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<oembed><version>1.0</version><provider_name>Department of Computer Science</provider_name><provider_url>https://www.cs.jhu.edu</provider_url><title>CS Seminar Series: Carlee Joe-Wong, Carnegie Mellon University, Carnegie Mellon University &#x2013; &#x201C;Optimizing the Cost of Distributed Learning&#x201D; - Department of Computer Science</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content"&gt;&lt;a href="https://www.cs.jhu.edu/event/cs-seminar-series-carlee-joe-wong-carnegie-mellon-university-carnegie-mellon-university-optimizing-the-cost-of-distributed-learning/"&gt;CS Seminar Series: Carlee Joe-Wong, Carnegie Mellon University, Carnegie Mellon University &#x2013; &#x201C;Optimizing the Cost of Distributed Learning&#x201D;&lt;/a&gt;&lt;/blockquote&gt;
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&lt;/script&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.cs.jhu.edu/event/cs-seminar-series-carlee-joe-wong-carnegie-mellon-university-carnegie-mellon-university-optimizing-the-cost-of-distributed-learning/embed/" width="600" height="338" title="&#x201C;CS Seminar Series: Carlee Joe-Wong, Carnegie Mellon University, Carnegie Mellon University &#x2013; &#x201C;Optimizing the Cost of Distributed Learning&#x201D;&#x201D; &#x2014; Department of Computer Science" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;</html><description>LocationZoom link: https://wse.zoom.us/j/95467665624AbstractAs machine learning models are trained on ever-larger and more complex datasets, it has become standard to distribute this training across multiple physical computing devices. Such an approach offers a number of potential benefits, including reduced training time and storage needs due to parallelization. Distributed stochastic gradient descent (SGD) is a common iterative&hellip;</description></oembed>
