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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=convolutional+neural+network">
  <cc:license rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=convolutional+neural+network]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for convolutional neural network]]></description>
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      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1009/Cybersecurity-Knowledge-Graph-Improvement-with-Graph-Neural-Networks"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/927/A-hybrid-quantum-enabled-RBM-advantage-convolutional-autoencoders-for-quantum-image-compression-and-generative-learning"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/881/A-Hybrid-Quantum-Enabled-RBM-Advantage-Convolutional-Autoencoders-for-Quantum-Image-Compression-and-Generative-Learning"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/882/RBM-Image-Generation-Using-the-D-Wave-2000Q"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/915/Deep-Representation-of-Lyrical-Style-and-Semantics-for-Music-Recommendation"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/932/Convexification-and-Deconvexification-for-Training-Artificial-Neural-Networks"/>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1009/Cybersecurity-Knowledge-Graph-Improvement-with-Graph-Neural-Networks">
  <title><![CDATA[Cybersecurity Knowledge Graph Improvement with Graph Neural Networks]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1009/Cybersecurity-Knowledge-Graph-Improvement-with-Graph-Neural-Networks</link>
  <description><![CDATA[Cybersecurity Knowledge Graphs (CKGs) help in
aggregating information about cyber-events. CKGs combined
with reasoning and querying systems such as SPARQL enable
security researchers to look up information about past cyberevents
that is helpful in understanding future cyber-events or
drawing similarity with a known cyber-event recorded in a
CKG. CKGs have assertions in the form of semantic triples. The
triples describe a relationship between a subject and object, both
of which are cyb...]]></description>
  <dc:date>2021-12-15</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/927/A-hybrid-quantum-enabled-RBM-advantage-convolutional-autoencoders-for-quantum-image-compression-and-generative-learning">
  <title><![CDATA[A hybrid quantum enabled RBM advantage: convolutional autoencoders for quantum image compression and generative learning]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/927/A-hybrid-quantum-enabled-RBM-advantage-convolutional-autoencoders-for-quantum-image-compression-and-generative-learning</link>
  <description><![CDATA[Understanding how the D-Wave quantum computer could be used for machine learning problems is of growing interest. Our work explores the feasibility of using the D-Wave as a sampler for a machine learning task. We describe a hybrid method that combines a classical deep neural network autoencoder with a quantum annealing Restricted Boltzmann Machine (RBM) using the D-Wave for image generation. Our method overcomes two key limitations in the 2000-qubit D-Wave processor, namely the limited number...]]></description>
  <dc:date>2020-05-20</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/881/A-Hybrid-Quantum-Enabled-RBM-Advantage-Convolutional-Autoencoders-for-Quantum-Image-Compression-and-Generative-Learning">
  <title><![CDATA[A Hybrid Quantum Enabled RBM Advantage: Convolutional Autoencoders for Quantum Image Compression and Generative Learning]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/881/A-Hybrid-Quantum-Enabled-RBM-Advantage-Convolutional-Autoencoders-for-Quantum-Image-Compression-and-Generative-Learning</link>
  <description><![CDATA[Understanding how the D-Wave quantum computer could be used for machine learning problems is of growing interest. Our work evaluates the feasibility of using the D-Wave as a sampler for machine learning. We describe a hybrid system that combines a classical deep neural network autoencoder with a quantum annealing Restricted Boltzmann Machine (RBM) using the D-Wave. We evaluate our hybrid autoencoder algorithm using two datasets, the MNIST dataset and MNIST Fashion dataset. We evaluate the qua...]]></description>
  <dc:date>2020-01-31</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/882/RBM-Image-Generation-Using-the-D-Wave-2000Q">
  <title><![CDATA[RBM Image Generation Using the D-Wave 2000Q]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/882/RBM-Image-Generation-Using-the-D-Wave-2000Q</link>
  <description><![CDATA[We describe a hybrid approach that combines a deep convolutional neural network autoencoder and a quantum
Restricted Boltzmann Machine (RBM) for image generation using the D-Wave 2000Q. We compare the quantum learned
distribution with the classical learned distribution, and quantify the quantum effects on latent representations.]]></description>
  <dc:date>2019-10-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/915/Deep-Representation-of-Lyrical-Style-and-Semantics-for-Music-Recommendation">
  <title><![CDATA[Deep Representation of Lyrical Style and Semantics for Music Recommendation]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/915/Deep-Representation-of-Lyrical-Style-and-Semantics-for-Music-Recommendation</link>
  <description><![CDATA[In an increasingly mobile and connected world, digital music consumption has rapidly increased. More recently, faster and cheaper mobile bandwidth has given the average mobile user the potential to access large troves of music through streaming services like Spotify and Google Music that boast catalogs with tens of millions of songs. At this scale, effective music recommendation is an important part of user experience and music discovery. Collaborative filtering (CF), a popular technique used...]]></description>
  <dc:date>2017-05-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/932/Convexification-and-Deconvexification-for-Training-Artificial-Neural-Networks">
  <title><![CDATA[Convexification and Deconvexification for Training Artificial Neural Networks]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/932/Convexification-and-Deconvexification-for-Training-Artificial-Neural-Networks</link>
  <description><![CDATA[The purpose of this dissertation research is to overcome a fundamental problem in the theory and application of artificial neural networks (ANNs). The problem, called the local minimum problem in training ANNs, has plagued the ANN community since the middle of 1980s.  ANNs trained with backpropagation are extensively utilized to solve various tasks in artificial intelligence fields for decades. The computing power of ANNs is derived through its particularly distributed structure together with...]]></description>
  <dc:date>2016-05-01</dc:date>
 </item>
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