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Uday Shankar Shanthamallu
Arizona State University
Machine learningComputer scienceArtificial neural networkFeature extractionFeature learning
13Publications
2H-index
44Citations
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Publications 16
Newest
May 1, 2020 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Uday Shankar Shanthamallu (ASU: Arizona State University)H-Index: 2
#2Jayaraman J. Thiagarajan (LLNL: Lawrence Livermore National Laboratory)H-Index: 14
Last. Andreas Spanias (ASU: Arizona State University)H-Index: 28
view all 3 authors...
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#1Uday Shankar Shanthamallu (ASU: Arizona State University)H-Index: 2
#2Jayaraman J. Thiagarajan (LLNL: Lawrence Livermore National Laboratory)H-Index: 14
Last. Andreas SpaniasH-Index: 28
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 ...
#1Uday Shankar Shanthamallu (ASU: Arizona State University)H-Index: 2
#2Jayaraman J. Thiagarajan (LLNL: Lawrence Livermore National Laboratory)H-Index: 14
Last. Andreas Spanias (ASU: Arizona State University)H-Index: 28
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...
2 CitationsSource
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: 2
#2Sunil Rao (ASU: Arizona State University)H-Index: 4
Last. Andreas Spanias (ASU: Arizona State University)H-Index: 28
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...
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Last. Andreas SpaniasH-Index: 28
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...
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...
2 Citations
#1Abhinav DixitH-Index: 1
Last. Mahesh K. Banavar (Clarkson University)H-Index: 14
view all 5 authors...
This work in progress paper describes software that enables online machine learning experiments in an undergraduate DSP course. This software operates in HTML5 and embeds several digital signal processing functions. The software can process natural signals such as speech and can extract various features, for machine learning applications. For example in the case of speech processing, LPC coefficients and formant frequencies can be computed. In this paper, we present speech processing, feature ex...
1 CitationsSource
#1Farib Khondoker (ASU: Arizona State University)H-Index: 1
#2Trevor Thornton (ASU: Arizona State University)H-Index: 23
Last. Uday Shankar Shanthamallu (ASU: Arizona State University)H-Index: 2
view all 4 authors...
Internet of Things (IoT) has enabled several applications related to data analytics. In this paper, an intuitive method for optimizing activity detection data is presented. Further applications include exploring detection accuracies of physical activities such as walking intensity and movement on stairs. This method utilizes different Microcontroller Units (MCUs) with embedded sensors which are used for activity detection. Additionally, this method also incorporates supervised learning - more sp...
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