Jayaraman J. Thiagarajan

ORCID

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 186

Top Venues

CoRR

ICASSP

AAAI

IEEE Trans. Neural Networks Learn. Syst.

Arizona State University, Tempe, USA (PHD)

Education History

Arizona State University, Tempe, USA

PHD

Publications

Rakshith Subramanyam, T. S. Jayram, Rushil Anirudh, Jayaraman J. Thiagarajan
Exploring the Utility of Clip Priors for Visual Relationship Prediction. ICASSP (2024)
Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. ICLR (2024)
Yang Liu, Kowshik Thopalli, Jayaraman J. Thiagarajan
Speeding Up Image Classifiers with Little Companions. CoRR (2024)
Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. CoRR (2024)
Vivek Sivaraman Narayanaswamy, Rushil Anirudh, Jayaraman J. Thiagarajan
The Double-Edged Sword Of Ai Safety: Balancing Anomaly Detection and OOD Generalization Via Model Anchoring. ICASSP (2024)
Joshua Feinglass, Jayaraman J. Thiagarajan, Rushil Anirudh, T. S. Jayram, Yezhou Yang
'Eyes of a Hawk and Ears of a Fox': Part Prototype Network for Generalized Zero-Shot Learning. CoRR (2024)
Vivek Sivaraman Narayanaswamy, Kowshik Thopalli, Rushil Anirudh, Yamen Mubarka, Wesam Sakla, Jayaraman J. Thiagarajan
On the Use of Anchoring for Training Vision Models. CoRR (2024)
Hongjun Choi, Jayaraman J. Thiagarajan, Ruben Glatt, Shusen Liu
Enhancing Accuracy and Parameter-Efficiency of Neural Representations for Network Parameterization. CoRR (2024)
Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan
On Estimating Link Prediction Uncertainty Using Stochastic Centering. ICASSP (2024)
Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan
A Closer Look at Scoring Functions and Generalization Prediction. CoRR (2023)
Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan
A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias. ICLR (2023)
Jiaming Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Stewart He, K. Aditya Mohan, Ulugbek S. Kamilov, Hyojin Kim
DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction. ICCV (2023)
S. Devi, Kowshik Thopalli, R. Dayana, P. Malarvezhi, Jayaraman J. Thiagarajan
Improving Object Detectors by Exploiting Bounding Boxes for Augmentation Design. IEEE Access 11 (2023)
Vivek Sivaraman Narayanaswamy, Yamen Mubarka, Rushil Anirudh, Deepta Rajan, Jayaraman J. Thiagarajan
Exploring Inlier and Outlier Specification for Improved Medical OOD Detection. ICCV (Workshops) (2023)
Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. CoRR (2023)
Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan
A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias. CoRR (2023)
Vivek Sivaraman Narayanaswamy, Yamen Mubarka, Rushil Anirudh, Deepta Rajan, Andreas Spanias, Jayaraman J. Thiagarajan
Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors. MIDL (2023)
Kowshik Thopalli, Rakshith Subramanyam, Pavan K. Turaga, Jayaraman J. Thiagarajan
Target-Aware Generative Augmentations for Single-Shot Adaptation. CoRR (2023)
Kowshik Thopalli, Rakshith Subramanyam, Pavan K. Turaga, Jayaraman J. Thiagarajan
Target-Aware Generative Augmentations for Single-Shot Adaptation. ICML (2023)
Rakshith Subramanyam, Mark Heimann, T. S. Jayram, Rushil Anirudh, Jayaraman J. Thiagarajan
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification. WACV (2023)
Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan
A Closer Look At Scoring Functions And Generalization Prediction. ICASSP (2023)
Tejas Gokhale, Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Chitta Baral, Yezhou Yang
Improving Diversity with Adversarially Learned Transformations for Domain Generalization. WACV (2023)
Matthew L. Olson, Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Weng-Keen Wong
Cross-GAN Auditing: Unsupervised Identification of Attribute Level Similarities and Differences Between Pretrained Generative Models. CVPR (2023)
Matthew L. Olson, Shusen Liu, Jayaraman J. Thiagarajan, Bogdan Kustowski, Weng-Keen Wong, Rushil Anirudh
Transformer-Powered Surrogates Close the ICF Simulation-Experiment Gap with Extremely Limited Data. CoRR (2023)
Alexandros Karargyris, Renato Umeton, Micah J. Sheller, Alejandro Aristizabal, Johnu George, Anna Wuest, Sarthak Pati, Hasan Kassem, Maximilian Zenk, Ujjwal Baid, Prakash Narayana Moorthy, Alexander Chowdhury, Junyi Guo, Sahil S. Nalawade, Jacob Rosenthal, David Kanter, Maria Xenochristou, Daniel J. Beutel, Verena Chung, Timothy Bergquist, James Eddy, Abubakar Abid, Lewis Tunstall, Omar Sanseviero, Dimitrios Dimitriadis, Yiming Qian, Xinxing Xu, Yong Liu, Rick Siow Mong Goh, Srini Bala, Victor Bittorf, Sreekar Reddy Puchala, Biagio Ricciuti, Soujanya Samineni, Eshna Sengupta, Akshay Chaudhari, Cody Coleman, Bala Desinghu, Gregory Diamos, Debo Dutta, Diane Feddema, Grigori Fursin, Xinyuan Huang, Satyananda Kashyap, Nicholas Lane, Indranil Mallick, Pietro Mascagni, Virendra Mehta, Cassiano Ferro Moraes, Vivek Natarajan, Nikola Nikolov, Nicolas Padoy, Gennady Pekhimenko, Vijay Janapa Reddi, G. Anthony Reina, Pablo Ribalta, Abhishek Singh, Jayaraman J. Thiagarajan, Jacob Albrecht, Thomas Wolf, Geralyn Miller, Huazhu Fu, Prashant Shah, Daguang Xu, Poonam Yadav, David Talby, Mark M. Awad, Jeremy P. Howard, Michael Rosenthal, Luigi Marchionni, Massimo Loda, Jason M. Johnson, Spyridon Bakas, Peter Mattson
Federated benchmarking of medical artificial intelligence with MedPerf. Nat. Mac. Intell. 5 (7) (2023)
Rakshith Subramanyam, T. S. Jayram, Rushil Anirudh, Jayaraman J. Thiagarajan
CREPE: Learnable Prompting With CLIP Improves Visual Relationship Prediction. CoRR (2023)
Jayaraman J. Thiagarajan, Vivek Sivaraman Narayanaswamy, Puja Trivedi, Rushil Anirudh
PAGER: A Framework for Failure Analysis of Deep Regression Models. CoRR (2023)
Matthew L. Olson, Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Weng-Keen Wong
Cross-GAN Auditing: Unsupervised Identification of Attribute Level Similarities and Differences between Pretrained Generative Models. CoRR (2023)
Rakshith Subramanyam, Kowshik Thopalli, Spring Berman, Pavan K. Turaga, Jayaraman J. Thiagarajan
Single-Shot Domain Adaptation via Target-Aware Generative Augmentations. ICASSP (2023)
Kowshik Thopalli, Rushil Anirudh, Pavan K. Turaga, Jayaraman J. Thiagarajan
The Surprising Effectiveness of Deep Orthogonal Procrustes Alignment in Unsupervised Domain Adaptation. IEEE Access 11 (2023)
Kowshik Thopalli, Devi S, Jayaraman J. Thiagarajan
InterAug: A Tuning-Free Augmentation Policy for Data-Efficient and Robust Object Detection. ICCV (Workshops) (2023)
Vivek Sivaraman Narayanaswamy, Yamen Mubarka, Rushil Anirudh, Deepta Rajan, Andreas Spanias, Jayaraman J. Thiagarajan
Revisiting Inlier and Outlier Specification for Improved Out-of-Distribution Detection. CoRR (2022)
Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan
Exploring the Design of Adaptation Protocols for Improved Generalization and Machine Learning Safety. CoRR (2022)
Rushil Anirudh, Jayaraman J. Thiagarajan
Out of Distribution Detection via Neural Network Anchoring. CoRR (2022)
Jayaraman J. Thiagarajan, Rushil Anirudh, Vivek Sivaraman Narayanaswamy, Peer-Timo Bremer
Single Model Uncertainty Estimation via Stochastic Data Centering. CoRR (2022)
Rakshith Subramanyam, Kowshik Thopalli, Spring Berman, Pavan K. Turaga, Jayaraman J. Thiagarajan
Single-Shot Domain Adaptation via Target-Aware Generative Augmentation. CoRR (2022)
Kowshik Thopalli, Jayaraman J. Thiagarajan, Rushil Anirudh, Pavan K. Turaga
Revisiting Deep Subspace Alignment for Unsupervised Domain Adaptation. CoRR (2022)
Vivek Sivaraman Narayanaswamy, Rushil Anirudh, Irene Kim, Yamen Mubarka, Andreas Spanias, Jayaraman J. Thiagarajan
Predicting the Generalization Gap in Deep Models using Anchoring. ICASSP (2022)
Yuzhe Lu, Shusen Liu, Jayaraman J. Thiagarajan, Wesam Sakla, Rushil Anirudh
On-the-fly Object Detection using StyleGAN with CLIP Guidance. CoRR (2022)
Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann, Danai Koutra, Jayaraman J. Thiagarajan
Analyzing Data-Centric Properties for Graph Contrastive Learning. NeurIPS (2022)
Jayaraman J. Thiagarajan, Rushil Anirudh, Peer-Timo Bremer, Timothy C. Germann, Sara Y. Del Valle, Frederick H. Streitz
Machine Learning-Powered Mitigation Policy Optimization in Epidemiological Models. Healthcare AI and COVID-19 Workshop (2022)
Rakshith Subramanyam, Mark Heimann, Jayram S. Thathachar, Rushil Anirudh, Jayaraman J. Thiagarajan
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification. CoRR (2022)
Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer
Sparsity Improves Unsupervised Attribute Discovery in Stylegan. ICASSP (2022)
Prasanna Sattigeri, Jayaraman J. Thiagarajan, Karthikeyan Ramamurthy, Andreas Spanias, Mahesh K. Banavar, Abhinav Dixit, Jie Fan, Mohit Malu, Kristen Jaskie, Sunil Rao, Uday Shankar Shanthamallu, Vivek Sivaraman Narayanaswamy, Sameeksha Katoch
Instruction Tools for Signal Processing and Machine Learning for Ion-Channel Sensors. Int. J. Virtual Pers. Learn. Environ. 12 (1) (2022)
Jiaming Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Stewart He, K. Aditya Mohan, Ulugbek S. Kamilov, Hyojin Kim
DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction. CoRR (2022)
Harsh Bhatia, Jayaraman J. Thiagarajan, Rushil Anirudh, T. S. Jayram, Tomas Oppelstrup, Helgi I. Ingólfsson, Felice C. Lightstone, Peer-Timo Bremer
A biology-informed similarity metric for simulated patches of human cell membrane. Mach. Learn. Sci. Technol. 3 (3) (2022)
Jayaraman J. Thiagarajan, Rushil Anirudh, Vivek Sivaraman Narayanaswamy, Timo Bremer
Single Model Uncertainty Estimation via Stochastic Data Centering. NeurIPS (2022)
Tejas Gokhale, Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Chitta Baral, Yezhou Yang
Improving Diversity with Adversarially Learned Transformations for Domain Generalization. CoRR (2022)
Kowshik Thopalli, Jayaraman J. Thiagarajan
Improving Single-Stage Object Detectors for Nighttime Pedestrian Detection. Int. J. Pattern Recognit. Artif. Intell. 36 (9) (2022)
Kowshik Thopalli, Pavan K. Turaga, Jayaraman J. Thiagarajan
Domain Alignment Meets Fully Test-Time Adaptation. CoRR (2022)
Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann, Danai Koutra, Jayaraman J. Thiagarajan
Analyzing Data-Centric Properties for Contrastive Learning on Graphs. CoRR (2022)
Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Timothy C. Germann, Sara Y. Del Valle, Frederick H. Streitz
Accurate Calibration of Agent-based Epidemiological Models with Neural Network Surrogates. Healthcare AI and COVID-19 Workshop (2022)
Rakshith Subramanyam, Vivek Sivaraman Narayanaswamy, Mark Naufel, Andreas Spanias, Jayaraman J. Thiagarajan
Improved StyleGAN-v2 based Inversion for Out-of-Distribution Images. ICML (2022)
Kowshik Thopalli, Pavan K. Turaga, Jayaraman J. Thiagarajan
Domain Alignment Meets Fully Test-Time Adaptation. ACML (2022)
Bogdan Kustowski, Jim A. Gaffney, Brian K. Spears, Gemma J. Anderson, Rushil Anirudh, Peer-Timo Bremer, Jayaraman J. Thiagarajan, Michael K. G. Kruse, Ryan Nora
Suppressing simulation bias in multi-modal data using transfer learning. Mach. Learn. Sci. Technol. 3 (1) (2022)
Rushil Anirudh, Jayaraman J. Thiagarajan
Out of Distribution Detection via Neural Network Anchoring. ACML (2022)
J. Luc Peterson, Benjamin Bay, Joe Koning, Peter B. Robinson, Jessica Semler, Jeremy White, Rushil Anirudh, Kevin Athey, Peer-Timo Bremer, Francesco Di Natale, David Fox, Jim A. Gaffney, Sam Ade Jacobs, Bhavya Kailkhura, Bogdan Kustowski, Steve H. Langer, Brian K. Spears, Jayaraman J. Thiagarajan, Brian Van Essen, Jae-Seung Yeom
Enabling machine learning-ready HPC ensembles with Merlin. Future Gener. Comput. Syst. 131 (2022)
Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Andreas Spanias
On the Design of Deep Priors for Unsupervised Audio Restoration. Interspeech (2021)
Jayaraman J. Thiagarajan, Vivek Sivaraman Narayanaswamy, Rushil Anirudh, Peer-Timo Bremer, Andreas Spanias
Accurate and Robust Feature Importance Estimation under Distribution Shifts. AAAI (2021)
Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Deepta Rajan, Andreas Spanias
Loss Estimators Improve Model Generalization. CoRR (2021)
Kowshik Thopalli, Sameeksha Katoch, Andreas Spanias, Pavan K. Turaga, Jayaraman J. Thiagarajan
Improving Multi-Domain Generalization through Domain Re-labeling. CoRR (2021)
Uday Shankar Shanthamallu, Jayaraman J. Thiagarajan, Andreas Spanias
Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks. AAAI (2021)
Bogdan Kustowski, Jim A. Gaffney, Brian K. Spears, Gemma J. Anderson, Rushil Anirudh, Peer-Timo Bremer, Jayaraman J. Thiagarajan
Transfer learning suppresses simulation bias in predictive models built from sparse, multi-modal data. CoRR (2021)
Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Andreas Spanias
On the Design of Deep Priors for Unsupervised Audio Restoration. CoRR (2021)
Tanzima Z. Islam, Philip Wu Liang, Forest Sweeney, Cody Pranger, Jayaraman J. Thiagarajan, Moushumi Sharmin, Shameem Ahmed
College Life is Hard! - Shedding Light on Stress Prediction for Autistic College Students using Data-Driven Analysis. COMPSAC (2021)
Jayaraman J. Thiagarajan, Vivek Sivaraman Narayanaswamy, Deepta Rajan, Jia Liang, Akshay Chaudhari, Andreas Spanias
Designing Counterfactual Generators using Deep Model Inversion. NeurIPS (2021)
Deepta Rajan, Jayaraman J. Thiagarajan, Alexandros Karargyris, Satyananda Kashyap
Self-training with improved regularization for sample-efficient chest x-ray classification. Medical Imaging: Computer-Aided Diagnosis (2021)
Gowtham Muniraju, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Cihan Tepedelenlioglu, Andreas Spanias
Coverage-Based Designs Improve Sample Mining and Hyperparameter Optimization. IEEE Trans. Neural Networks Learn. Syst. 32 (3) (2021)
Hoseung Song, Jayaraman J. Thiagarajan, Bhavya Kailkhura
Preventing Failures by Dataset Shift Detection in Safety-Critical Graph Applications. Frontiers Artif. Intell. 4 (2021)
Tejas Gokhale, Rushil Anirudh, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Chitta Baral, Yezhou Yang
Attribute-Guided Adversarial Training for Robustness to Natural Perturbations. AAAI (2021)
Tarek Ramadan, Tanzima Z. Islam, Chase Phelps, Nathan Pinnow, Jayaraman J. Thiagarajan
Comparative Code Structure Analysis using Deep Learning for Performance Prediction. ISPASS (2021)
Rushil Anirudh, Jayaraman J. Thiagarajan
Δ-UQ: Accurate Uncertainty Quantification via Anchor Marginalization. CoRR (2021)
Sunil Rao, Vivek Sivaraman Narayanaswamy, Michael Esposito, Jayaraman J. Thiagarajan, Andreas Spanias
Deep Learning with hyper-parameter tuning for COVID-19 Cough Detection. IISA (2021)
Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Andreas Spanias
Using Deep Image Priors to Generate Counterfactual Explanations. ICASSP (2021)
Rushil Anirudh, Jayaraman J. Thiagarajan, Rahul Sridhar, Peer-Timo Bremer
MARGIN: Uncovering Deep Neural Networks Using Graph Signal Analysis. Frontiers Big Data 4 (2021)
Sunil Rao, Vivek Sivaraman Narayanaswamy, Michael Esposito, Jayaraman J. Thiagarajan, Andreas Spanias
COVID-19 detection using cough sound analysis and deep learning algorithms. Intell. Decis. Technol. 15 (4) (2021)
Alexandros Karargyris, Renato Umeton, Micah J. Sheller, Alejandro Aristizabal, Johnu George, Srini Bala, Daniel J. Beutel, Victor Bittorf, Akshay Chaudhari, Alexander Chowdhury, Cody Coleman, Bala Desinghu, Gregory F. Diamos, Debo Dutta, Diane Feddema, Grigori Fursin, Junyi Guo, Xinyuan Huang, David Kanter, Satyananda Kashyap, Nicholas D. Lane, Indranil Mallick, Pietro Mascagni, Virendra Mehta, Vivek Natarajan, Nikola Nikolov, Nicolas Padoy, Gennady Pekhimenko, Vijay Janapa Reddi, G. Anthony Reina, Pablo Ribalta, Jacob Rosenthal, Abhishek Singh, Jayaraman J. Thiagarajan, Anna Wuest, Maria Xenochristou, Daguang Xu, Poonam Yadav, Michael Rosenthal, Massimo Loda, Jason M. Johnson, Peter Mattson
MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation. CoRR (2021)
Jayaraman J. Thiagarajan, Vivek Sivaraman Narayanaswamy, Deepta Rajan, Jason Liang, Akshay Chaudhari, Andreas Spanias
Designing Counterfactual Generators using Deep Model Inversion. CoRR (2021)
Ankita Shukla, Rushil Anirudh, Eugene Kur, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Brian K. Spears, Tammy Ma, Pavan K. Turaga
Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion. CoRR (2021)
Nathan Pinnow, Tarek Ramadan, Tanzima Z. Islam, Chase Phelps, Jayaraman J. Thiagarajan
Comparative Code Structure Analysis using Deep Learning for Performance Prediction. CoRR (2021)
Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Peer-Timo Bremer
MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking. Int. J. Comput. Vis. 128 (10) (2020)
Deepta Rajan, Jayaraman J. Thiagarajan, Alexandros Karargyris, Satyananda Kashyap
Self-Training with Improved Regularization for Few-Shot Chest X-Ray Classification. CoRR (2020)
Uday Shankar Shanthamallu, Jayaraman J. Thiagarajan, Huan Song, Andreas Spanias
GrAMME: Semisupervised Learning Using Multilayered Graph Attention Models. IEEE Trans. Neural Networks Learn. Syst. 31 (10) (2020)
Bhavya Kailkhura, Jayaraman J. Thiagarajan, Qunwei Li, Jize Zhang, Yi Zhou, Timo Bremer
A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning. NeurIPS (2020)
Abhinav Bhatele, Jayaraman J. Thiagarajan, Taylor Groves, Rushil Anirudh, Staci A. Smith, Brandon Cook, David K. Lowenthal
The Case of Performance Variability on Dragonfly-based Systems. IPDPS (2020)
Bindya Venkatesh, Jayaraman J. Thiagarajan
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification. CoRR (2020)
Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer
Uncovering interpretable relationships in high-dimensional scientific data through function preserving projections. Mach. Learn. Sci. Technol. 1 (4) (2020)
Jayaraman J. Thiagarajan, Bindya Venkatesh, Deepta Rajan, Prasanna Sattigeri
Improving Reliability of Clinical Models Using Prediction Calibration. UNSURE/GRAIL@MICCAI (2020)
Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Andreas Spanias
Using Deep Image Priors to Generate Counterfactual Explanations. CoRR (2020)
Gemma J. Anderson, Jim A. Gaffney, Brian K. Spears, Peer-Timo Bremer, Rushil Anirudh, Jayaraman J. Thiagarajan
Meaningful uncertainties from deep neural network surrogates of large-scale numerical simulations. CoRR (2020)
Tejas Gokhale, Rushil Anirudh, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Chitta Baral, Yezhou Yang
Attribute-Guided Adversarial Training for Robustness to Natural Perturbations. CoRR (2020)
Uday Shankar Shanthamallu, Jayaraman J. Thiagarajan, Andreas Spanias
Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks. CoRR (2020)
Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Timothy C. Germann, Sara Y. Del Valle, Frederick H. Streitz
Accurate Calibration of Agent-based Epidemiological Models with Neural Network Surrogates. CoRR (2020)
Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Rushil Anirudh, Andreas Spanias
Unsupervised Audio Source Separation using Generative Priors. CoRR (2020)
Jayaraman J. Thiagarajan, Bindya Venkatesh, Rushil Anirudh, Peer-Timo Bremer, Jim Gaffney, Gemma Anderson, Brian K. Spears
Designing Accurate Emulators for Scientific Processes using Calibration-Driven Deep Models. CoRR (2020)
Bindya Venkatesh, Jayaraman J. Thiagarajan, Kowshik Thopalli, Prasanna Sattigeri
Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration. CoRR (2020)
Jayaraman J. Thiagarajan, Bindya Venkatesh, Deepta Rajan
Learn-By-Calibrating: Using Calibration As A Training Objective. ICASSP (2020)
Shusen Liu, Jim Gaffney, J. Luc Peterson, Peter B. Robinson, Harsh Bhatia, Valerio Pascucci, Brian K. Spears, Peer-Timo Bremer, Di Wang, Dan Maljovec, Rushil Anirudh, Jayaraman J. Thiagarajan, Sam Ade Jacobs, Brian C. Van Essen, David Hysom, Jae-Seung Yeom
Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications. IEEE Trans. Vis. Comput. Graph. 26 (1) (2020)
Jayaraman J. Thiagarajan, Peer-Timo Bremer, Rushil Anirudh, Timothy C. Germann, Sara Y. Del Valle, Frederick H. Streitz
Machine Learning-Powered Mitigation Policy Optimization in Epidemiological Models. CoRR (2020)
Jayaraman J. Thiagarajan, Prasanna Sattigeri, Deepta Rajan, Bindya Venkatesh
Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models. CoRR (2020)

Jayaraman J. Thiagarajan.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Jayaraman J. Thiagarajan