Browsing by Advisor "Chepuri, Sundeep Prabhakar"
Now showing items 1-5 of 5
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Going Beyond Pairwise Relationships: Representation Learning on Simplicial Complexes
Relational data appear naturally in many real-world applications and can be modeled using mathematical structures known as graphs. Graphs model entities as nodes and their pairwise relations or interactions as edges. For ... -
Graph Neural Networks with Parallel Local Neighborhood Aggregations
Graph neural networks (GNNs) have become very popular for processing and analyzing graph-structured data in the last few years. Using message passing as their basic building blocks that aggregate information from neighborhoods, ... -
Learning with Multi-domain and Multi-view Graph Data
In many applications, we observe large volumes of data supported on irregular (non-Euclidean) domains. In graph signal processing (GSP) and graph machine learning (GML), data is indexed using the nodes of a graph and ... -
Sampling, Recovery, and Learning Methods for Graphs and Simplicial Complexes
Many datasets are often supported by irregular non-Euclidean domains. Graph signal processing (GSP) and topological signal processing (TSP) enables effective processing of such data by modeling interactions among data ... -
Signal Processing Algorithms for Next-Generation Wireless Systems: Reconfigurable Intelligent Surfaces and Integrated Sensing and Communications
Next-generation wireless systems aim to revolutionize communication by achieving data rates up to terabits per second while also supporting diverse applications such as autonomous mobility, industrial automation, and ...