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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=neural">
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  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=neural]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for neural]]></description>
  <items>
    <rdf:Seq>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/150/Activity-recognition-from-RFID-sensor-data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/71/ArRf-Activity-Recognition-with-RF"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1203/DUNE-A-Machine-Learning-Deep-UNET-based-ensemble-Approach-to-Monthly-Seasonal-and-Annual-Climate-Forecasting"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1202/Towards-a-Dynamic-Data-Driven-AI-Regional-Weather-Forecast-Model"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1177/FABULA-Intelligence-Report-Generation-Using-Retrieval-Augmented-Narrative-Construction"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1104/Knowledge-Enhanced-Neurosymbolic-Artificial-Intelligence-for-Cybersecurity-and-Privacy"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1068/Neural-Bregman-Divergences-for-Distance-Learning"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1013/A-Machine-Learning-Plume-Resolving-Model-Implementation-over-North-America-for-Mega-Wildland-Fire-Smoke-Impacts-on-Distant-Planetary-Boundary-Layers"/>
      <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/1005/The-Integration-of-Artificial-Intelligence-for-Improved-Operational-Air-Quality-Forecasting"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1003/Neural-Variational-Learning-for-Grounded-Language-Acquisition"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/954/Event-Representation-with-Sequential-Semi-Supervised-Discrete-Variables"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/389/After-75-Years-of-AI-Can-Machines-Think-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/394/After-75-years-of-AI-Can-Machines-Think-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/395/After-75-Years-of-AI-Can-Machines-Think-"/>
    </rdf:Seq>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/150/Activity-recognition-from-RFID-sensor-data">
  <title><![CDATA[Activity recognition from RFID sensor data]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/150/Activity-recognition-from-RFID-sensor-data</link>
  <description><![CDATA[As the population ages tools for aiding in the care of elderly become increasingly valuable. There is a need for a suite of tools that monitor senior citizens, help them through their day, and alert others if they need help. Several good techniques for creating systems that assist senior citizens have emerged. What all such computer systems lack is a good way to determine what a person is actually doing. Entering every task that a person does into a computer is time consuming and not practica...]]></description>
  <dc:date>2005-09-25</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/71/ArRf-Activity-Recognition-with-RF">
  <title><![CDATA[ArRf - Activity Recognition with RF]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/71/ArRf-Activity-Recognition-with-RF</link>
  <description><![CDATA[As the population ages tools for aiding in the care of elderly become increasingly valuable.  There is a need for a suite of tools that monitor senior citizens, help them through their day, and alert others if they need help.  Several good techniques for creating systems that assist senior citizens have emerged.  What all such computer systems lack is a good way to determine what a person is actually doing.  Entering every task that a person does into a computer is time consuming and not prac...]]></description>
  <dc:date>2005-09-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1203/DUNE-A-Machine-Learning-Deep-UNET-based-ensemble-Approach-to-Monthly-Seasonal-and-Annual-Climate-Forecasting">
  <title><![CDATA[DUNE: A Machine Learning Deep UNET++ based ensemble Approach to Monthly, Seasonal and Annual Climate Forecasting]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1203/DUNE-A-Machine-Learning-Deep-UNET-based-ensemble-Approach-to-Monthly-Seasonal-and-Annual-Climate-Forecasting</link>
  <description><![CDATA[Capitalizing on the recent availability of ERA5 monthly averaged, long-term data records of mean atmospheric and climate fields derived from the high-resolution reanalysis, deep learning architectures provide an alternative to physics-based daily numerical weather predictions for subseasonal to seasonal (S2S) and annual forecasts. A novel deep U-Net++-based ensemble (DUNE) neural architecture is introduced, incorporating encoder–decoder structures with residual blocks. When initialized with...]]></description>
  <dc:date>2025-10-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1202/Towards-a-Dynamic-Data-Driven-AI-Regional-Weather-Forecast-Model">
  <title><![CDATA[Towards a Dynamic Data Driven AI Regional  Weather Forecast Model]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1202/Towards-a-Dynamic-Data-Driven-AI-Regional-Weather-Forecast-Model</link>
  <description><![CDATA[The advent of long-term reanalysis datasets such as ECMWF
ERA 4/5 has enabled the development of AI-driven machine learning
models for weather forecasting. The major benefit of AI as an approach
is its ability to reduce computational forecast time from tens of hours

to tens of seconds, thereby enabling a variety of new applications rang-
ing from extreme regional weather event forecasting to first responder

aid for wildfires, severe storms, floods, oil spills, tornadoes, and other
...]]></description>
  <dc:date>2024-11-08</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1177/FABULA-Intelligence-Report-Generation-Using-Retrieval-Augmented-Narrative-Construction">
  <title><![CDATA[FABULA: Intelligence Report Generation Using Retrieval-Augmented Narrative Construction]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1177/FABULA-Intelligence-Report-Generation-Using-Retrieval-Augmented-Narrative-Construction</link>
  <description><![CDATA[Narrative construction is the process of representing disparate event information into a logical plot structure that models an end-to-end story. Intelligence analysis is an example of a domain that can benefit tremendously from narrative construction techniques, particularly in aiding analysts during the largely manual and costly process of synthesizing event information into comprehensive intelligence reports. Manual intelligence report generation is often prone to challenges such as integra...]]></description>
  <dc:date>2023-11-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1104/Knowledge-Enhanced-Neurosymbolic-Artificial-Intelligence-for-Cybersecurity-and-Privacy">
  <title><![CDATA[Knowledge-Enhanced Neurosymbolic Artificial Intelligence for Cybersecurity and Privacy]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1104/Knowledge-Enhanced-Neurosymbolic-Artificial-Intelligence-for-Cybersecurity-and-Privacy</link>
  <description><![CDATA[Neurosymbolic artificial intelligence (AI) is an emerging and quickly advancing field that
combines the subsymbolic strengths of (deep) neural networks and the explicit, symbolic
knowledge contained in knowledge graphs (KGs) to enhance explainability and safety in
AI systems. This approach addresses a key criticism of current generation systems,
namely, their inability to generate human-understandable explanations for their
outcomes and ensure safe behaviors, especially in scenarios with...]]></description>
  <dc:date>2023-09-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1068/Neural-Bregman-Divergences-for-Distance-Learning">
  <title><![CDATA[Neural Bregman Divergences for Distance Learning]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1068/Neural-Bregman-Divergences-for-Distance-Learning</link>
  <description><![CDATA[Many metric learning tasks, such as triplet learning, nearest neighbor retrieval, and visualization, are treated primarily as embedding tasks where the ultimate metric is some variant of the Euclidean distance (e.g., cosine or Mahalanobis), and the algorithm must learn to embed points into the pre-chosen space. The study of non-Euclidean geometries is often not explored, which we believe is due to a lack of tools for learning non-Euclidean measures of distance. Recent work has shown that Breg...]]></description>
  <dc:date>2023-05-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1013/A-Machine-Learning-Plume-Resolving-Model-Implementation-over-North-America-for-Mega-Wildland-Fire-Smoke-Impacts-on-Distant-Planetary-Boundary-Layers">
  <title><![CDATA[A Machine Learning Plume-Resolving Model Implementation over North America for Mega-Wildland Fire Smoke Impacts on Distant Planetary Boundary Layers]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1013/A-Machine-Learning-Plume-Resolving-Model-Implementation-over-North-America-for-Mega-Wildland-Fire-Smoke-Impacts-on-Distant-Planetary-Boundary-Layers</link>
  <description><![CDATA[Recent persistent droughts and extreme heatwave events over the Western states of the US are creating highly favorable conditions for mega wildland fires. The IPCC AR6 report suggests that such extreme events will continue occurring with increasing frequency and intensity over forested regions, globally. We have shown that smoke produced from such wildland fires, which contain the burnt biomass carbon-based particulate matter at 2.5 microns, can be dynamically transported 1000s of km to affec...]]></description>
  <dc:date>2022-01-25</dc:date>
 </item>
 <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/1005/The-Integration-of-Artificial-Intelligence-for-Improved-Operational-Air-Quality-Forecasting">
  <title><![CDATA[The Integration of Artificial Intelligence for Improved Operational Air Quality Forecasting]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1005/The-Integration-of-Artificial-Intelligence-for-Improved-Operational-Air-Quality-Forecasting</link>
  <description><![CDATA[The National Oceanic and Atmospheric Administration (NOAA) is actively seeking to integrate the latest research in Artificial Intelligence (AI) techniques to solve a number of operational challenges. We describe two efforts underway that integrate deep learning techniques to improve operational air quality (AQ) forecasting.
The first effort is a collaboration between NOAA and the University of Maryland Baltimore County (UMBC) that uses deep learning to improve bias correction of model predic...]]></description>
  <dc:date>2021-12-13</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1003/Neural-Variational-Learning-for-Grounded-Language-Acquisition">
  <title><![CDATA[Neural Variational Learning for Grounded Language Acquisition]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1003/Neural-Variational-Learning-for-Grounded-Language-Acquisition</link>
  <description><![CDATA[We propose a learning system in which language
is grounded in visual percepts without specific pre-defined
categories of terms. We present a unified generative method
to acquire a shared semantic/visual embedding that enables
the learning of language about a wide range of real-world
objects. We evaluate the efficacy of this learning by predicting
the semantics of objects and comparing the performance with
neural and non-neural inputs. We show that this generative
approach exhibits pro...]]></description>
  <dc:date>2021-08-08</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/954/Event-Representation-with-Sequential-Semi-Supervised-Discrete-Variables">
  <title><![CDATA[Event Representation with Sequential, Semi-Supervised Discrete Variables]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/954/Event-Representation-with-Sequential-Semi-Supervised-Discrete-Variables</link>
  <description><![CDATA[Within the context of event modeling and understanding, we propose a new method for neural sequence modeling that takes partially-observed sequences of discrete, external knowledge into account. We construct a sequential, neural variational autoencoder that uses a carefully defined encoder, and Gumbel-Softmax reparametrization, to allow for successful backpropagation during training. We show that our approach outperforms multiple baselines and the state-of-the-art in narrative script inductio...]]></description>
  <dc:date>2021-06-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/389/After-75-Years-of-AI-Can-Machines-Think-">
  <title><![CDATA[After 75 Years of AI, Can Machines Think?]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/389/After-75-Years-of-AI-Can-Machines-Think-</link>
  <description><![CDATA[Mathematician Alan Turing proposed a simple test to answer the question 'Can machines think?' nearly 75 years ago. Today, the surprising abilities of generative AI systems like ChatGPT make many wonder if we can finally respond positively. Dr. Finin will briefly cover AI's history leading up to the recent development of systems using neural networks and large language models like ChatGPT and what to expect in the next few years. He'll touch on what current systems can and cannot do, the ways ...]]></description>
  <dc:date>2024-04-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/394/After-75-years-of-AI-Can-Machines-Think-">
  <title><![CDATA[After 75 years of AI, Can Machines Think?]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/394/After-75-years-of-AI-Can-Machines-Think-</link>
  <description><![CDATA[Mathematician Alan Turing proposed a simple test to answer the question 'Can machines think?' nearly 75 years ago. Today, the surprising abilities of the latest generative AI systems make many wonder if we can finally respond positively. The talk briefly covers AI's history leading up to the recent development of systems using neural networks and large language models like ChatGPT and what to expect in the next few years.

A presentation given at the Charlestown Retirement Community on Apri...]]></description>
  <dc:date>2025-04-10</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/395/After-75-Years-of-AI-Can-Machines-Think-">
  <title><![CDATA[After 75 Years of AI, Can Machines Think?]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/395/After-75-Years-of-AI-Can-Machines-Think-</link>
  <description><![CDATA[Mathematician Alan Turing proposed a simple test to answer the question 'Can machines think?' nearly 75 years ago. Today, the surprising abilities of the latest generative AI systems make many wonder if we can finally respond positively. The talk briefly covers AI's history leading up to the recent development of systems using neural networks and large language models like ChatGPT and what to expect in the next few years.

A presentation given at the Charlestown Retirement Community on Apri...]]></description>
 </item>
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