# Raghavan Krishnan

Orcid: 0000-0001-9409-2011
According to our database

Collaborative distances:

^{1}, Raghavan Krishnan authored at least 29 papers between 2015 and 2024.Collaborative distances:

## Timeline

#### Legend:

Book In proceedings Article PhD thesis Dataset Other## Links

#### On csauthors.net:

## Bibliography

2024

CoRR, 2024

2023

Int. J. High Perform. Comput. Appl., July, 2023

CoRR, 2023

CoRR, 2023

CoRR, 2023

CoRR, 2023

Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles.

CoRR, 2023

SF-SFD: Stochastic Optimization of Fourier Coefficients to Generate Space-Filling Designs.

Proceedings of the Winter Simulation Conference, 2023

Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations.

Proceedings of the IEEE International Conference on Cluster Computing, 2023

2022

IEEE Trans. Big Data, 2022

Proceedings of the IEEE/ACM Workshop on Workflows in Support of Large-Scale Science, 2022

Automated Continual Learning of Defect Identification in Coherent Diffraction Imaging.

Proceedings of the IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications, 2022

Proceedings of the 26th International Conference on Pattern Recognition, 2022

2021

Distributed Min-Max Learning Scheme for Neural Networks With Applications to High-Dimensional Classification.

IEEE Trans. Neural Networks Learn. Syst., 2021

CoRR, 2021

CoRR, 2021

CoRR, 2021

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Direct Error Driven Learning for Classification in Applications Generating Big-Data.

Proceedings of the Development and Analysis of Deep Learning Architectures, 2020

Direct Error-Driven Learning for Deep Neural Networks With Applications to Big Data.

IEEE Trans. Neural Networks Learn. Syst., 2020

Online Optimal Adaptive Control of a Class of Uncertain Nonlinear Discrete-time Systems.

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

2019

A Multi-Step Nonlinear Dimension-Reduction Approach with Applications to Big Data.

IEEE Trans. Knowl. Data Eng., 2019

A Hierarchical Dimension Reduction Approach for Big Data with Application to Fault Diagnostics.

Big Data Res., 2019

2018

Proceedings of the INNS Conference on Big Data and Deep Learning 2018, 2018

Direct Error Driven Learning for Deep Neural Networks with Applications to Bigdata.

Proceedings of the INNS Conference on Big Data and Deep Learning 2018, 2018

Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018

Distributed Learning of Deep Sparse Neural Networks for High-dimensional Classification.

Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018

2017

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

2015

Hierarchical Mahalanobis Distance Clustering Based Technique for Prognostics in Applications Generating Big Data.

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2015