<?xml version="1.0" encoding="utf-8" standalone="yes" ?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Machine Learning | Learning, Intelligence &#43; Signal Processing Lab</title>
    <link>http://lisplab.host.dartmouth.edu/tag/machine-learning/</link>
      <atom:link href="http://lisplab.host.dartmouth.edu/tag/machine-learning/index.xml" rel="self" type="application/rss+xml" />
    <description>Machine Learning</description>
    <generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><lastBuildDate>Sun, 07 Aug 2022 20:17:27 -0500</lastBuildDate>
    <image>
      <url>http://lisplab.host.dartmouth.edu/media/sharing.png</url>
      <title>Machine Learning</title>
      <link>http://lisplab.host.dartmouth.edu/tag/machine-learning/</link>
    </image>
    
    <item>
      <title>Finding Dimensionality in Large Data</title>
      <link>http://lisplab.host.dartmouth.edu/project/finding-dimensionality-in-large-data/</link>
      <pubDate>Wed, 03 Aug 2022 22:12:42 -0500</pubDate>
      <guid>http://lisplab.host.dartmouth.edu/project/finding-dimensionality-in-large-data/</guid>
      <description>&lt;p&gt;The &amp;ldquo;intrinsic&amp;rdquo; dimensionality of a dataset is a quantity of great interest in the machine learning community. There are many techniques aimed at estimating this &amp;ldquo;intrinsic&amp;rdquo; dimensionality, but there are currently none which have demonstrated scalability to large, complex datasets. In this project we aim to find the intrinsic dimension of both small and large, simple and complex datasets.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Information Propagation in Multilayer Networks</title>
      <link>http://lisplab.host.dartmouth.edu/project/information-propagation-in-multilayer-networks/</link>
      <pubDate>Wed, 03 Aug 2022 22:22:59 -0500</pubDate>
      <guid>http://lisplab.host.dartmouth.edu/project/information-propagation-in-multilayer-networks/</guid>
      <description>&lt;p&gt;With the emergence of social media, information and influence propagation in online networks has become an active field of research over the last decade. Individuals often participate in multiple social networks which leads to information spreading faster and the propagation becoming more complex. We would like to understand the pattern of such propagation in multilayer networks using game theoretic tools.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Information Propagation Through Graph Neural Networks and Relation to the Brain</title>
      <link>http://lisplab.host.dartmouth.edu/project/information-propagation-through-graph-neural-networks-and-relation-to-the-brain/</link>
      <pubDate>Wed, 03 Aug 2022 22:23:19 -0500</pubDate>
      <guid>http://lisplab.host.dartmouth.edu/project/information-propagation-through-graph-neural-networks-and-relation-to-the-brain/</guid>
      <description>&lt;p&gt;A popular theory of intelligence argues that intelligence arises from the connections between primitive computing units rather than the computing units themselves. Interestingly, neurons in the brain form topological structures for processing different types of information. We are exploring the relationship between graph topology and model performance using Graph Neural Networks, and comparing our findings to known phenomena in the brain.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Using Machine Learning for Side Channel Analysis</title>
      <link>http://lisplab.host.dartmouth.edu/project/using-machine-learning-for-side-channel-analysis/</link>
      <pubDate>Wed, 03 Aug 2022 23:11:53 -0500</pubDate>
      <guid>http://lisplab.host.dartmouth.edu/project/using-machine-learning-for-side-channel-analysis/</guid>
      <description>&lt;p&gt;Side channel analysis involves using externally recorded signals from a device (such as electromagnetic radiation or power consumption) to determine what the device is preforming. Our current work involves using this avenue of data in conjunction with machine learning techniques to accomplish two tasks. First is anomaly detection, in which a model takes as input a side channel signal and outputs a determination of whether or not the device is running software that the model has been trained on, or if it is running &amp;ldquo;anomalous&amp;rdquo; software. The second task is instruction level tracking, where a model is trained to recognize &amp;ldquo;jump&amp;rdquo; commands within code, and then mark in a recording where those jump commands occur.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>🎉 Laura Greige&#39;s Successful Ph.D. Thesis Defense 🎉</title>
      <link>http://lisplab.host.dartmouth.edu/post/laura-greige-phd-thesis-defense/</link>
      <pubDate>Sun, 07 Aug 2022 20:17:27 -0500</pubDate>
      <guid>http://lisplab.host.dartmouth.edu/post/laura-greige-phd-thesis-defense/</guid>
      <description>&lt;p&gt;Congratulations to Dr. Greige! Laura has successfully defended her PhD thesis entitled &amp;ldquo;Anomaly Detection in Competitive Multiplayer Games&amp;rdquo;.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>🎉 Yida Xin&#39;s Successful Ph.D. Thesis Defense 🎉</title>
      <link>http://lisplab.host.dartmouth.edu/post/yida-xin-phd-thesis-defense/</link>
      <pubDate>Sun, 07 Aug 2022 20:17:27 -0500</pubDate>
      <guid>http://lisplab.host.dartmouth.edu/post/yida-xin-phd-thesis-defense/</guid>
      <description>&lt;p&gt;Congratulations to Dr. Xin! Yida has successfully defended his PhD thesis entitled &amp;ldquo;Disambiguating Natural Language via Aligning Meaningful Descriptions&amp;rdquo;.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>🎉 Xiao Zhou&#39;s Successful Ph.D. Thesis Defense 🎉</title>
      <link>http://lisplab.host.dartmouth.edu/post/xiao-zhou-phd-thesis-defense/</link>
      <pubDate>Wed, 01 Dec 2021 03:35:06 -0500</pubDate>
      <guid>http://lisplab.host.dartmouth.edu/post/xiao-zhou-phd-thesis-defense/</guid>
      <description>&lt;p&gt;Congratulations to Dr. Zhou! Xiao has successfully defended his PhD thesis entitled &amp;ldquo;Competitive Deep Learning for Imaging Applications&amp;rdquo;.&lt;/p&gt;
&lt;!-- Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integer tempus augue non tempor egestas. Proin nisl nunc, dignissim in accumsan dapibus, auctor ullamcorper neque. Quisque at elit felis. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia curae; Aenean eget elementum odio. Cras interdum eget risus sit amet aliquet. In volutpat, nisl ut fringilla dignissim, arcu nisl suscipit ante, at accumsan sapien nisl eu eros.

Sed eu dui nec ligula bibendum dapibus. Nullam imperdiet auctor tortor, vel cursus mauris malesuada non. Quisque ultrices euismod dapibus. Aenean sed gravida risus. Sed nisi tortor, vulputate nec quam non, placerat porta nisl. Nunc varius lobortis urna, condimentum facilisis ipsum molestie eu. Ut molestie eleifend ligula sed dignissim. Duis ut tellus turpis. Praesent tincidunt, nunc sed congue malesuada, mauris enim maximus massa, eget interdum turpis urna et ante. Morbi sem nisl, cursus quis mollis et, interdum luctus augue. Aliquam laoreet, leo et accumsan tincidunt, libero neque aliquet lectus, a ultricies lorem mi a orci.

Mauris dapibus sem vel magna convallis laoreet. Donec in venenatis urna, vitae sodales odio. Praesent tortor diam, varius non luctus nec, bibendum vel est. Quisque id sem enim. Maecenas at est leo. Vestibulum tristique pellentesque ex, blandit placerat nunc eleifend sit amet. Fusce eget lectus bibendum, accumsan mi quis, luctus sem. Etiam vitae nulla scelerisque, eleifend odio in, euismod quam. Etiam porta ullamcorper massa, vitae gravida turpis euismod quis. Mauris sodales sem ac ultrices viverra. In placerat ultrices sapien. Suspendisse eu arcu hendrerit, luctus tortor cursus, maximus dolor. Proin et velit et quam gravida dapibus. Donec blandit justo ut consequat tristique. --&gt;</description>
    </item>
    
    <item>
      <title>🎉 Louis Jensen&#39;s Successful Ph.D. Thesis Defense 🎉</title>
      <link>http://lisplab.host.dartmouth.edu/post/louis-jensen-phd-thesis-defense/</link>
      <pubDate>Fri, 06 Aug 2021 03:35:06 -0500</pubDate>
      <guid>http://lisplab.host.dartmouth.edu/post/louis-jensen-phd-thesis-defense/</guid>
      <description>&lt;p&gt;Congratulations to Dr. Jensen! Louis has successfully defended his PhD thesis entitled &amp;ldquo;Minimalism in Deep Learning&amp;rdquo;.&lt;/p&gt;
&lt;!-- Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integer tempus augue non tempor egestas. Proin nisl nunc, dignissim in accumsan dapibus, auctor ullamcorper neque. Quisque at elit felis. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia curae; Aenean eget elementum odio. Cras interdum eget risus sit amet aliquet. In volutpat, nisl ut fringilla dignissim, arcu nisl suscipit ante, at accumsan sapien nisl eu eros.

Sed eu dui nec ligula bibendum dapibus. Nullam imperdiet auctor tortor, vel cursus mauris malesuada non. Quisque ultrices euismod dapibus. Aenean sed gravida risus. Sed nisi tortor, vulputate nec quam non, placerat porta nisl. Nunc varius lobortis urna, condimentum facilisis ipsum molestie eu. Ut molestie eleifend ligula sed dignissim. Duis ut tellus turpis. Praesent tincidunt, nunc sed congue malesuada, mauris enim maximus massa, eget interdum turpis urna et ante. Morbi sem nisl, cursus quis mollis et, interdum luctus augue. Aliquam laoreet, leo et accumsan tincidunt, libero neque aliquet lectus, a ultricies lorem mi a orci.

Mauris dapibus sem vel magna convallis laoreet. Donec in venenatis urna, vitae sodales odio. Praesent tortor diam, varius non luctus nec, bibendum vel est. Quisque id sem enim. Maecenas at est leo. Vestibulum tristique pellentesque ex, blandit placerat nunc eleifend sit amet. Fusce eget lectus bibendum, accumsan mi quis, luctus sem. Etiam vitae nulla scelerisque, eleifend odio in, euismod quam. Etiam porta ullamcorper massa, vitae gravida turpis euismod quis. Mauris sodales sem ac ultrices viverra. In placerat ultrices sapien. Suspendisse eu arcu hendrerit, luctus tortor cursus, maximus dolor. Proin et velit et quam gravida dapibus. Donec blandit justo ut consequat tristique. --&gt;</description>
    </item>
    
    <item>
      <title>🎉 Kieran Wang&#39;s Successful Ph.D. Thesis Defense 🎉</title>
      <link>http://lisplab.host.dartmouth.edu/post/kieran-wang-phd-thesis-defense/</link>
      <pubDate>Wed, 16 Dec 2020 03:35:06 -0500</pubDate>
      <guid>http://lisplab.host.dartmouth.edu/post/kieran-wang-phd-thesis-defense/</guid>
      <description>&lt;p&gt;Congratulations to Dr. Wang! Kieran has successfully defended his PhD thesis entitled &amp;ldquo;Topics of Deep Learning in Security and Compression&amp;rdquo;.&lt;/p&gt;
&lt;!-- Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integer tempus augue non tempor egestas. Proin nisl nunc, dignissim in accumsan dapibus, auctor ullamcorper neque. Quisque at elit felis. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia curae; Aenean eget elementum odio. Cras interdum eget risus sit amet aliquet. In volutpat, nisl ut fringilla dignissim, arcu nisl suscipit ante, at accumsan sapien nisl eu eros.

Sed eu dui nec ligula bibendum dapibus. Nullam imperdiet auctor tortor, vel cursus mauris malesuada non. Quisque ultrices euismod dapibus. Aenean sed gravida risus. Sed nisi tortor, vulputate nec quam non, placerat porta nisl. Nunc varius lobortis urna, condimentum facilisis ipsum molestie eu. Ut molestie eleifend ligula sed dignissim. Duis ut tellus turpis. Praesent tincidunt, nunc sed congue malesuada, mauris enim maximus massa, eget interdum turpis urna et ante. Morbi sem nisl, cursus quis mollis et, interdum luctus augue. Aliquam laoreet, leo et accumsan tincidunt, libero neque aliquet lectus, a ultricies lorem mi a orci.

Mauris dapibus sem vel magna convallis laoreet. Donec in venenatis urna, vitae sodales odio. Praesent tortor diam, varius non luctus nec, bibendum vel est. Quisque id sem enim. Maecenas at est leo. Vestibulum tristique pellentesque ex, blandit placerat nunc eleifend sit amet. Fusce eget lectus bibendum, accumsan mi quis, luctus sem. Etiam vitae nulla scelerisque, eleifend odio in, euismod quam. Etiam porta ullamcorper massa, vitae gravida turpis euismod quis. Mauris sodales sem ac ultrices viverra. In placerat ultrices sapien. Suspendisse eu arcu hendrerit, luctus tortor cursus, maximus dolor. Proin et velit et quam gravida dapibus. Donec blandit justo ut consequat tristique. --&gt;</description>
    </item>
    
    <item>
      <title>🎉 Ken Zhou&#39;s Successful M.S. Thesis Defense 🎉</title>
      <link>http://lisplab.host.dartmouth.edu/post/ken-zhou-ms-thesis-defense/</link>
      <pubDate>Tue, 07 Aug 2018 21:56:00 -0500</pubDate>
      <guid>http://lisplab.host.dartmouth.edu/post/ken-zhou-ms-thesis-defense/</guid>
      <description>&lt;p&gt;Congratulations to Ken Zhou! Ken has successfully defended his MS thesis entitled &amp;ldquo;Bidirectional Long Short-term Memory Network For Proto-object Representation&amp;rdquo;.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>🎉 Tao Xiong&#39;s Successful Ph.D. Thesis Defense 🎉</title>
      <link>http://lisplab.host.dartmouth.edu/post/tao-xiong-phd-thesis-defense/</link>
      <pubDate>Mon, 07 Aug 2017 20:17:27 -0500</pubDate>
      <guid>http://lisplab.host.dartmouth.edu/post/tao-xiong-phd-thesis-defense/</guid>
      <description>&lt;p&gt;Congratulations to Dr. Xiong! Tao has successfully defended his PhD thesis entitled &amp;ldquo;Data-Driven Representation, Learning and Applications: From Compressed
Sensing to Deep Neural Networks&amp;rdquo;.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Feature-aided multiple hypothesis tracking using topological and statistical behavior classifiers</title>
      <link>http://lisplab.host.dartmouth.edu/publication/rouse-feature-aided-2015/</link>
      <pubDate>Thu, 01 Jan 2015 00:00:00 +0000</pubDate>
      <guid>http://lisplab.host.dartmouth.edu/publication/rouse-feature-aided-2015/</guid>
      <description></description>
    </item>
    
  </channel>
</rss>
