Arvind Kumar Shekar

Orcid: 0000-0002-5853-5310

According to our database1, Arvind Kumar Shekar authored at least 13 papers between 2017 and 2023.

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

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2023
Visual Concept Programming: A Visual Analytics Approach to Injecting Human Intelligence at Scale.
IEEE Trans. Vis. Comput. Graph., 2023

2022
Where Can We Help? A Visual Analytics Approach to Diagnosing and Improving Semantic Segmentation of Movable Objects.
IEEE Trans. Vis. Comput. Graph., 2022

Self-supervised Semantic Segmentation Grounded in Visual Concepts.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

2021
VATLD: A Visual Analytics System to Assess, Understand and Improve Traffic Light Detection.
IEEE Trans. Vis. Comput. Graph., 2021

Label-Free Robustness Estimation of Object Detection CNNs for Autonomous Driving Applications.
Int. J. Comput. Vis., 2021

Novelty-based Generalization Evaluation for Traffic Light Detection.
Proceedings of the 20th IEEE International Conference on Machine Learning and Applications, 2021

2020
Multivariate Correlation Analysis for Supervised Feature Selection in High-Dimensional Data
PhD thesis, 2020

2019
Building Robust Prediction Models for Defective Sensor Data Using Artificial Neural Networks.
Proceedings of the 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019), 2019

2018
Building robust prediction models for defective sensor data using Artificial Neural Networks.
CoRR, 2018

Selection of Relevant and Non-Redundant Multivariate Ordinal Patterns for Time Series Classification.
Proceedings of the Discovery Science - 21st International Conference, 2018

2017
Including Multi-feature Interactions and Redundancy for Feature Ranking in Mixed Datasets.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Framework for Exploring and Understanding Multivariate Correlations.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Diverse Selection of Feature Subsets for Ensemble Regression.
Proceedings of the Big Data Analytics and Knowledge Discovery, 2017


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