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2020 – today
- 2024
[c102]Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan:
On Estimating Link Prediction Uncertainty Using Stochastic Centering. ICASSP 2024: 6810-6814- [c101]Rakshith Subramanyam, T. S. Jayram, Rushil Anirudh, Jayaraman J. Thiagarajan:
Exploring the Utility of Clip Priors for Visual Relationship Prediction. ICASSP 2024: 6825-6829 - [c100]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: 7235-7239 - [c99]Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan:
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. ICLR 2024 - [c98]Jayaraman J. Thiagarajan, Vivek Sivaraman Narayanaswamy, Puja Trivedi, Rushil Anirudh:
PAGER: Accurate Failure Characterization in Deep Regression Models. ICML 2024 - [i98]Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan:
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. CoRR abs/2401.03350 (2024) - [i97]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 abs/2404.08761 (2024) - [i96]Vivek Sivaraman Narayanaswamy, Kowshik Thopalli, Rushil Anirudh, Yamen Mubarka, Wesam Sakla, Jayaraman J. Thiagarajan:
On the Use of Anchoring for Training Vision Models. CoRR abs/2406.00529 (2024) - [i95]Yang Liu, Kowshik Thopalli, Jayaraman J. Thiagarajan:
Speeding Up Image Classifiers with Little Companions. CoRR abs/2406.17117 (2024) - [i94]Hongjun Choi, Jayaraman J. Thiagarajan, Ruben Glatt, Shusen Liu:
Enhancing Accuracy and Parameter-Efficiency of Neural Representations for Network Parameterization. CoRR abs/2407.00356 (2024) - [i93]Rakshith Subramanyam, Kowshik Thopalli, Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan:
DECIDER: Leveraging Foundation Model Priors for Improved Model Failure Detection and Explanation. CoRR abs/2408.00331 (2024) - 2023
- [j31]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: 12858-12869 (2023) - [j30]S. Devi, Kowshik Thopalli, R. Dayana, P. Malarvezhi, Jayaraman J. Thiagarajan:
Improving Object Detectors by Exploiting Bounding Boxes for Augmentation Design. IEEE Access 11: 108356-108364 (2023) - [j29]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 A. 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 F. Diamos, Debo Dutta, Diane Feddema, Grigori Fursin, Xinyuan Huang, Satyananda Kashyap, Nicholas D. 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): 799-810 (2023) - [c97]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: 7981-7990 - [c96]Rakshith Subramanyam, Kowshik Thopalli, Spring Berman, Pavan K. Turaga, Jayaraman J. Thiagarajan:
Single-Shot Domain Adaptation via Target-Aware Generative Augmentations. ICASSP 2023: 1-5 - [c95]Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan:
A Closer Look At Scoring Functions And Generalization Prediction. ICASSP 2023: 1-5 - [c94]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: 10464-10474 - [c93]Kowshik Thopalli, Devi S, Jayaraman J. Thiagarajan:
InterAug: A Tuning-Free Augmentation Policy for Data-Efficient and Robust Object Detection. ICCV (Workshops) 2023: 253-261 - [c92]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: 4591-4600 - [c91]Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan:
A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias. ICLR 2023 - [c90]Kowshik Thopalli, Rakshith Subramanyam, Pavan K. Turaga, Jayaraman J. Thiagarajan:
Target-Aware Generative Augmentations for Single-Shot Adaptation. ICML 2023: 34105-34119 - [c89]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: 190-211 - [c88]Tejas Gokhale, Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Chitta Baral, Yezhou Yang:
Improving Diversity with Adversarially Learned Transformations for Domain Generalization. WACV 2023: 434-443 - [c87]Rakshith Subramanyam, Mark Heimann, T. S. Jayram, Rushil Anirudh, Jayaraman J. Thiagarajan:
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification. WACV 2023: 2478-2486 - [i92]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 abs/2303.10774 (2023) - [i91]Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan:
A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias. CoRR abs/2303.13500 (2023) - [i90]Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan:
A Closer Look at Scoring Functions and Generalization Prediction. CoRR abs/2303.13589 (2023) - [i89]Kowshik Thopalli, Rakshith Subramanyam, Pavan K. Turaga, Jayaraman J. Thiagarajan:
Target-Aware Generative Augmentations for Single-Shot Adaptation. CoRR abs/2305.13284 (2023) - [i88]Rakshith Subramanyam, T. S. Jayram, Rushil Anirudh, Jayaraman J. Thiagarajan:
CREPE: Learnable Prompting With CLIP Improves Visual Relationship Prediction. CoRR abs/2307.04838 (2023) - [i87]Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan:
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. CoRR abs/2309.10976 (2023) - [i86]Jayaraman J. Thiagarajan, Vivek Sivaraman Narayanaswamy, Puja Trivedi, Rushil Anirudh:
PAGER: A Framework for Failure Analysis of Deep Regression Models. CoRR abs/2309.10977 (2023) - [i85]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 abs/2312.03642 (2023) - 2022
- [j28]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: 255-268 (2022) - [j27]Kowshik Thopalli, Jayaraman J. Thiagarajan:
Improving Single-Stage Object Detectors for Nighttime Pedestrian Detection. Int. J. Pattern Recognit. Artif. Intell. 36(9): 2250034:1-2250034:23 (2022) - [j26]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): 1-17 (2022) - [j25]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): 15035 (2022) - [j24]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): 35010 (2022) - [c86]Rushil Anirudh, Jayaraman J. Thiagarajan:
Out of Distribution Detection via Neural Network Anchoring. ACML 2022: 32-47 - [c85]Kowshik Thopalli, Pavan K. Turaga, Jayaraman J. Thiagarajan:
Domain Alignment Meets Fully Test-Time Adaptation. ACML 2022: 1006-1021 - [c84]Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer:
Sparsity Improves Unsupervised Attribute Discovery in Stylegan. ICASSP 2022: 3388-3392 - [c83]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: 4393-4397 - [c82]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: 54-62 - [c81]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: 63-72 - [c80]Rakshith Subramanyam, Vivek Sivaraman Narayanaswamy, Mark Naufel, Andreas Spanias, Jayaraman J. Thiagarajan:
Improved StyleGAN-v2 based Inversion for Out-of-Distribution Images. ICML 2022: 20625-20639 - [c79]Jayaraman J. Thiagarajan, Rushil Anirudh, Vivek Sivaraman Narayanaswamy, Timo Bremer:
Single Model Uncertainty Estimation via Stochastic Data Centering. NeurIPS 2022 - [c78]Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann, Danai Koutra, Jayaraman J. Thiagarajan:
Analyzing Data-Centric Properties for Graph Contrastive Learning. NeurIPS 2022 - [i84]Kowshik Thopalli, Jayaraman J. Thiagarajan, Rushil Anirudh, Pavan K. Turaga:
Revisiting Deep Subspace Alignment for Unsupervised Domain Adaptation. CoRR abs/2201.01806 (2022) - [i83]Tejas Gokhale, Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Chitta Baral, Yezhou Yang:
Improving Diversity with Adversarially Learned Transformations for Domain Generalization. CoRR abs/2206.07736 (2022) - [i82]Rushil Anirudh, Jayaraman J. Thiagarajan:
Out of Distribution Detection via Neural Network Anchoring. CoRR abs/2207.04125 (2022) - [i81]Kowshik Thopalli, Pavan K. Turaga, Jayaraman J. Thiagarajan:
Domain Alignment Meets Fully Test-Time Adaptation. CoRR abs/2207.04185 (2022) - [i80]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 abs/2207.05286 (2022) - [i79]Jayaraman J. Thiagarajan, Rushil Anirudh, Vivek Sivaraman Narayanaswamy, Peer-Timo Bremer:
Single Model Uncertainty Estimation via Stochastic Data Centering. CoRR abs/2207.07235 (2022) - [i78]Rakshith Subramanyam, Mark Heimann, Jayram S. Thathachar, Rushil Anirudh, Jayaraman J. Thiagarajan:
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification. CoRR abs/2207.12346 (2022) - [i77]Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan:
Exploring the Design of Adaptation Protocols for Improved Generalization and Machine Learning Safety. CoRR abs/2207.12615 (2022) - [i76]Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann, Danai Koutra, Jayaraman J. Thiagarajan:
Analyzing Data-Centric Properties for Contrastive Learning on Graphs. CoRR abs/2208.02810 (2022) - [i75]Rakshith Subramanyam, Kowshik Thopalli, Spring Berman, Pavan K. Turaga, Jayaraman J. Thiagarajan:
Single-Shot Domain Adaptation via Target-Aware Generative Augmentation. CoRR abs/2210.16692 (2022) - [i74]Yuzhe Lu, Shusen Liu, Jayaraman J. Thiagarajan, Wesam Sakla, Rushil Anirudh:
On-the-fly Object Detection using StyleGAN with CLIP Guidance. CoRR abs/2210.16742 (2022) - [i73]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 abs/2211.12340 (2022) - 2021
- [j23]Rushil Anirudh, Jayaraman J. Thiagarajan, Rahul Sridhar, Peer-Timo Bremer:
MARGIN: Uncovering Deep Neural Networks Using Graph Signal Analysis. Frontiers Big Data 4: 589417 (2021) - [j22]Hoseung Song, Jayaraman J. Thiagarajan, Bhavya Kailkhura:
Preventing Failures by Dataset Shift Detection in Safety-Critical Graph Applications. Frontiers Artif. Intell. 4: 589632 (2021) - [j21]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): 655-665 (2021) - [j20]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): 1241-1253 (2021) - [c77]Tejas Gokhale, Rushil Anirudh, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Chitta Baral, Yezhou Yang:
Attribute-Guided Adversarial Training for Robustness to Natural Perturbations. AAAI 2021: 7574-7582 - [c76]Jayaraman J. Thiagarajan, Vivek Sivaraman Narayanaswamy, Rushil Anirudh, Peer-Timo Bremer, Andreas Spanias:
Accurate and Robust Feature Importance Estimation under Distribution Shifts. AAAI 2021: 7891-7898 - [c75]Uday Shankar Shanthamallu, Jayaraman J. Thiagarajan, Andreas Spanias:
Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks. AAAI 2021: 9524-9532 - [c74]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: 428-437 - [c73]Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Andreas Spanias:
Using Deep Image Priors to Generate Counterfactual Explanations. ICASSP 2021: 2770-2774 - [c72]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: 1-5 - [c71]Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Andreas Spanias:
On the Design of Deep Priors for Unsupervised Audio Restoration. Interspeech 2021: 2167-2171 - [c70]Tarek Ramadan, Tanzima Z. Islam, Chase Phelps, Nathan Pinnow, Jayaraman J. Thiagarajan:
Comparative Code Structure Analysis using Deep Learning for Performance Prediction. ISPASS 2021: 151-161 - [c69]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 - [c68]Jayaraman J. Thiagarajan, Vivek Sivaraman Narayanaswamy, Deepta Rajan, Jia Liang, Akshay Chaudhari, Andreas Spanias:
Designing Counterfactual Generators using Deep Model Inversion. NeurIPS 2021: 16873-16884 - [i72]Nathan Pinnow, Tarek Ramadan, Tanzima Z. Islam, Chase Phelps, Jayaraman J. Thiagarajan:
Comparative Code Structure Analysis using Deep Learning for Performance Prediction. CoRR abs/2102.07660 (2021) - [i71]Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Deepta Rajan, Andreas Spanias:
Loss Estimators Improve Model Generalization. CoRR abs/2103.03788 (2021) - [i70]Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Andreas Spanias:
On the Design of Deep Priors for Unsupervised Audio Restoration. CoRR abs/2104.07161 (2021) - [i69]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 abs/2104.09684 (2021) - [i68]Jayaraman J. Thiagarajan, Vivek Sivaraman Narayanaswamy, Deepta Rajan, Jason Liang, Akshay Chaudhari, Andreas Spanias:
Designing Counterfactual Generators using Deep Model Inversion. CoRR abs/2109.14274 (2021) - [i67]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 abs/2110.01406 (2021) - [i66]Rushil Anirudh, Jayaraman J. Thiagarajan:
Δ-UQ: Accurate Uncertainty Quantification via Anchor Marginalization. CoRR abs/2110.02197 (2021) - [i65]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 abs/2111.12798 (2021) - [i64]Kowshik Thopalli, Sameeksha Katoch, Andreas Spanias, Pavan K. Turaga, Jayaraman J. Thiagarajan:
Improving Multi-Domain Generalization through Domain Re-labeling. CoRR abs/2112.09802 (2021) - 2020
- [j19]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): 2459-2477 (2020) - [j18]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): 45016 (2020) - [j17]Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Brian K. Spears:
Improved surrogates in inertial confinement fusion with manifold and cycle consistencies. Proc. Natl. Acad. Sci. USA 117(18): 9741-9746 (2020) - [j16]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): 3977-3988 (2020) - [j15]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): 291-300 (2020) - [c67]Jayaraman J. Thiagarajan, Bindya Venkatesh, Prasanna Sattigeri, Peer-Timo Bremer:
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors. AAAI 2020: 6005-6012 - [c66]Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Jayaraman J. Thiagarajan, Bhavya Kailkhura:
Treeview and Disentangled Representations for Explaining Deep Neural Networks Decisions. ACSSC 2020: 284-288 - [c65]Uday Shankar Shanthamallu, Jayaraman J. Thiagarajan, Andreas Spanias:
A Regularized Attention Mechanism for Graph Attention Networks. ICASSP 2020: 3372-3376 - [c64]Jayaraman J. Thiagarajan, Bindya Venkatesh, Deepta Rajan:
Learn-By-Calibrating: Using Calibration As A Training Objective. ICASSP 2020: 3632-3636 - [c63]Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Rushil Anirudh, Andreas Spanias:
Unsupervised Audio Source Separation Using Generative Priors. INTERSPEECH 2020: 2657-2661 - [c62]Abhinav Bhatele, Jayaraman J. Thiagarajan, Taylor L. Groves, Rushil Anirudh, Staci A. Smith, Brandon Cook, David K. Lowenthal:
The Case of Performance Variability on Dragonfly-based Systems. IPDPS 2020: 896-905 - [c61]Jayaraman J. Thiagarajan, Bindya Venkatesh, Deepta Rajan, Prasanna Sattigeri:
Improving Reliability of Clinical Models Using Prediction Calibration. UNSURE/GRAIL@MICCAI 2020: 71-80 - [c60]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 - [i63]Bindya Venkatesh, Jayaraman J. Thiagarajan, Kowshik Thopalli, Prasanna Sattigeri:
Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration. CoRR abs/2002.03875 (2020) - [i62]Jayaraman J. Thiagarajan, Prasanna Sattigeri, Deepta Rajan, Bindya Venkatesh:
Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models. CoRR abs/2004.14480 (2020) - [i61]Deepta Rajan, Jayaraman J. Thiagarajan, Alexandros Karargyris, Satyananda Kashyap:
Self-Training with Improved Regularization for Few-Shot Chest X-Ray Classification. CoRR abs/2005.02231 (2020) - [i60]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 abs/2005.02328 (2020) - [i59]Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Rushil Anirudh, Andreas Spanias:
Unsupervised Audio Source Separation using Generative Priors. CoRR abs/2005.13769 (2020) - [i58]Bindya Venkatesh, Jayaraman J. Thiagarajan:
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification. CoRR abs/2009.14448 (2020) - [i57]Jayaraman J. Thiagarajan, Vivek Sivaraman Narayanaswamy, Rushil Anirudh, Peer-Timo Bremer, Andreas Spanias:
Accurate and Robust Feature Importance Estimation under Distribution Shifts. CoRR abs/2009.14454 (2020) - [i56]Uday Shankar Shanthamallu, Jayaraman J. Thiagarajan, Andreas Spanias:
Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks. CoRR abs/2009.14455 (2020) - [i55]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 abs/2010.06558 (2020) - [i54]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 abs/2010.08478 (2020) - [i53]Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Andreas Spanias:
Using Deep Image Priors to Generate Counterfactual Explanations. CoRR abs/2010.12046 (2020) - [i52]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 abs/2010.13749 (2020) - [i51]Tejas Gokhale, Rushil Anirudh, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Chitta Baral, Yezhou Yang:
Attribute-Guided Adversarial Training for Robustness to Natural Perturbations. CoRR abs/2012.01806 (2020)
2010 – 2019
- 2019
- [c59]Huan Song, Jayaraman J. Thiagarajan:
Improved Deep Embeddings for Inferencing with Multi-Layered Graphs. IEEE BigData 2019: 5394-5400 - [c58]Sam Ade Jacobs, Jim Gaffney, Tom Benson, Peter B. Robinson, J. Luc Peterson,