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Journal Articles
- 2024
[j7]Krishnan Raghavan
, Vignesh Narayanan, Sarangapani Jagannathan:
Cooperative Deep Q-Learning Framework for Environments Providing Image Feedback. IEEE Trans. Neural Networks Learn. Syst. 35(7): 9267-9276 (2024)- 2023
- [j6]Hongwei Jin, Krishnan Raghavan, George Papadimitriou, Cong Wang, Anirban Mandal, Mariam Kiran, Ewa Deelman, Prasanna Balaprakash:
Graph neural networks for detecting anomalies in scientific workflows. Int. J. High Perform. Comput. Appl. 37(3-4): 394-411 (2023) - 2022
- [j5]Krishnan Raghavan, Sarangapani Jagannathan, V. A. Samaranayake:
A Game Theoretic Approach for Addressing Domain-Shift in Big-Data. IEEE Trans. Big Data 8(6): 1610-1621 (2022) - 2021
- [j4]Raghavan Krishnan, Shweta Garg, Sarangapani Jagannathan, V. A. Samaranayake:
Distributed Min-Max Learning Scheme for Neural Networks With Applications to High-Dimensional Classification. IEEE Trans. Neural Networks Learn. Syst. 32(10): 4323-4333 (2021) - 2020
- [j3]Raghavan Krishnan, Sarangapani Jagannathan, V. A. Samaranayake:
Direct Error-Driven Learning for Deep Neural Networks With Applications to Big Data. IEEE Trans. Neural Networks Learn. Syst. 31(5): 1763-1770 (2020) - 2019
- [j2]Raghavan Krishnan, V. A. Samaranayake, Sarangapani Jagannathan:
A Hierarchical Dimension Reduction Approach for Big Data with Application to Fault Diagnostics. Big Data Res. 18 (2019) - [j1]Raghavan Krishnan, V. A. Samaranayake, Sarangapani Jagannathan:
A Multi-Step Nonlinear Dimension-Reduction Approach with Applications to Big Data. IEEE Trans. Knowl. Data Eng. 31(12): 2249-2261 (2019)
Conference and Workshop Papers
- 2023
- [c13]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:
Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations. CLUSTER Workshops 2023: 37-43 - [c12]Manisha Garg, Tyler H. Chang, Krishnan Raghavan:
SF-SFD: Stochastic Optimization of Fourier Coefficients to Generate Space-Filling Designs. WSC 2023: 3636-3646 - 2022
- [c11]Romain Egele, Romit Maulik, Krishnan Raghavan, Bethany Lusch, Isabelle Guyon, Prasanna Balaprakash:
AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification. ICPR 2022: 1908-1914 - [c10]Orcun Yildiz, Henry Chan, Krishnan Raghavan, William Judge, Mathew J. Cherukara, Prasanna Balaprakash, Subramanian Sankaranarayanan, Tom Peterka:
Automated Continual Learning of Defect Identification in Coherent Diffraction Imaging. AI4S 2022: 1-6 - [c9]Hongwei Jin, Krishnan Raghavan, George Papadimitriou, Cong Wang, Anirban Mandal, Patrycja Krawczuk, Loïc Pottier, Mariam Kiran, Ewa Deelman, Prasanna Balaprakash:
Workflow Anomaly Detection with Graph Neural Networks. WORKS@SC 2022: 35-42 - 2021
- [c8]Krishnan Raghavan, Prasanna Balaprakash:
Formalizing the Generalization-Forgetting Trade-off in Continual Learning. NeurIPS 2021: 17284-17297 - 2020
- [c7]Rohollah Moghadam, Pappa Natarajan, Raghavan Krishnan, Sarangapani Jagannathan:
Online Optimal Adaptive Control of a Class of Uncertain Nonlinear Discrete-time Systems. IJCNN 2020: 1-6 - 2018
- [c6]Raghavan Krishnan, Sarangapani Jagannathan, V. A. Samaranayake:
A Minimax Approach for Classification with Big-data. IEEE BigData 2018: 1437-1444 - [c5]Shweta Garg, Raghavan Krishnan, Sarangapani Jagannathan, V. A. Samaranayake:
Distributed Learning of Deep Sparse Neural Networks for High-dimensional Classification. IEEE BigData 2018: 1587-1592 - [c4]Raghavan Krishnan, V. A. Samaranayake, Sarangapani Jagannathan:
A Multi-step Nonlinear Dimension-reduction Approach with Applications to Bigdata. INNS Conference on Big Data 2018: 81-88 - [c3]Raghavan Krishnan, Sarangapani Jagannathan, V. A. Samaranayake:
Direct Error Driven Learning for Deep Neural Networks with Applications to Bigdata. INNS Conference on Big Data 2018: 89-95 - 2017
- [c2]Raghavan Krishnan, Sarangapani Jagannathan, V. A. Samaranayake:
Deep learning inspired prognostics scheme for applications generating big data. IJCNN 2017: 3296-3302 - 2015
- [c1]Raghavan Krishnan, Sarangapani Jagannathan:
Hierarchical Mahalanobis Distance Clustering Based Technique for Prognostics in Applications Generating Big Data. SSCI 2015: 516-521
Parts in Books or Collections
- 2020
- [p1]Raghavan Krishnan, Sarangapani Jagannathan, V. A. Samaranayake:
Direct Error Driven Learning for Classification in Applications Generating Big-Data. Development and Analysis of Deep Learning Architectures 2020: 1-29
Informal and Other Publications
- 2024
- [i12]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:
Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations. CoRR abs/2401.16971 (2024) - [i11]Krishnan Raghavan, George Papadimitriou, Hongwei Jin, Anirban Mandal, Mariam Kiran, Prasanna Balaprakash, Ewa Deelman:
Advancing Anomaly Detection in Computational Workflows with Active Learning. CoRR abs/2405.06133 (2024) - [i10]Hongwei Jin, George Papadimitriou, Krishnan Raghavan, Pawel Zuk, Prasanna Balaprakash, Cong Wang, Anirban Mandal, Ewa Deelman:
Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning. CoRR abs/2407.17545 (2024) - 2023
- [i9]Romit Maulik, Romain Egele, Krishnan Raghavan, Prasanna Balaprakash:
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles. CoRR abs/2302.09748 (2023) - [i8]Krishnan Raghavan, Prasanna Balaprakash:
Learning Continually on a Sequence of Graphs - The Dynamical System Way. CoRR abs/2305.12030 (2023) - [i7]George Papadimitriou, Hongwei Jin, Cong Wang, Krishnan Raghavan, Anirban Mandal, Prasanna Balaprakash, Ewa Deelman:
Flow-Bench: A Dataset for Computational Workflow Anomaly Detection. CoRR abs/2306.09930 (2023) - [i6]Hongwei Jin, Krishnan Raghavan, George Papadimitriou, Cong Wang, Anirban Mandal, Ewa Deelman, Prasanna Balaprakash:
Self-supervised Learning for Anomaly Detection in Computational Workflows. CoRR abs/2310.01247 (2023) - [i5]Jan Hückelheim, Tadbhagya Kumar, Krishnan Raghavan, Pinaki Pal:
Forward Gradients for Data-Driven CFD Wall Modeling. CoRR abs/2311.11876 (2023) - 2021
- [i4]Krishnan Raghavan, Prasanna Balaprakash:
Formalizing the Generalization-Forgetting Trade-off in Continual Learning. CoRR abs/2109.14035 (2021) - [i3]Romain Egele, Romit Maulik, Krishnan Raghavan, Prasanna Balaprakash, Bethany Lusch:
AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification. CoRR abs/2110.13511 (2021) - [i2]Krishnan Raghavan, Vignesh Narayanan, Jagannathan Saraangapani:
Learning to Control using Image Feedback. CoRR abs/2110.15290 (2021) - [i1]Raghavan Krishnan, Vignesh Narayanan, Jagannathan Sarangapani:
Cooperative Deep Q-learning Framework for Environments Providing Image Feedback. CoRR abs/2110.15305 (2021)
Coauthor Index
aka: Jagannathan Sarangapani
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