon"></span>
</button>
<div class="collapse navbar-collapse text-right flex-grow-1" id="navbarNav">
<ul class="navbar-nav ml-auto">

<!--
<li class="nav-item dropdown">
<a class="nav-link dropdown-toggle" href="#" id="navbarDropdown" role="button"
data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">
Main Conference
</a>
<div class="dropdown-menu" aria-labelledby="navbarDropdown">
<div class="dropdown-divider"></div>
<a class="dropdown-item" href="/virtual/2023/events/oral">Orals</a>
<div class="dropdown-divider"></div>
<a class="dropdown-item" href="/virtual/2023/events/spotlight">Spotlights</a>
<div class="dropdown-divider"></div>
<a class="dropdown-item" href="/virtual/2023/papers.html">Papers</a>
</div>
</li>
-->

<!--
<li class="nav-item">
<a class="nav-link" href="../index_file.html">All Papers</a>
</li>
-->

<!--
<li class="nav-item">
<a class="nav-link" href="../">Papers with Videos</a>
</li>
-->

<!--
<li class="nav-item">
<a class="nav-link" href="../features_papers.html">Featured Papers</a>
</li>
-->

<!--
<li class="nav-item ">
<a class="nav-link" href="/virtual/2023/search"><i class="fas fa-search"></i> &nbsp;</a>
</li>
-->
</ul>
</div>
</div>
</nav>


<div class="container">
<!-- Title -->
<div class="pp-card m-3" style="" id="bookmark-here">
<div class="card-header">
<!-- <h3 class="text-center">Spotlight</h3> -->

<h2 class="card-title main-title text-center" style="">
Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs
</h2>

<h3 class="card-subtitle mb-2 text-muted text-center">
Ahmad Naser Eddin &middot; Jacopo Bono &middot; David Oliveira Aparicio &middot; Hugo Ferreira &middot; Pedro Manuel Pinto Ribeiro &middot; Pedro Bizarro
</h3>

<div class="text-center p-3">

<!--
<a class="card-link" data-toggle="collapse" role="button" href="#details">
Abstract
</a>
-->

<div class="schedule-html-detail"></div>

<div>
<span class="nowrap" style="white-space:nowrap">
<a href="https://openreview.net/forum?id=0uwe0z2Hqm" class="btn btn-default" title="OpenReview">
<img src="../message-logo2.svg" width="30px" alt="Discussion Logo"/> OpenReview
</span>

<span class="nowrap" style="white-space:nowrap">
<a href="https://openreview.net/pdf?id=0uwe0z2Hqm" class="btn btn-default href_PDF" title="Paper PDF">
<img src="../pdf-logo.svg" width="30px" alt="PDF Logo"/> Paper PDF
</a>
</span>


</div>
</div>
<div class=" text-center text-muted text-monospace ">
<div>
</div>
</div>
</div>
</div>


<!-- YouTube Embed -->
<div class="text-center">
<h4 class="text-center">Video</h4>
<iframe width="896" height="504" src="https://www.youtube.com/embed/PL6HOcHf_w6mrceEUlo0TgW7jioxXV38o6" title="Embedded Video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
</div>

<div class="m-3 text-center">
<h4 class="text-center">Paper PDF</h4>
<a href="https://openreview.net/pdf?id=0uwe0z2Hqm"><img src="../paper_thumbnails/0uwe0z2Hqm.pdf.jpg" class="border border-dark rounded" alt="Thumbnail of paper pages" /></a>
</div>

<div id="details" class="pp-card m-3">
<div class="card-body">
<p class="card-text">
<div id="abstractExample">
<h4 class="text-center">Abstract</h4>
<p>
Continuous-time dynamic graphs (CTDGs) are essential for modeling interconnected, evolving systems. Traditional methods for extracting knowledge from these graphs often depend on feature engineering or deep learning. Feature engineering is limited by the manual and time-intensive nature of crafting features, while deep learning approaches suffer from high inference latency, making them impractical for real-time applications. This paper introduces Deep-Graph-Sprints (DGS), a novel deep learning architecture designed for efficient representation learning on CTDGs with low-latency inference requirements. We benchmark DGS against state-of-the-art (SOTA) feature engineering and graph neural network methods using five diverse datasets. The results indicate that DGS achieves competitive performance while inference speed improves between 4x and 12x compared to other deep learning approaches on our benchmark datasets. Our method effectively bridges the gap between deep representation learning and low-latency application requirements for CTDGs.
</p>
</div>
</p>

</div>
</div>
</div>


<script>
var show_abstract = false;
</script>


<script type="text/x-mathjax-config">
MathJax.Hub.Config({
"tex2jax": {
"inlineMath": [["$","$"], ["\(","\)"]],
"displayMath": [["\[","\]"]],
"processEscapes": true
}
}
);
var jq2 = $;
</script>

<script type="text/javascript" async
src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-MML-AM_CHTML">
</script>

</body>
</html>

0 comments on commit e5c35d6

Please sign in to comment.