Uday Shankar Shanthamallu
Arizona State University
Deep learningSignalMachine learningSimple random sampleMultidisciplinary approachControl (management)SoftwareComputer sciencePhotovoltaic systemArtificial neural networkFeature extractionSemi-supervised learningFeature learningReal-time computingTheoretical computer scienceRegularization (mathematics)Digital signal processingInformation processingComputer hardwareRobustness (computer science)
17Publications
4H-index
113Citations
Publications 18
Newest
#1Uday Shankar Shanthamallu (ASU: Arizona State University)H-Index: 4
#2Jayaraman J. Thiagarajan (LLNL: Lawrence Livermore National Laboratory)H-Index: 18
Last. Andreas Spanias (ASU: Arizona State University)H-Index: 31
view all 4 authors...
Modern data analysis pipelines are becoming increasingly complex due to the presence of multiview information sources. While graphs are effective in modeling complex relationships, in many scenarios, a single graph is rarely sufficient to succinctly represent all interactions, and hence, multilayered graphs have become popular. Though this leads to richer representations, extending solutions from the single-graph case is not straightforward. Consequently, there is a strong need for novel solutio...
5 CitationsSource
#1Uday Shankar Shanthamallu (ASU: Arizona State University)H-Index: 4
#2Jayaraman J. Thiagarajan (LLNL: Lawrence Livermore National Laboratory)H-Index: 18
Last. Andreas Spanias (ASU: Arizona State University)H-Index: 31
view all 3 authors...
May 1, 2020 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Uday Shankar Shanthamallu (ASU: Arizona State University)H-Index: 4
#2Jayaraman J. Thiagarajan (LLNL: Lawrence Livermore National Laboratory)H-Index: 18
Last. Andreas Spanias (ASU: Arizona State University)H-Index: 31
view all 3 authors...
Machine learning models that can exploit the inherent structure in data have gained prominence. In particular, there is a surge in deep learning solutions for graph-structured data, due to its wide-spread applicability in several fields. Graph attention networks (GAT), a recent addition to the broad class of feature learning models in graphs, utilizes the attention mechanism to efficiently learn continuous vector representations for semi-supervised learning problems. In this paper, we perform a ...
2 CitationsSource
#1Uday Shankar Shanthamallu (ASU: Arizona State University)H-Index: 4
#2Jayaraman J. Thiagarajan (LLNL: Lawrence Livermore National Laboratory)H-Index: 18
Last. Andreas Spanias (ASU: Arizona State University)H-Index: 31
view all 3 authors...
Machine learning models that can exploit the inherent structure in data have gained prominence. In particular, there is a surge in deep learning solutions for graph-structured data, due to its wide-spread applicability in several fields. Graph attention networks (GAT), a recent addition to the broad class of feature learning models in graphs, utilizes the attention mechanism to efficiently learn continuous vector representations for semi-supervised learning problems. In this paper, we perform a ...
#1Vivek Sivaraman Narayanaswamy (ASU: Arizona State University)H-Index: 3
#2Uday Shankar Shanthamallu (ASU: Arizona State University)H-Index: 4
Last. Farib Khondoker (ASU: Arizona State University)H-Index: 1
view all 13 authors...
Last. Farib KhondokerH-Index: 1
view all 13 authors...
May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Uday Shankar Shanthamallu (ASU: Arizona State University)H-Index: 4
#2Sunil Rao (ASU: Arizona State University)H-Index: 5
Last. Andreas Spanias (ASU: Arizona State University)H-Index: 31
view all 6 authors...
Machine Learning (ML) and Artificial Intelligence (AI) algorithms are enabling several modern smart products and devices. Furthermore, several initiatives such as smart cities and autonomous vehicles utilize AI and ML computational engines. The current and emerging applications and the growing industrial interest in AI necessitate introducing ML algorithms at the undergraduate level. In this paper, we describe a series of activities to introduce ML in undergraduate digital signal processing (DSP...
1 CitationsSource
Last. Andreas SpaniasH-Index: 31
view all 3 authors...
Machine learning models that can exploit the inherent structure in data have gained prominence. In particular, there is a surge in deep learning solutions for graph-structured data, due to its wide-spread applicability in several fields. Graph attention networks (GAT), a recent addition to the broad class of feature learning models in graphs, utilizes the attention mechanism to efficiently learn continuous vector representations for semi-supervised learning problems. In this paper, we perform a ...
2 Citations
Modern data analysis pipelines are becoming increasingly complex due to the presence of multi-view information sources. While graphs are effective in modeling complex relationships, in many scenarios a single graph is rarely sufficient to succinctly represent all interactions, and hence multi-layered graphs have become popular. Though this leads to richer representations, extending solutions from the single-graph case is not straightforward. Consequently, there is a strong need for novel solutio...
5 Citations
Modern data analysis pipelines are becoming increasingly complex due to the presence of multi-view information sources. While graphs are effective in modeling complex relationships, in many scenarios a single graph is rarely sufficient to succinctly represent all interactions, and hence multi-layered graphs have become popular. Though this leads to richer representations, extending solutions from the single-graph case is not straightforward. Consequently, there is a strong need for novel solutio...
3 Citations