S. Sumitra

Orcid: 0000-0002-0461-9789

Affiliations:
  • Indian Institute of Space Science and Technology, Department of Mathematics, Thiruvananthapuram, India


According to our database1, S. Sumitra authored at least 16 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of six.
  • Erdős number3 of five.

Timeline

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Bibliography

2024
Spectral Graph Convolutional Neural Networks in the Context of Regularization Theory.
IEEE Trans. Neural Networks Learn. Syst., April, 2024

2023
Neighborhood Preserving Kernels for Attributed Graphs.
IEEE Trans. Pattern Anal. Mach. Intell., 2023

2022
Graph kernels based on optimal node assignment.
Knowl. Based Syst., 2022

On safe sequential optimization using posterior sampling.
Proceedings of the IEEE International Conference on Signal Processing and Communications, 2022

Self-Supervised Enhancement of Latent Discovery in GANs.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Selecting the Points for Training using Graph Centrality.
CoRR, 2021

Disentanglement based Active Learning.
Proceedings of the International Joint Conference on Neural Networks, 2021

2020
Design of multi-view graph embedding using multiple kernel learning.
Eng. Appl. Artif. Intell., 2020

Framework for Designing Filters of Spectral Graph Convolutional Neural Networks in the Context of Regularization Theory.
CoRR, 2020

2019
Disentanglement based Active Learning.
CoRR, 2019

Structural Health Monitoring of Cantilever Beam, a Case Study - Using Bayesian Neural Network AND Deep Learning.
CoRR, 2019

Kernel collaborative online algorithms for multi-task learning.
Ann. Math. Artif. Intell., 2019

2017
Multiple kernel learning using single stage function approximation for binary classification problems.
Int. J. Syst. Sci., 2017

Multiple kernel learning using composite kernel functions.
Eng. Appl. Artif. Intell., 2017

Formulation of Two Stage Multiple Kernel Learning Using Regression Framework.
Proceedings of the Pattern Recognition and Machine Intelligence, 2017

Effectiveness of Representation and Length Variation of Shortest Paths in Graph Classification.
Proceedings of the Pattern Recognition and Machine Intelligence, 2017


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