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call.htm
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Workshop on Exploiting Learning Dynamics in Neurocognitive AI</title>
<link rel="stylesheet"
href="https://cdn.jsdelivr.net/npm/[email protected]/css/bulma.min.css">
<link rel="stylesheet" href="css/style.css">
</head>
<body>
<nav class="navbar has-shadow sticky" role="navigation"
aria-label="main navigation">
<div class="mx-auto" style="width: 400px;">
<a class="navbar-brand navbar-light bg-light" href="https://aicip.github.io/neurocognitiveAI23.htm">Workshop on Exploiting Learning Dynamics in Neurocognitive AI</a>
</div>
<div class="navbar-brand">
<a class="navbar-item" href="call.htm">
Call for Papers
</a>
<a class="navbar-item" href="program.htm">
Program
</a>
<a class="navbar-item" href="organizer.htm">
Organizers
</a>
<a class="navbar-item" href="committee.htm">
Program Committee
</a>
<a class="navbar-item" href="speaker.htm">
Invited Speakers
</a>
</div>
</nav>
<section class="section">
<div class="columns">
<aside class="menu is-4-tablet is-3-desktop is-2-widescreen">
<!-- side bar -->
<div class="box is-size-9">
<h1>Unite neural & cognitive scientists with AI researchers. Explore learning dynamics at the forefront of intelligence. </h1>
</div>
<div class="box is-size-8">
<h1 class="has-text-weight-bold">Important Dates: </h1>
<li>09/29/23: Submission Due
<li>09/29/23: Travel Funds Due
<li>10/27/23: Author Notification
<li>10/30/23: Travel Funds Notification
<li>11/30/23: Camera-ready Due
</div>
</aside>
<!-- ### MAIN CONTENT STARTS -->
<div class="column content">
<h1 class="title pt-6">Call for Papers</h1>
<hr/>
<p>We invite submissions of 4-6 pages (references not included) of original papers on the methodology that discovers learning dynamics from human learning and incorporates it into AI practices. Example topics include (but are not limited to):</p>
<ul>
<li>Experimental and computational methods for discovering learning dynamics at the neuron, neural circuits, neural population, and the behavior levels
<li>Examinations of learning dynamics at multiple time scales and their interrelationships
<li>Improved methods for decoding dynamics in brain signals
<li>How learning dynamics support task generalization (meta-learning)
<li>How learning dynamics support causality
<li>Incorporation of learning dynamics in reinforcement learning
<li>Examinations of the development of learning dynamics in infants and children
<li>Training dynamics in continual/lifelong learning settings
<li>Network optimization through training dynamics
<li>Neurocognitive-inspired AI hardware design
</ul>
</div>
</div></section></body>
</html>