Raghavan Krishnan

Orcid: 0000-0001-9409-2011

According to our database1, Raghavan Krishnan authored at least 29 papers between 2015 and 2024.

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Bibliography

2024
Advancing Anomaly Detection in Computational Workflows with Active Learning.
CoRR, 2024

2023
Graph neural networks for detecting anomalies in scientific workflows.
Int. J. High Perform. Comput. Appl., July, 2023

Forward Gradients for Data-Driven CFD Wall Modeling.
CoRR, 2023

Self-supervised Learning for Anomaly Detection in Computational Workflows.
CoRR, 2023

Flow-Bench: A Dataset for Computational Workflow Anomaly Detection.
CoRR, 2023

Learning Continually on a Sequence of Graphs - The Dynamical System Way.
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


2022
A Game Theoretic Approach for Addressing Domain-Shift in Big-Data.
IEEE Trans. Big Data, 2022

Workflow Anomaly Detection with Graph Neural Networks.
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

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification.
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

Cooperative Deep Q-learning Framework for Environments Providing Image Feedback.
CoRR, 2021

Learning to Control using Image Feedback.
CoRR, 2021

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification.
CoRR, 2021

Formalizing the Generalization-Forgetting Trade-off in Continual Learning.
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
A Multi-step Nonlinear Dimension-reduction Approach with Applications to Bigdata.
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

A Minimax Approach for Classification with Big-data.
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
Deep learning inspired prognostics scheme for applications generating big data.
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


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