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<h1 class="title">ICLR 2020 Notes: Speakers and Workshops</h1>
<p class="byline">May 5, 2020</p>
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<div id="toc"><h2>Table of Contents</h2><ul>
<h1 class="title">ICLR 2020 Notes: Speakers and Workshops</h1>
<p class="byline">May 5, 2020</p>
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<div id="toc"><h2>Table of Contents</h2><ul>
<li><a href="#the-format-of-the-virtual-conference">The Format of the Virtual Conference</a></li>
<li><a href="#speakers">Speakers</a>
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<p>Cognitive science is fascinating, and I believe that collaboration between ML practitioners and cognitive scientists will greatly help advance both fields. I only watched <a href="https://baicsworkshop.github.io/program/baics_45.html">Leslie Kaelblings presentation</a>, which echoes a lot of things from her talk at the main conference. It complements it nicely, with more focus on intelligence, especially <em>embodied</em> intelligence. I think she has the right approach to relationships between AI and natural science, explicitly listing the things from her work that would be helpful to natural scientists, and things she wish she knew about natural intelligences. It raises many fascinating questions on ourselves, what we build, and what we understand. I felt it was very motivational!</p>
<h3 id="integration-of-deep-neural-models-and-differential-equations"><a href="https://iclr.cc/virtual_2020/workshops_5.html">Integration of Deep Neural Models and Differential Equations</a></h3>
<p>I didnt attend this workshop, but I think I will watch the presentations if I can find the time. I have found the intersection of differential equations and ML very interesting, ever since the famous <a href="https://papers.nips.cc/paper/7892-neural-ordinary-differential-equations">NeurIPS best paper</a> on Neural ODEs. I think that such improvements to ML theory from other fields in mathematics would be extremely beneficial to a better understanding of the systems we build.</p>
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