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Jayaraman J. Thiagarajan

Lawrence Livermore National Laboratory, Livermore, CA

Transfer learning suppresses simulation bias in predictive models built from sparse, multi-modal data


Apr 19, 2021
Bogdan Kustowski, Jim A. Gaffney, Brian K. Spears, Gemma J. Anderson, Rushil Anirudh, Peer-Timo Bremer, Jayaraman J. Thiagarajan

* 13 pages, 12 figures 

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On the Design of Deep Priors for Unsupervised Audio Restoration


Apr 14, 2021
Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Andreas Spanias


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Loss Estimators Improve Model Generalization


Mar 05, 2021
Vivek Narayanaswamy, Jayaraman J. Thiagarajan, Deepta Rajan, Andreas Spanias


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Comparative Code Structure Analysis using Deep Learning for Performance Prediction


Feb 12, 2021
Nathan Pinnow, Tarek Ramadan, Tanzima Z. Islam, Chase Phelps, Jayaraman J. Thiagarajan

* 11 pages, The paper has been accepted for publication on International Symposium on Performance Analysis of Systems and Software (ISPASS) 2020 conference 

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Attribute-Guided Adversarial Training for Robustness to Natural Perturbations


Dec 03, 2020
Tejas Gokhale, Rushil Anirudh, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Chitta Baral, Yezhou Yang

* Accepted to AAAI 2021. Preprint 

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Meaningful uncertainties from deep neural network surrogates of large-scale numerical simulations


Oct 26, 2020
Gemma J. Anderson, Jim A. Gaffney, Brian K. Spears, Peer-Timo Bremer, Rushil Anirudh, Jayaraman J. Thiagarajan


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Using Deep Image Priors to Generate Counterfactual Explanations


Oct 22, 2020
Vivek Narayanaswamy, Jayaraman J. Thiagarajan, Andreas Spanias


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Machine Learning-Powered Mitigation Policy Optimization in Epidemiological Models


Oct 16, 2020
Jayaraman J. Thiagarajan, Peer-Timo Bremer, Rushil Anirudh, Timothy C. Germann, Sara Y. Del Valle, Frederick H. Streitz


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Accurate Calibration of Agent-based Epidemiological Models with Neural Network Surrogates


Oct 13, 2020
Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Timothy C. Germann, Sara Y. Del Valle, Frederick H. Streitz


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Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks


Sep 30, 2020
Uday Shankar Shanthamallu, Jayaraman J. Thiagarajan, Andreas Spanias


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Accurate and Robust Feature Importance Estimation under Distribution Shifts


Sep 30, 2020
Jayaraman J. Thiagarajan, Vivek Narayanaswamy, Rushil Anirudh, Peer-Timo Bremer, Andreas Spanias


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Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification


Sep 30, 2020
Bindya Venkatesh, Jayaraman J. Thiagarajan


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Unsupervised Audio Source Separation using Generative Priors


May 28, 2020
Vivek Narayanaswamy, Jayaraman J. Thiagarajan, Rushil Anirudh, Andreas Spanias

* 5 pages, 2 figures 

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Designing Accurate Emulators for Scientific Processes using Calibration-Driven Deep Models


May 05, 2020
Jayaraman J. Thiagarajan, Bindya Venkatesh, Rushil Anirudh, Peer-Timo Bremer, Jim Gaffney, Gemma Anderson, Brian Spears


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Self-Training with Improved Regularization for Few-Shot Chest X-Ray Classification


May 03, 2020
Deepta Rajan, Jayaraman J. Thiagarajan, Alexandros Karargyris, Satyananda Kashyap


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Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models


Apr 27, 2020
Jayaraman J. Thiagarajan, Prasanna Sattigeri, Deepta Rajan, Bindya Venkatesh


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Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration


Feb 11, 2020
Bindya Venkatesh, Jayaraman J. Thiagarajan, Kowshik Thopalli, Prasanna Sattigeri


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MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking


Feb 07, 2020
Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Timo Bremer

* Minor revision at the International Journal on Computer Vision's (IJCV) special issue on GANs (2020) 

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Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies


Dec 17, 2019
Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Brian K. Spears

* 10 pages, 6 figures 

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Invenio: Discovering Hidden Relationships Between Tasks/Domains Using Structured Meta Learning


Nov 24, 2019
Sameeksha Katoch, Kowshik Thopalli, Jayaraman J. Thiagarajan, Pavan Turaga, Andreas Spanias

* Semantic structure development for tasks/domains essential for efficient knowledge transfer 

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Heteroscedastic Calibration of Uncertainty Estimators in Deep Learning


Oct 30, 2019
Bindya Venkatesh, Jayaraman J. Thiagarajan


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Learn-By-Calibrating: Using Calibration as a Training Objective


Oct 30, 2019
Jayaraman J. Thiagarajan, Bindya Venkatesh, Deepta Rajan


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Improving Limited Angle CT Reconstruction with a Robust GAN Prior


Oct 14, 2019
Rushil Anirudh, Hyojin Kim, Jayaraman J. Thiagarajan, K. Aditya Mohan, Kyle M. Champley

* Deep Inverse NeurIPS 2019 Workshop 

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Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion


Oct 03, 2019
Rushil Anirudh, Jayaraman J. Thiagarajan, Shusen Liu, Peer-Timo Bremer, Brian K. Spears

* Machine Learning for Physical Sciences Workshop at NeurIPS 2019 

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Function Preserving Projection for Scalable Exploration of High-Dimensional Data


Sep 25, 2019
Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer


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Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors


Sep 09, 2019
Jayaraman J. Thiagarajan, Bindya Venkatesh, Prasanna Sattigeri, Peer-Timo Bremer


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Distill-to-Label: Weakly Supervised Instance Labeling Using Knowledge Distillation


Jul 26, 2019
Jayaraman J. Thiagarajan, Satyananda Kashyap, Alexandros Karagyris


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Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications


Jul 19, 2019
Shusen Liu, Di Wang, Dan Maljovec, Rushil Anirudh, Jayaraman J. Thiagarajan, Sam Ade Jacobs, Brian C. Van Essen, David Hysom, Jae-Seung Yeom, Jim Gaffney, Luc Peterson, Peter B. Robinson, Harsh Bhatia, Valerio Pascucci, Brian K. Spears, Peer-Timo Bremer


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