Krishnan Raghavan | DeepAI

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∙ 06/16/2023

Flow-Bench: A Dataset for Computational Workflow Anomaly Detection

A computational workflow, also known as workflow, consists of tasks that...

0 George Papadimitriou, et al. ∙

∙ 05/20/2023

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

Due to the curse of dimensionality, it is often prohibitively expensive ...

0 Manisha Garg, et al. ∙

∙ 05/19/2023

Learning Continually on a Sequence of Graphs – The Dynamical System Way

Continual learning (CL) is a field concerned with learning a series of i...

0 Krishnan Raghavan, et al. ∙

∙ 02/20/2023

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

Classical problems in computational physics such as data-driven forecast...

0 Romit Maulik, et al. ∙

∙ 10/28/2021

Cooperative Deep Q-learning Framework for Environments Providing Image Feedback

In this paper, we address two key challenges in deep reinforcement learn...

0 Krishnan Raghavan, et al. ∙

∙ 10/28/2021

Learning to Control using Image Feedback

Learning to control complex systems using non-traditional feedback, e.g....

0 Krishnan Raghavan, et al. ∙

∙ 10/26/2021

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification

Deep neural networks are powerful predictors for a variety of tasks. How...

0 Romain Egele, et al. ∙

∙ 09/28/2021

Formalizing the Generalization-Forgetting Trade-off in Continual Learning

We formulate the continual learning (CL) problem via dynamic programming...

0 Krishnan Raghavan, et al. ∙

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