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2020 – today
- 2024
[c47]Rakshith Subramanyam, T. S. Jayram, Rushil Anirudh, Jayaraman J. Thiagarajan:
Exploring the Utility of Clip Priors for Visual Relationship Prediction. ICASSP 2024: 6825-6829- [c46]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 - [c45]Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan:
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. ICLR 2024 - [c44]Jayaraman J. Thiagarajan, Vivek Sivaraman Narayanaswamy, Puja Trivedi, Rushil Anirudh:
PAGER: Accurate Failure Characterization in Deep Regression Models. ICML 2024 - [i52]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) - [i51]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) - [i50]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) - 2023
- [j12]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) - [c43]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 - [c42]Eun Som Jeon, Suhas Lohit, Rushil Anirudh, Pavan K. Turaga:
Robust Time Series Recovery and Classification Using Test-Time Noise Simulator Networks. ICASSP 2023: 1-5 - [c41]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 - [c40]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 - [c39]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 - [c38]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 - [c37]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 - [i49]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) - [i48]Rakshith Subramanyam, T. S. Jayram, Rushil Anirudh, Jayaraman J. Thiagarajan:
CREPE: Learnable Prompting With CLIP Improves Visual Relationship Prediction. CoRR abs/2307.04838 (2023) - [i47]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) - [i46]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) - [i45]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
- [j11]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) - [j10]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) - [j9]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) - [c36]Rushil Anirudh, Jayaraman J. Thiagarajan:
Out of Distribution Detection via Neural Network Anchoring. ACML 2022: 32-47 - [c35]Mikel Landajuela, Rushil Anirudh, Joe Loscazo, Robert Blake:
Intracardiac Electrical Imaging Using the 12-Lead ECG: A Machine Learning Approach Using Synthetic Data. CinC 2022: 1-4 - [c34]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 2022: 7791-7798 - [c33]Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer:
Sparsity Improves Unsupervised Attribute Discovery in Stylegan. ICASSP 2022: 3388-3392 - [c32]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 - [c31]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 - [c30]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 - [c29]Jayaraman J. Thiagarajan, Rushil Anirudh, Vivek Sivaraman Narayanaswamy, Timo Bremer:
Single Model Uncertainty Estimation via Stochastic Data Centering. NeurIPS 2022 - [i44]Kowshik Thopalli, Jayaraman J. Thiagarajan, Rushil Anirudh, Pavan K. Turaga:
Revisiting Deep Subspace Alignment for Unsupervised Domain Adaptation. CoRR abs/2201.01806 (2022) - [i43]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) - [i42]Rushil Anirudh, Jayaraman J. Thiagarajan:
Out of Distribution Detection via Neural Network Anchoring. CoRR abs/2207.04125 (2022) - [i41]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) - [i40]Jayaraman J. Thiagarajan, Rushil Anirudh, Vivek Sivaraman Narayanaswamy, Peer-Timo Bremer:
Single Model Uncertainty Estimation via Stochastic Data Centering. CoRR abs/2207.07235 (2022) - [i39]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) - [i38]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) - [i37]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
- [j8]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) - [j7]Qunwei Li, Bhavya Kailkhura, Rushil Anirudh, Jize Zhang, Yi Zhou, Yingbin Liang, Thomas Yong-Jin Han, Pramod K. Varshney:
MR-GAN: Manifold Regularized Generative Adversarial Networks for Scientific Data. SIAM J. Math. Data Sci. 3(4): 1197-1222 (2021) - [c28]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 - [c27]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 - [c26]Albert W. Reed, Hyojin Kim, Rushil Anirudh, K. Aditya Mohan, Kyle Champley, Jingu Kang, Suren Jayasuriya:
Dynamic CT Reconstruction from Limited Views with Implicit Neural Representations and Parametric Motion Fields. ICCV 2021: 2238-2248 - [c25]Harsh Bhatia, Steve Petruzza, Rushil Anirudh, Attila Gyulassy, Robert M. Kirby, Valerio Pascucci, Peer-Timo Bremer:
Data-Driven Estimation of Temporal-Sampling Errors in Unsteady Flows. ISVC (1) 2021: 235-248 - [c24]Suhas Lohit, Rushil Anirudh, Pavan K. Turaga:
Recovering Trajectories of Unmarked Joints in 3D Human Actions Using Latent Space Optimization. WACV 2021: 2341-2350 - [c23]Rushil Anirudh, Suhas Lohit, Pavan K. Turaga:
Generative Patch Priors for Practical Compressive Image Recovery. WACV 2021: 2534-2544 - [i36]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) - [i35]Rushil Anirudh, Jayaraman J. Thiagarajan:
Δ-UQ: Accurate Uncertainty Quantification via Anchor Marginalization. CoRR abs/2110.02197 (2021) - [i34]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) - 2020
- [j6]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) - [j5]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) - [j4]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) - [j3]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) - [c22]Kaushik Koneripalli, Suhas Lohit, Rushil Anirudh, Pavan K. Turaga:
Rate-Invariant Autoencoding of Time-Series. ICASSP 2020: 3732-3736 - [c21]Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Rushil Anirudh, Andreas Spanias:
Unsupervised Audio Source Separation Using Generative Priors. INTERSPEECH 2020: 2657-2661 - [c20]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 - [i33]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) - [i32]Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Rushil Anirudh, Andreas Spanias:
Unsupervised Audio Source Separation using Generative Priors. CoRR abs/2005.13769 (2020) - [i31]Rushil Anirudh, Suhas Lohit, Pavan K. Turaga:
Generative Patch Priors for Practical Compressive Image Recovery. CoRR abs/2006.10873 (2020) - [i30]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) - [i29]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) - [i28]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) - [i27]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) - [i26]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) - [i25]Suhas Lohit, Rushil Anirudh, Pavan K. Turaga:
Recovering Trajectories of Unmarked Joints in 3D Human Actions Using Latent Space Optimization. CoRR abs/2012.02043 (2020)
2010 – 2019
- 2019
- [c19]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 - [c18]Jayaraman J. Thiagarajan, Irene Kim, Rushil Anirudh, Peer-Timo Bremer:
Understanding Deep Neural Networks through Input Uncertainties. ICASSP 2019: 2812-2816 - [c17]Rushil Anirudh, Jayaraman J. Thiagarajan:
Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification. ICASSP 2019: 3197-3201 - [c16]Kowshik Thopalli, Rushil Anirudh, Jayaraman J. Thiagarajan, Pavan K. Turaga:
Multiple Subspace Alignment Improves Domain Adaptation. ICASSP 2019: 3552-3556 - [c15]Jayaraman J. Thiagarajan, Rushil Anirudh, Rahul Sridhar, Peer-Timo Bremer:
Unsupervised Dimension Selection Using a Blue Noise Graph Spectrum. ICASSP 2019: 5436-5440 - [i24]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) - [i23]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) - [i22]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) - [i21]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) - [i20]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) - [i19]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) - [i18]Hyojin Kim, Rushil Anirudh, K. Aditya Mohan, Kyle Champley:
Extreme Few-view CT Reconstruction using Deep Inference. CoRR abs/1910.05375 (2019) - [i17]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) - [i16]Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Timo Bremer:
MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking. CoRR abs/1912.07748 (2019) - [i15]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) - 2018
- [c14]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 - [c13]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 - [c12]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 - [i14]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) - [i13]Jayaraman J. Thiagarajan, Irene Kim, Rushil Anirudh, Peer-Timo Bremer:
Understanding Deep Neural Networks through Input Uncertainties. CoRR abs/1810.13425 (2018) - [i12]Jayaraman J. Thiagarajan, Rushil Anirudh, Rahul Sridhar, Peer-Timo Bremer:
Unsupervised Dimension Selection using a Blue Noise Spectrum. CoRR abs/1810.13427 (2018) - [i11]Kowshik Thopalli, Rushil Anirudh, Jayaraman J. Thiagarajan, Pavan K. Turaga:
Multiple Subspace Alignment Improves Domain Adaptation. CoRR abs/1811.04491 (2018) - [i10]Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Timo Bremer:
MimicGAN: Corruption-Mimicking for Blind Image Recovery & Adversarial Defense. CoRR abs/1811.08484 (2018) - [i9]Qunwei Li, Bhavya Kailkhura, Rushil Anirudh, Yi Zhou, Yingbin Liang, Pramod K. Varshney:
MR-GAN: Manifold Regularized Generative Adversarial Networks. CoRR abs/1811.10427 (2018) - 2017
- [j2]Rushil Anirudh, Pavan K. Turaga, Jingyong Su, Anuj Srivastava:
Elastic Functional Coding of Riemannian Trajectories. IEEE Trans. Pattern Anal. Mach. Intell. 39(5): 922-936 (2017) - [c11]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 - [c10]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 - [p1]Rushil Anirudh, Pavan K. Turaga, Anuj Srivastava:
Optimization Problems Associated with Manifold-Valued Curves with Applications in Computer Vision. Handbook of Convex Optimization Methods in Imaging Science 2017: 207-228 - [i8]Rushil Anirudh, Jayaraman J. Thiagarajan:
Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification. CoRR abs/1704.07487 (2017) - [i7]Rushil Anirudh, Jayaraman J. Thiagarajan, Rahul Sridhar, Timo Bremer:
Influential Sample Selection: A Graph Signal Processing Approach. CoRR abs/1711.05407 (2017) - [i6]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) - 2016
- [b1]Rushil Anirudh:
Statistical and Dynamical Modeling of Riemannian Trajectories with Application to Human Movement Analysis. Arizona State University, Tempe, USA, 2016 - [j1]Rushil Anirudh, Pavan K. Turaga:
Geometry-Based Symbolic Approximation for Fast Sequence Matching on Manifolds. Int. J. Comput. Vis. 116(2): 161-173 (2016) - [c9]Anirudh Som, Rushil Anirudh, Qiao Wang, Pavan K. Turaga:
Riemannian Geometric Approaches for Measuring Movement Quality. CVPR Workshops 2016: 1005 - [c8]Rushil Anirudh, Vinay Venkataraman, Karthikeyan Natesan Ramamurthy, Pavan K. Turaga:
A Riemannian Framework for Statistical Analysis of Topological Persistence Diagrams. CVPR Workshops 2016: 1023-1031 - [c7]Rushil Anirudh, Ahnaf Masroor, Pavan K. Turaga:
Diversity promoting online sampling for streaming video summarization. ICIP 2016: 3329-3333 - [c6]Rushil Anirudh, Jayaraman J. Thiagarajan, Timo Bremer, Hyojin Kim:
Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data. Medical Imaging: Computer-Aided Diagnosis 2016: 978532 - [i5]Rushil Anirudh, Pavan K. Turaga, Jingyong Su, Anuj Srivastava:
Elastic Functional Coding of Riemannian Trajectories. CoRR abs/1603.02200 (2016) - [i4]Rushil Anirudh, Vinay Venkataraman, Karthikeyan Natesan Ramamurthy, Pavan K. Turaga:
A Riemannian Framework for Statistical Analysis of Topological Persistence Diagrams. CoRR abs/1605.08912 (2016) - [i3]Rushil Anirudh, Ahnaf Masroor, Pavan K. Turaga:
Diversity Promoting Online Sampling for Streaming Video Summarization. CoRR abs/1610.09582 (2016) - 2015
- [c5]Rushil Anirudh, Pavan K. Turaga, Jingyong Su, Anuj Srivastava:
Elastic functional coding of human actions: From vector-fields to latent variables. CVPR 2015: 3147-3155 - [c4]Aswin Sivakumar, Rushil Anirudh, Pavan K. Turaga:
Geometric Compression of Orientation Signals for Fast Gesture Analysis. DCC 2015: 423-432 - [c3]Michael Krzyzaniak, Rushil Anirudh, Vinay Venkataraman, Pavan K. Turaga, Sha Xin Wei:
Towards realtime measurement of connectedness in human movement. MOCO 2015: 120-123 - 2014
- [c2]Rushil Anirudh, Pavan K. Turaga:
Interactively test driving an object detector: Estimating performance on unlabeled data. WACV 2014: 175-182 - [i2]Rushil Anirudh, Pavan K. Turaga:
Geometry-based Adaptive Symbolic Approximation for Fast Sequence Matching on Manifolds: Applications to Activity Analysis. CoRR abs/1403.0820 (2014) - [i1]Rushil Anirudh, Pavan K. Turaga:
Interactively Test Driving an Object Detector: Estimating Performance on Unlabeled Data. CoRR abs/1406.5653 (2014) - 2013
- [c1]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
Coauthor Index
Peer-Timo Bremer
aka: Timo Bremer
aka: Timo Bremer