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2020 – today
- 2024
[c104]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. CVPR Workshops 2024: 7791-7798- [c103]Rakshith Subramanyam, Kowshik Thopalli, Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan:
DECIDER: Leveraging Foundation Model Priors for Improved Model Failure Detection and Explanation. ECCV (79) 2024: 465-482 - [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 - [i99]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) - [i98]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) - [i97]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) - [i96]Yang Liu, Kowshik Thopalli, Jayaraman J. Thiagarajan:
Speeding Up Image Classifiers with Little Companions. CoRR abs/2406.17117 (2024) - [i95]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) - [i94]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) - [i93]Banooqa H. Banday, Kowshik Thopalli, Tanzima Z. Islam, Jayaraman J. Thiagarajan:
On The Role of Prompt Construction In Enhancing Efficacy and Efficiency of LLM-Based Tabular Data Generation. CoRR abs/2409.03946 (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. 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, Brian K. Spears, Brian Van Essen, David Hysom, Jae-Seung Yeom, Tim Moon, Rushil Anirudh, Jayaraman J. Thiagarajan, Shusen Liu, Peer-Timo Bremer:
Parallelizing Training of Deep Generative Models on Massive Scientific Datasets. CLUSTER 2019: 1-10 - [c57]Jayaraman J. Thiagarajan, Irene Kim, Rushil Anirudh, Peer-Timo Bremer:
Understanding Deep Neural Networks through Input Uncertainties. ICASSP 2019: 2812-2816 - [c56]Rushil Anirudh, Jayaraman J. Thiagarajan:
Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification. ICASSP 2019: 3197-3201 - [c55]Kowshik Thopalli, Rushil Anirudh, Jayaraman J. Thiagarajan, Pavan K. Turaga:
Multiple Subspace Alignment Improves Domain Adaptation. ICASSP 2019: 3552-3556 - [c54]Jayaraman J. Thiagarajan, Rushil Anirudh, Rahul Sridhar, Peer-Timo Bremer:
Unsupervised Dimension Selection Using a Blue Noise Graph Spectrum. ICASSP 2019: 5436-5440 - [c53]Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Huan Song, Andreas Spanias:
Designing an Effective Metric Learning Pipeline for Speaker Diarization. ICASSP 2019: 5806-5810 - [c52]Jayaraman J. Thiagarajan, Satyananda Kashyap, Alexandros Karargyris:
Distill-to-Label: Weakly Supervised Instance Labeling Using Knowledge Distillation. ICMLA 2019: 902-907 - [c51]Tapasya Patki, Jayaraman J. Thiagarajan, Alexis Ayala, Tanzima Z. Islam:
Performance optimality or reproducibility: that is the question. SC 2019: 77:1-77:30 - [i50]Vivek Sivaraman Narayanaswamy, Sameeksha Katoch, Jayaraman J. Thiagarajan, Huan Song, Andreas Spanias:
Audio Source Separation via Multi-Scale Learning with Dilated Dense U-Nets. CoRR abs/1904.04161 (2019) - [i49]Bhavya Kailkhura, Jayaraman J. Thiagarajan, Qunwei Li, Peer-Timo Bremer:
A Look at the Effect of Sample Design on Generalization through the Lens of Spectral Analysis. CoRR abs/1906.02732 (2019) - [i48]Kowshik Thopalli, Jayaraman J. Thiagarajan, Rushil Anirudh, Pavan K. Turaga:
SALT: Subspace Alignment as an Auxiliary Learning Task for Domain Adaptation. CoRR abs/1906.04338 (2019) - [i47]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, J. Luc Peterson, Peter B. Robinson, Harsh Bhatia, Valerio Pascucci, Brian K. Spears, Peer-Timo Bremer:
Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications. CoRR abs/1907.08325 (2019) - [i46]Jayaraman J. Thiagarajan, Satyananda Kashyap, Alexandros Karargyris:
Distill-to-Label: Weakly Supervised Instance Labeling Using Knowledge Distillation. CoRR abs/1907.12926 (2019) - [i45]Jayaraman J. Thiagarajan, Bindya Venkatesh, Prasanna Sattigeri, Peer-Timo Bremer:
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors. CoRR abs/1909.04079 (2019) - [i44]Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer:
Function Preserving Projection for Scalable Exploration of High-Dimensional Data. CoRR abs/1909.11804 (2019) - [i43]Rushil Anirudh, Hyojin Kim, Jayaraman J. Thiagarajan, K. Aditya Mohan, Kyle Champley:
Improving Limited Angle CT Reconstruction with a Robust GAN Prior. CoRR abs/1910.01634 (2019) - [i42]Rushil Anirudh, Jayaraman J. Thiagarajan, Shusen Liu, Peer-Timo Bremer, Brian K. Spears:
Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion. CoRR abs/1910.01666 (2019) - [i41]Sam Ade Jacobs, Brian Van Essen, David Hysom, Jae-Seung Yeom, Tim Moon, Rushil Anirudh, Jayaraman J. Thiagarajan, Shusen Liu, Peer-Timo Bremer, Jim Gaffney, Tom Benson, Peter B. Robinson, J. Luc Peterson, Brian K. Spears:
Parallelizing Training of Deep Generative Models on Massive Scientific Datasets. CoRR abs/1910.02270 (2019) - [i40]Jayaraman J. Thiagarajan, Bindya Venkatesh, Deepta Rajan:
Learn-By-Calibrating: Using Calibration as a Training Objective. CoRR abs/1910.14175 (2019) - [i39]Bindya Venkatesh, Jayaraman J. Thiagarajan:
Heteroscedastic Calibration of Uncertainty Estimators in Deep Learning. CoRR abs/1910.14179 (2019) - [i38]Sameeksha Katoch, Kowshik Thopalli, Jayaraman J. Thiagarajan, Pavan K. Turaga, Andreas Spanias:
Invenio: Discovering Hidden Relationships Between Tasks/Domains Using Structured Meta Learning. CoRR abs/1911.10600 (2019) - [i37]J. Luc Peterson, Rushil Anirudh, Kevin Athey, Benjamin Bay, Peer-Timo Bremer, Vic Castillo, Francesco Di Natale, David Fox, Jim A. Gaffney, David Hysom, Sam Ade Jacobs, Bhavya Kailkhura, Joe Koning, Bogdan Kustowski, Steven H. Langer, Peter B. Robinson, Jessica Semler, Brian K. Spears, Jayaraman J. Thiagarajan, Brian Van Essen, Jae-Seung Yeom:
Merlin: Enabling Machine Learning-Ready HPC Ensembles. CoRR abs/1912.02892 (2019) - [i36]Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Timo Bremer:
MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking. CoRR abs/1912.07748 (2019) - [i35]Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Brian K. Spears:
Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies. CoRR abs/1912.08113 (2019) - [i34]Enrico Bertini, Peer-Timo Bremer, Daniela Oelke, Jayaraman J. Thiagarajan:
Machine Learning Meets Visualization to Make Artificial Intelligence Interpretable (Dagstuhl Seminar 19452). Dagstuhl Reports 9(11): 24-33 (2019) - 2018
- [j14]Jayaraman J. Thiagarajan, Shusen Liu, Karthikeyan Natesan Ramamurthy, Peer-Timo Bremer:
Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections. Comput. Graph. Forum 37(3): 241-251 (2018) - [j13]Bhavya Kailkhura, Jayaraman J. Thiagarajan, Charvi Rastogi, Pramod K. Varshney, Peer-Timo Bremer:
A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms. J. Mach. Learn. Res. 19: 34:1-34:46 (2018) - [j12]Huan Song, Jayaraman J. Thiagarajan, Prasanna Sattigeri, Andreas Spanias:
Optimizing Kernel Machines Using Deep Learning. IEEE Trans. Neural Networks Learn. Syst. 29(11): 5528-5540 (2018) - [j11]Shusen Liu, Peer-Timo Bremer, Jayaraman J. Thiagarajan, Vivek Srikumar, Bei Wang, Yarden Livnat, Valerio Pascucci:
Visual Exploration of Semantic Relationships in Neural Word Embeddings. IEEE Trans. Vis. Comput. Graph. 24(1): 553-562 (2018) - [c50]Huan Song, Deepta Rajan, Jayaraman J. Thiagarajan, Andreas Spanias:
Attend and Diagnose: Clinical Time Series Analysis Using Attention Models. AAAI 2018: 4091-4098 - [c49]Rushil Anirudh, Hyojin Kim, Jayaraman J. Thiagarajan, K. Aditya Mohan, Kyle Champley, Timo Bremer:
Lose the Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion. CVPR 2018: 6343-6352 - [c48]Deepta Rajan, Jayaraman J. Thiagarajan:
A Generative Modeling Approach to Limited Channel ECG Classification. EMBC 2018: 2571-2574 - [c47]Jayaraman J. Thiagarajan, Nikhil Jain, Rushil Anirudh, Alfredo Giménez, Rahul Sridhar, Aniruddha Marathe, Tao Wang, Murali Emani, Abhinav Bhatele, Todd Gamblin:
Bootstrapping Parameter Space Exploration for Fast Tuning. ICS 2018: 385-395 - [c46]Huan Song, Megan M. Willi, Jayaraman J. Thiagarajan, Visar Berisha, Andreas Spanias:
Triplet Network with Attention for Speaker Diarization. INTERSPEECH 2018: 3608-3612 - [c45]Jayaraman J. Thiagarajan, Rushil Anirudh, Bhavya Kailkhura, Nikhil Jain, Tanzima Z. Islam, Abhinav Bhatele, Jae-Seung Yeom, Todd Gamblin:
PADDLE: Performance Analysis Using a Data-Driven Learning Environment. IPDPS 2018: 784-793 - [c44]Staci A. Smith, Clara E. Cromey, David K. Lowenthal, Jens Domke, Nikhil Jain, Jayaraman J. Thiagarajan, Abhinav Bhatele:
Mitigating inter-job interference using adaptive flow-aware routing. SC 2018: 27:1-27:15 - [i33]Deepta Rajan, Jayaraman J. Thiagarajan:
A Generative Modeling Approach to Limited Channel ECG Classification. CoRR abs/1802.06458 (2018) - [i32]Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Timo Bremer:
An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks. CoRR abs/1805.07281 (2018) - [i31]Huan Song, Megan M. Willi, Jayaraman J. Thiagarajan, Visar Berisha, Andreas Spanias:
Triplet Network with Attention for Speaker Diarization. CoRR abs/1808.01535 (2018) - [i30]Gowtham Muniraju, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer:
Controlled Random Search Improves Sample Mining and Hyper-Parameter Optimization. CoRR abs/1809.01712 (2018) - [i29]Jayaraman J. Thiagarajan, Deepta Rajan, Prasanna Sattigeri:
Can Deep Clinical Models Handle Real-World Domain Shifts? CoRR abs/1809.07806 (2018) - [i28]Uday Shankar Shanthamallu, Jayaraman J. Thiagarajan, Huan Song, Andreas Spanias:
Attention Models with Random Features for Multi-layered Graph Embeddings. CoRR abs/1810.01405 (2018) - [i27]Jayaraman J. Thiagarajan, Irene Kim, Rushil Anirudh, Peer-Timo Bremer:
Understanding Deep Neural Networks through Input Uncertainties. CoRR abs/1810.13425 (2018) - [i26]Jayaraman J. Thiagarajan, Rushil Anirudh, Rahul Sridhar, Peer-Timo Bremer:
Unsupervised Dimension Selection using a Blue Noise Spectrum. CoRR abs/1810.13427 (2018) - [i25]Uday Shankar Shanthamallu, Jayaraman J. Thiagarajan, Andreas Spanias:
Improving Robustness of Attention Models on Graphs. CoRR abs/1811.00181 (2018) - [i24]Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Huan Song, Andreas Spanias:
Designing an Effective Metric Learning Pipeline for Speaker Diarization. CoRR abs/1811.00183 (2018) - [i23]Kowshik Thopalli, Rushil Anirudh, Jayaraman J. Thiagarajan, Pavan K. Turaga:
Multiple Subspace Alignment Improves Domain Adaptation. CoRR abs/1811.04491 (2018) - [i22]Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Timo Bremer:
MimicGAN: Corruption-Mimicking for Blind Image Recovery & Adversarial Defense. CoRR abs/1811.08484 (2018) - [i21]Huan Song, Jayaraman J. Thiagarajan:
Improved Community Detection using Deep Embeddings from Multilayer Graphs. CoRR abs/1811.12156 (2018) - 2017
- [c43]Rushil Anirudh, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer:
Poisson Disk Sampling on the Grassmannnian: Applications in Subspace Optimization. CVPR Workshops 2017: 690-698 - [c42]Huan Song, Jayaraman J. Thiagarajan, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Andreas Spanias:
A deep learning approach to multiple kernel fusion. ICASSP 2017: 2292-2296 - [c41]Peng Zheng, Aleksandr Y. Aravkin, Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy:
Learning Robust Representations for Computer Vision. ICCV Workshops 2017: 1784-1791 - [c40]Aniruddha Marathe, Rushil Anirudh, Nikhil Jain, Abhinav Bhatele, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Jae-Seung Yeom, Barry Rountree, Todd Gamblin:
Performance modeling under resource constraints using deep transfer learning. SC 2017: 31 - [i20]Rushil Anirudh, Jayaraman J. Thiagarajan:
Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification. CoRR abs/1704.07487 (2017) - [i19]Peng Zheng, Aleksandr Y. Aravkin, Karthikeyan Natesan Ramamurthy, Jayaraman J. Thiagarajan:
Learning Robust Representations for Computer Vision. CoRR abs/1708.00069 (2017) - [i18]Youzuo Lin, Shusen Wang, Jayaraman J. Thiagarajan, George Guthrie, David Coblentz:
Efficient Data-Driven Geologic Feature Detection from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm. CoRR abs/1710.04329 (2017) - [i17]Huan Song, Deepta Rajan, Jayaraman J. Thiagarajan, Andreas Spanias:
Attend and Diagnose: Clinical Time Series Analysis using Attention Models. CoRR abs/1711.03905 (2017) - [i16]Huan Song, Jayaraman J. Thiagarajan, Prasanna Sattigeri, Andreas Spanias:
Optimizing Kernel Machines using Deep Learning. CoRR abs/1711.05374 (2017) - [i15]Rushil Anirudh, Jayaraman J. Thiagarajan, Rahul Sridhar, Timo Bremer:
Influential Sample Selection: A Graph Signal Processing Approach. CoRR abs/1711.05407 (2017) - [i14]Rushil Anirudh, Hyojin Kim, Jayaraman J. Thiagarajan, K. Aditya Mohan, Kyle Champley, Peer-Timo Bremer:
Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion. CoRR abs/1711.10388 (2017) - [i13]Bhavya Kailkhura, Jayaraman J. Thiagarajan, Charvi Rastogi, Pramod K. Varshney, Peer-Timo Bremer:
A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms. CoRR abs/1712.06028 (2017) - [i12]Jayaraman J. Thiagarajan, Shusen Liu, Karthikeyan Natesan Ramamurthy, Peer-Timo Bremer:
Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections. CoRR abs/1712.07106 (2017) - 2016
- [j10]Prashant Khanduri, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Pramod K. Varshney:
Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach. IEEE Signal Process. Lett. 23(10): 1484-1488 (2016) - [j9]Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Pramod K. Varshney:
Stair blue noise sampling. ACM Trans. Graph. 35(6): 248:1-248:10 (2016) - [c39]Huan Song, Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Andreas Spanias, Pavan K. Turaga:
Consensus inference on mobile phone sensors for activity recognition. ICASSP 2016: 2294-2298 - [c38]Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Pramod K. Varshney:
Theoretical guarantees for poisson disk sampling using pair correlation function. ICASSP 2016: 2589-2593 - [c37]Karthikeyan Natesan Ramamurthy, Aleksandr Y. Aravkin, Jayaraman J. Thiagarajan:
Beyond L2-loss functions for learning sparse models. ICASSP 2016: 4692-4696 - [c36]Jayaraman J. Thiagarajan, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Bhavya Kailkhura:
Robust Local Scaling Using Conditional Quantiles of Graph Similarities. ICDM Workshops 2016: 762-769 - [c35]Huan Song, Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Andreas Spanias:
Auto-context modeling using multiple Kernel learning. ICIP 2016: 1868-1872 - [c34]Rushil Anirudh, Jayaraman J. Thiagarajan, Timo Bremer, Hyojin Kim:
Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data. Computer-Aided Diagnosis 2016: 978532 - [c33]Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Berkay Kanberoglu, David H. Frakes, Kevin Bennett, Andreas Spanias:
Measuring glomerular number from kidney MRI images. Image Processing 2016: 978412 - [c32]Qunwei Li, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Zhenliang Zhang, Pramod K. Varshney:
Influential Node Detection in Implicit Social Networks using Multi-task Gaussian Copula Models. NIPS Time Series Workshop 2016: 27-37 - [c31]Prasanna Sattigeri, Jayaraman J. Thiagarajan:
Sparsifying Word Representations for Deep Unordered Sentence Modeling. Rep4NLP@ACL 2016: 206-214 - [c30]Jae-Seung Yeom, Jayaraman J. Thiagarajan, Abhinav Bhatele, Greg Bronevetsky, Tzanio V. Kolev:
Data-Driven Performance Modeling of Linear Solvers for Sparse Matrices. PMBS@SC 2016: 32-42 - [c29]Tanzima Z. Islam, Jayaraman J. Thiagarajan, Abhinav Bhatele, Martin Schulz, Todd Gamblin:
A machine learning framework for performance coverage analysis of proxy applications. SC 2016: 538-549 - [i11]Prashant Khanduri, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Pramod K. Varshney:
Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach. CoRR abs/1601.06201 (2016) - [i10]Jayaraman J. Thiagarajan, Bhavya Kailkhura, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy:
TreeView: Peeking into Deep Neural Networks Via Feature-Space Partitioning. CoRR abs/1611.07429 (2016) - [i9]Qunwei Li, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Zhenliang Zhang, Pramod K. Varshney:
Influential Node Detection in Implicit Social Networks using Multi-task Gaussian Copula Models. CoRR abs/1611.10305 (2016) - [i8]Huan Song, Jayaraman J. Thiagarajan, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Andreas Spanias:
A Deep Learning Approach To Multiple Kernel Fusion. CoRR abs/1612.09007 (2016) - 2015
- [j8]Shusen Liu, Bei Wang, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Valerio Pascucci:
Visual Exploration of High-Dimensional Data through Subspace Analysis and Dynamic Projections. Comput. Graph. Forum 34(3): 271-280 (2015) - [j7]Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Andreas Spanias:
Learning Stable Multilevel Dictionaries for Sparse Representations. IEEE Trans. Neural Networks Learn. Syst. 26(9): 1913-1926 (2015) - [c28]Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy:
Subspace learning using consensus on the grassmannian manifold. ICASSP 2015: 2031-2035 - [c27]Hyojin Kim, Jayaraman J. Thiagarajan, Peer-Timo Bremer:
A Randomized Ensemble Approach to Industrial CT Segmentation. ICCV 2015: 1707-1715 - [c26]Abhinav Bhatele, Andrew R. Titus, Jayaraman J. Thiagarajan, Nikhil Jain, Todd Gamblin, Peer-Timo Bremer, Martin Schulz, Laxmikant V. Kalé:
Identifying the Culprits Behind Network Congestion. IPDPS 2015: 113-122 - [i7]Suhas Ranganath, Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Shuang Hu, Mahesh K. Banavar, Andreas Spanias:
Undergraduate Signal Processing Laboratories for the Android Operating System. CoRR abs/1502.07026 (2015) - 2014
- [b3]Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Pavan K. Turaga, Andreas Spanias:
Image Understanding Using Sparse Representations. Synthesis Lectures on Image, Video, and Multimedia Processing, Morgan & Claypool Publishers 2014, ISBN 978-3-031-01122-1 - [j6]Karthikeyan Natesan Ramamurthy, Jayaraman J. Thiagarajan, Andreas Spanias:
Recovering non-negative and combined sparse representations. Digit. Signal Process. 26: 21-35 (2014) - [j5]Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Deepta Rajan, Andreas Spanias, Anup Puri, David H. Frakes:
Kernel Sparse Models for Automated Tumor Segmentation. Int. J. Artif. Intell. Tools 23(3) (2014) - [j4]Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Andreas Spanias:
Multiple Kernel Sparse Representations for Supervised and Unsupervised Learning. IEEE Trans. Image Process. 23(7): 2905-2915 (2014) - [c25]Karthikeyan Natesan Ramamurthy, Jayaraman J. Thiagarajan, Rahul Sridhar, Premnishanth Kothandaraman, Ramanathan Nachiappan:
Consensus inference with multilayer graphs for multi-modal data. ACSSC 2014: 1341-1345 - [c24]Prasanna Sattigeri, Jayaraman J. Thiagarajan, Mohit Shah, Karthikeyan Natesan Ramamurthy, Andreas Spanias:
A scalable feature learning and tag prediction framework for natural environment sounds. ACSSC 2014: 1779-1783 - [c23]Jayaraman J. Thiagarajan, Peer-Timo Bremer, Karthikeyan Natesan Ramamurthy:
Multiple kernel interpolation for inverting non-linear dimensionality reduction and dimension estimation. ICASSP 2014: 6751-6755 - [c22]Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Peer-Timo Bremer, Andreas Spanias:
Automatic image annotation using inverse maps from semantic embeddings. ICIP 2014: 3107-3111 - [c21]Hyojin Kim, Jayaraman J. Thiagarajan, Peer-Timo Bremer:
Image segmentation using consensus from hierarchical segmentation ensembles. ICIP 2014: 3272-3276 - [c20]Shusen Liu, Bei Wang, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Valerio Pascucci:
Multivariate volume visualization through dynamic projections. LDAV 2014: 35-42 - [c19]Karthikeyan Natesan Ramamurthy, Kush R. Varshney, Jayaraman J. Thiagarajan:
Computing persistent homology under random projection. SSP 2014: 105-108 - [i6]Karthikeyan Natesan Ramamurthy, Aleksandr Y. Aravkin, Jayaraman J. Thiagarajan:
Beyond L2-Loss Functions for Learning Sparse Models. CoRR abs/1403.6706 (2014) - 2013
- [b2]Jayaraman J. Thiagarajan:
Sparse Methods in Image Understanding and Computer Vision. Arizona State University, Tempe, USA, 2013 - [j3]Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Andreas Spanias:
Mixing matrix estimation using discriminative clustering for blind source separation. Digit. Signal Process. 23(1): 9-18 (2013) - [c18]Karthikeyan Natesan Ramamurthy, Jayaraman J. Thiagarajan, Andreas Spanias, Prasanna Sattigeri:
Boosted dictionaries for image restoration based on sparse representations. ICASSP 2013: 1583-1587 - [c17]Rushil Anirudh, Karthikeyan Natesan Ramamurthy, Jayaraman J. Thiagarajan, Pavan K. Turaga, Andreas Spanias:
A heterogeneous dictionary model for representation and recognition of human actions. ICASSP 2013: 3472-3476 - [i5]Karthikeyan Natesan Ramamurthy, Jayaraman J. Thiagarajan, Prasanna Sattigeri, Andreas Spanias:
Ensemble Sparse Models for Image Analysis. CoRR abs/1302.6957 (2013) - [i4]Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Andreas Spanias:
Learning Stable Multilevel Dictionaries for Sparse Representation of Images. CoRR abs/1303.0448 (2013) - [i3]Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Andreas Spanias:
Multiple Kernel Sparse Representations for Supervised and Unsupervised Learning. CoRR abs/1303.0582 (2013) - [i2]Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Deepta Rajan, Anup Puri, David H. Frakes, Andreas Spanias:
Kernel Sparse Models for Automated Tumor Segmentation. CoRR abs/1303.2610 (2013) - [i1]Karthikeyan Natesan Ramamurthy, Jayaraman J. Thiagarajan, Andreas Spanias:
Recovering Non-negative and Combined Sparse Representations. CoRR abs/1303.4694 (2013) - 2012
- [c16]Karthikeyan Natesan Ramamurthy, Jayaraman J. Thiagarajan, Prasanna Sattigeri, Andreas Spanias:
Learning dictionaries with graph embedding constraints. ACSCC 2012: 1974-1978 - [c15]Jayaraman J. Thiagarajan, Deepta Rajan, Karthikeyan Natesan Ramamurthy, David H. Frakes, Andreas Spanias:
Automated tumor segmentation using kernel sparse representations. BIBE 2012: 401-406 - [c14]Prasanna Sattigeri, Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Andreas Spanias:
Implementation of a fast image coding and retrieval system using a GPU. ESPA 2012: 5-8 - [c13]Suhas Ranganath, Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Shuang Hu, Mahesh K. Banavar, Andreas Spanias:
Work in progress: Performing signal analysis laboratories using Android devices. FIE 2012: 1-2 - [c12]Jinru Liu, Shuang Hu, Jayaraman J. Thiagarajan, Xue Zhang, Suhas Ranganath, Mahesh K. Banavar, Andreas Spanias:
Interactive DSP laboratories on mobile phones and tablets. ICASSP 2012: 2761-2764 - [c11]Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Andreas Spanias:
Supervised local sparse coding of sub-image features for image retrieval. ICIP 2012: 3117-3120 - [c10]Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Andreas Spanias, Panos Nasiopoulos:
Learning Multilevel Dictionaries for Compressed Sensing Using Discriminative Clustering. IIH-MSP 2012: 494-497 - 2011
- [b1]Jayaraman J. Thiagarajan, Andreas Spanias:
Analysis of the MPEG-1 Layer III (MP3) Algorithm Using MATLAB. Synthesis Lectures on Algorithms and Software in Engineering, Morgan & Claypool Publishers 2011, ISBN 978-3-031-00390-5 - [j2]Karthikeyan Natesan Ramamurthy, Jayaraman J. Thiagarajan, Prasanna Sattigeri, Michael Goryll, Andreas Spanias, Trevor Thornton, Stephen M. Phillips:
Transform domain features for ion-channel signal classification. Biomed. Signal Process. Control. 6(3): 219-224 (2011) - [j1]Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Andreas Spanias:
Optimality and stability of the K-hyperline clustering algorithm. Pattern Recognit. Lett. 32(9): 1299-1304 (2011) - [c9]Jayaraman J. Thiagarajan, Andreas Spanias:
Learning dictionaries for local sparse coding in image classification. ACSCC 2011: 2014-2018 - [c8]Jinru Liu, Andreas Spanias, Mahesh K. Banavar, Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Shuang Hu, Xue Zhang:
Work in progress - Interactive signal-processing labs and simulations on iOS devices. FIE 2011: 2 - [c7]Kostas Tsakalis, Jayaraman J. Thiagarajan, Tolga M. Duman, Martin Reisslein, G. Tong Zhou, Xiaoli Ma, Photini Spanias:
Work in progress - Modules and laboratories for a pathways course in signals and systems. FIE 2011: 2 - [c6]Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Jayaraman J. Thiagarajan, Michael Goryll, Andreas Spanias, Trevor Thornton:
Analyte detection using an ion-channel sensor array. DSP 2011: 1-6 - [c5]Karthikeyan Natesan Ramamurthy, Jayaraman J. Thiagarajan, Andreas Spanias:
Improved sparse coding using manifold projections. ICIP 2011: 1237-1240 - 2010
- [c4]Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Andreas Spanias:
Dimensionality Reduction for Distance Based Video Clustering. AIAI 2010: 270-277
2000 – 2009
- 2009
- [c3]Karthikeyan Natesan Ramamurthy, Jayaraman J. Thiagarajan, Andreas Spanias:
Fast image registration with non-stationary Gauss-Markov random field templates. ICIP 2009: 185-188 - [c2]Karthikeyan Natesan Ramamurthy, Jayaraman J. Thiagarajan, Andreas Spanias:
Template Learning using Wavelet Domain Statistical Models. SGAI Conf. 2009: 179-192 - 2008
- [c1]Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Andreas Spanias:
Sparse Representations for Pattern Classification using Learned Dictionaries. SGAI Conf. 2008: 33-45
Coauthor Index
[c104] [c101] [c100] [c99] [c98] [i99] [i98] [i97] [j31] [c97] [c94] [c92] [c89] [c88] [c87] [i92] [i88] [i87] [i86] [i85] [j28] [j25] [j24] [c86] [c84] [c83] [c82] [c81] [c79] [i84] [i83] [i82] [i80] [i79] [i78] [i74] [i73] [j23] [c77] [c76] [i69] [i66] [i65] [j19] [j18] [j17] [j15] [c63] [c62] [i60] [i59] [i57] [i55] [i54] [i52] [i51] [c58] [c57] [c56] [c55] [c54] [i48] [i47] [i44] [i43] [i42] [i41] [i37] [i36] [i35] [c49] [c47] [c45] [i32] [i27] [i26] [i23] [i22] [c43] [c40] [i20] [i15] [i14] [c34] [c17]
Peer-Timo Bremer
aka: Timo Bremer
aka: Timo Bremer
[c97] [i92] [j28] [j25] [j24] [c84] [c82] [c81] [c79] [i79] [j23] [j20] [c76] [i69] [i65] [j19] [j18] [j17] [j15] [c67] [c60] [i60] [i57] [i55] [i54] [i52] [c58] [c57] [c54] [i49] [i47] [i45] [i44] [i42] [i41] [i37] [i36] [i35] [i34] [j14] [j13] [j11] [c49] [i32] [i30] [i27] [i26] [i22] [c43] [i15] [i14] [i13] [i12] [j9] [c38] [c34] [j8] [c27] [c26] [c23] [c22] [c21] [c20]
Karthikeyan Natesan Ramamurthy
aka: Karthikeyan Ramamurthy
aka: Karthikeyan Ramamurthy
[c89] [j26] [c83] [c80] [i80] [j21] [j20] [c76] [c75] [c73] [c72] [c71] [c68] [i71] [i70] [i68] [i64] [j16] [c65] [c63] [i59] [i57] [i56] [i53] [c53] [i50] [i38] [j12] [c50] [c46] [i31] [i28] [i25] [i24] [c42] [i17] [i16] [c39] [c35] [c33] [i8] [j7] [i7] [b3] [j6] [j5] [j4] [c24] [c22] [j3] [c18] [c17] [i5] [i4] [i3] [i2] [i1] [c16] [c15] [c14] [c13] [c12] [c11] [c10] [b1] [j2] [j1] [c9] [c8] [c6] [c5] [c4] [c3] [c2] [c1]
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