( en | de | ru )

 

Disambiguation of "Raghavan, Krishnan"

Author of the publication

copydeleteadd this publication to your clipboard
Self-supervised Learning for Anomaly Detection in Computational Workflows.

, , , , , , and . CoRR, (2023)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Raghavan Vallur

Krishnan Sircar

Ajay Krishnan

Venkata Krishnan

Ajay Krishnan

 

Other publications of authors with the same name

Forward Gradients for Data-Driven CFD Wall Modeling., , , and . CoRR, (2023)Hierarchical Mahalanobis Distance Clustering Based Technique for Prognostics in Applications Generating Big Data., and . SSCI, page 516-521. IEEE, (2015)Direct Error Driven Learning for Classification in Applications Generating Big-Data., , and . Development and Analysis of Deep Learning Architectures, volume 867 of Studies in Computational Intelligence, Springer, (2020)Distributed Learning of Deep Sparse Neural Networks for High-dimensional Classification., , , and . IEEE BigData, page 1587-1592. IEEE, (2018)Self-supervised Learning for Anomaly Detection in Computational Workflows., , , , , , and . CoRR, (2023)Automated Continual Learning of Defect Identification in Coherent Diffraction Imaging., , , , , , , and . AI4S, page 1-6. IEEE, (2022)Direct Error Driven Learning for Deep Neural Networks with Applications to Bigdata., , and . INNS Conference on Big Data, volume 144 of Procedia Computer Science, page 89-95. Elsevier, (2018)Deep learning inspired prognostics scheme for applications generating big data., , and . IJCNN, page 3296-3302. IEEE, (2017)A Multi-Step Nonlinear Dimension-Reduction Approach with Applications to Big Data., , and . IEEE Trans. Knowl. Data Eng., 31 (12): 2249-2261 (2019)Workflow Anomaly Detection with Graph Neural Networks., , , , , , , , , and . WORKS@SC, page 35-42. IEEE, (2022)