Raghavan Krishnan

According to our database, Raghavan Krishnan authored at least 28 papers between 2015 and 2023.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2023

Graph neural networks for detecting anomalies in scientific workflows.

Hongwei Jin, Krishnan Raghavan, George Papadimitriou, Cong Wang, Anirban Mandal, Mariam Kiran, Ewa Deelman, Prasanna Balaprakash

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

Forward Gradients for Data-Driven CFD Wall Modeling.

Jan Hückelheim, Tadbhagya Kumar, Krishnan Raghavan, Pinaki Pal

CoRR, 2023

Self-supervised Learning for Anomaly Detection in Computational Workflows.

Hongwei Jin, Krishnan Raghavan, George Papadimitriou, Cong Wang, Anirban Mandal, Ewa Deelman, Prasanna Balaprakash

CoRR, 2023

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

George Papadimitriou, Hongwei Jin, Cong Wang, Krishnan Raghavan, Anirban Mandal, Prasanna Balaprakash, Ewa Deelman

CoRR, 2023

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

Krishnan Raghavan, Prasanna Balaprakash

CoRR, 2023

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

Romit Maulik, Romain Egele, Krishnan Raghavan, Prasanna Balaprakash

CoRR, 2023

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

Manisha Garg, Tyler H. Chang, Krishnan Raghavan

Proceedings of the Winter Simulation Conference, 2023

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

Francieli Boito, Jim M. Brandt, Valeria Cardellini, Philip H. Carns, Florina M. Ciorba, Hilary Egan, Ahmed Eleliemy, Ann C. Gentile, Thomas Gruber, Jeff Hanson, Utz-Uwe Haus, Kevin A. Huck, Thomas Ilsche, Thomas Jakobsche, Terry R. Jones, Sven Karlsson, Abdullah Mueen, Michael Ott, Tapasya Patki, Ivy Peng, Krishnan Raghavan, Stephen Simms, Kathleen Shoga, Michael T. Showerman, Devesh Tiwari, Torsten Wilde, Keiji Yamamoto

Proceedings of the IEEE International Conference on Cluster Computing, 2023

2022

A Game Theoretic Approach for Addressing Domain-Shift in Big-Data.

Krishnan Raghavan, Sarangapani Jagannathan, V. A. Samaranayake

IEEE Trans. Big Data, 2022

Workflow Anomaly Detection with Graph Neural Networks.

Hongwei Jin, Krishnan Raghavan, George Papadimitriou, Cong Wang, Anirban Mandal, Patrycja Krawczuk, Loïc Pottier, Mariam Kiran, Ewa Deelman, Prasanna Balaprakash

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.

Orcun Yildiz, Henry Chan, Krishnan Raghavan, William Judge, Mathew J. Cherukara, Prasanna Balaprakash, Subramanian Sankaranarayanan, Tom Peterka

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

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification.

Romain Egele, Romit Maulik, Krishnan Raghavan, Bethany Lusch, Isabelle Guyon, Prasanna Balaprakash

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.

Raghavan Krishnan, Shweta Garg, Sarangapani Jagannathan, V. A. Samaranayake

IEEE Trans. Neural Networks Learn. Syst., 2021

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

Raghavan Krishnan, Vignesh Narayanan, Jagannathan Sarangapani

CoRR, 2021

Learning to Control using Image Feedback.

Krishnan Raghavan, Vignesh Narayanan, Jagannathan Saraangapani

CoRR, 2021

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification.

Romain Egele, Romit Maulik, Krishnan Raghavan, Prasanna Balaprakash, Bethany Lusch

CoRR, 2021

Formalizing the Generalization-Forgetting Trade-off in Continual Learning.

Krishnan Raghavan, Prasanna Balaprakash

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.

Raghavan Krishnan, Sarangapani Jagannathan, V. A. Samaranayake

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

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

Raghavan Krishnan, Sarangapani Jagannathan, V. A. Samaranayake

IEEE Trans. Neural Networks Learn. Syst., 2020

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

Rohollah Moghadam, Pappa Natarajan, Raghavan Krishnan, Sarangapani Jagannathan

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

2019

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

Raghavan Krishnan, V. A. Samaranayake, Sarangapani Jagannathan

IEEE Trans. Knowl. Data Eng., 2019

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

Raghavan Krishnan, V. A. Samaranayake, Sarangapani Jagannathan

Big Data Res., 2019

2018

A Multi-step Nonlinear Dimension-reduction Approach with Applications to Bigdata.

Raghavan Krishnan, V. A. Samaranayake, Sarangapani Jagannathan

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.

Raghavan Krishnan, Sarangapani Jagannathan, V. A. Samaranayake

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

A Minimax Approach for Classification with Big-data.

Raghavan Krishnan, Sarangapani Jagannathan, V. A. Samaranayake

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

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

Shweta Garg, Raghavan Krishnan, Sarangapani Jagannathan, V. A. Samaranayake

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

2017

Deep learning inspired prognostics scheme for applications generating big data.

Raghavan Krishnan, Sarangapani Jagannathan, V. A. Samaranayake

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.

Raghavan Krishnan, Sarangapani Jagannathan

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2015