A heterogeneous dictionary model for representation and recognition of human actions. - Rushil Anirudh; Karthikeyan Natesan Ramamurthy; Jayaraman J. Thiagarajan; Pavan K. Turaga; Andreas Spanias (ICASSP, 2013)

Interactively test driving an object detector: Estimating performance on unlabeled data. - Rushil Anirudh; Pavan K. Turaga (WACV, 2014)

Geometry-based Adaptive Symbolic Approximation for Fast Sequence Matching on Manifolds: Applications to Activity Analysis. - Rushil Anirudh; Pavan K. Turaga (CoRR, 2014)

Elastic functional coding of human actions: From vector-fields to latent variables. - Rushil Anirudh; Pavan K. Turaga; Jingyong Su; Anuj Srivastava (CVPR, 2015)

Geometric Compression of Orientation Signals for Fast Gesture Analysis. - Aswin Sivakumar; Rushil Anirudh; Pavan K. Turaga (DCC, 2015)

Towards realtime measurement of connectedness in human movement. - Michael Krzyzaniak; Rushil Anirudh; Vinay Venkataraman; Pavan K. Turaga; Sha Xin Wei (MOCO, 2015)

A Riemannian Framework for Statistical Analysis of Topological Persistence Diagrams. - Rushil Anirudh; Vinay Venkataraman; Karthikeyan Natesan Ramamurthy; Pavan K. Turaga (CVPR Workshops, 2016)

Riemannian Geometric Approaches for Measuring Movement Quality. - Anirudh Som; Rushil Anirudh; Qiao Wang; Pavan K. Turaga (CVPR Workshops, 2016)

Diversity promoting online sampling for streaming video summarization. - Rushil Anirudh; Ahnaf Masroor; Pavan K. Turaga (ICIP, 2016)

Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data. - Rushil Anirudh; Jayaraman J. Thiagarajan; Timo Bremer; Hyojin Kim 001 (Computer-Aided Diagnosis, 2016)

Geometry-Based Symbolic Approximation for Fast Sequence Matching on Manifolds. - Rushil Anirudh; Pavan K. Turaga (Int. J. Comput. Vis., 2016)

Statistical and Dynamical Modeling of Riemannian Trajectories with Application to Human Movement Analysis. - Rushil Anirudh (2016)

Optimization Problems Associated with Manifold-Valued Curves with Applications in Computer Vision. - Rushil Anirudh; Pavan K. Turaga; Anuj Srivastava (Handbook of Convex Optimization Methods in Imaging Science, 2017)

Poisson Disk Sampling on the Grassmannnian: Applications in Subspace Optimization. - Rushil Anirudh; Bhavya Kailkhura; Jayaraman J. Thiagarajan; Peer-Timo Bremer (CVPR Workshops, 2017)

Performance modeling under resource constraints using deep transfer learning. - Aniruddha Marathe; Rushil Anirudh; Nikhil Jain; Abhinav Bhatele; Jayaraman J. Thiagarajan; Bhavya Kailkhura; Jae-Seung Yeom; Barry Rountree; Todd Gamblin (SC, 2017)

Influential Sample Selection: A Graph Signal Processing Approach. - Rushil Anirudh; Jayaraman J. Thiagarajan; Rahul Sridhar; Timo Bremer (CoRR, 2017)

Elastic Functional Coding of Riemannian Trajectories. - Rushil Anirudh; Pavan K. Turaga; Jingyong Su; Anuj Srivastava (IEEE Trans. Pattern Anal. Mach. Intell., 2017)

Lose the Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion. - Rushil Anirudh; Hyojin Kim 001; Jayaraman J. Thiagarajan; K. Aditya Mohan; Kyle Champley; Timo Bremer (CVPR, 2018)

Bootstrapping Parameter Space Exploration for Fast Tuning. - Jayaraman J. Thiagarajan; Nikhil Jain; Rushil Anirudh; Alfredo Giménez; Rahul Sridhar; Aniruddha Marathe; Tao Wang 077; Murali Emani; Abhinav Bhatele; Todd Gamblin (ICS, 2018)

PADDLE: Performance Analysis Using a Data-Driven Learning Environment. - Jayaraman J. Thiagarajan; Rushil Anirudh; Bhavya Kailkhura; Nikhil Jain; Tanzima Z. Islam; Abhinav Bhatele; Jae-Seung Yeom; Todd Gamblin (IPDPS, 2018)

An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks. - Rushil Anirudh; Jayaraman J. Thiagarajan; Bhavya Kailkhura; Timo Bremer (CoRR, 2018)

Unsupervised Dimension Selection using a Blue Noise Spectrum. - Jayaraman J. Thiagarajan; Rushil Anirudh; Rahul Sridhar; Peer-Timo Bremer (CoRR, 2018)

MimicGAN: Corruption-Mimicking for Blind Image Recovery & Adversarial Defense. - Rushil Anirudh; Jayaraman J. Thiagarajan; Bhavya Kailkhura; Timo Bremer (CoRR, 2018)

MR-GAN: Manifold Regularized Generative Adversarial Networks. - Qunwei Li; Bhavya Kailkhura; Rushil Anirudh; Yi Zhou 017; Yingbin Liang; Pramod K. Varshney (CoRR, 2018)

Parallelizing Training of Deep Generative Models on Massive Scientific Datasets. - 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; et al. (CLUSTER, 2019)

Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification. - Rushil Anirudh; Jayaraman J. Thiagarajan (ICASSP, 2019)

Unsupervised Dimension Selection Using a Blue Noise Graph Spectrum. - Jayaraman J. Thiagarajan; Rushil Anirudh; Rahul Sridhar; Peer-Timo Bremer (ICASSP, 2019)

Understanding Deep Neural Networks through Input Uncertainties. - Jayaraman J. Thiagarajan; Irene Kim; Rushil Anirudh; Peer-Timo Bremer (ICASSP, 2019)

Multiple Subspace Alignment Improves Domain Adaptation. - Kowshik Thopalli; Rushil Anirudh; Jayaraman J. Thiagarajan; Pavan K. Turaga (ICASSP, 2019)

SALT: Subspace Alignment as an Auxiliary Learning Task for Domain Adaptation. - Kowshik Thopalli; Jayaraman J. Thiagarajan; Rushil Anirudh; Pavan K. Turaga (CoRR, 2019)

Function Preserving Projection for Scalable Exploration of High-Dimensional Data. - Shusen Liu 001; Rushil Anirudh; Jayaraman J. Thiagarajan; Peer-Timo Bremer (CoRR, 2019)

Improving Limited Angle CT Reconstruction with a Robust GAN Prior. - Rushil Anirudh; Hyojin Kim 001; Jayaraman J. Thiagarajan; K. Aditya Mohan; Kyle Champley (CoRR, 2019)

Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion. - Rushil Anirudh; Jayaraman J. Thiagarajan; Shusen Liu 001; Peer-Timo Bremer; Brian K. Spears (CoRR, 2019)

Extreme Few-view CT Reconstruction using Deep Inference. - Hyojin Kim 001; Rushil Anirudh; K. Aditya Mohan; Kyle Champley (CoRR, 2019)

Merlin: Enabling Machine Learning-Ready HPC Ensembles. - 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; et al. (CoRR, 2019)

Rate-Invariant Autoencoding of Time-Series. - Kaushik Koneripalli; Suhas Lohit; Rushil Anirudh; Pavan K. Turaga (ICASSP, 2020)

Unsupervised Audio Source Separation Using Generative Priors. - Vivek Sivaraman Narayanaswamy; Jayaraman J. Thiagarajan; Rushil Anirudh; Andreas Spanias (INTERSPEECH, 2020)

The Case of Performance Variability on Dragonfly-based Systems. - Abhinav Bhatele; Jayaraman J. Thiagarajan; Taylor L. Groves; Rushil Anirudh; Staci A. Smith; Brandon Cook 001; David K. Lowenthal (IPDPS, 2020)

Designing Accurate Emulators for Scientific Processes using Calibration-Driven Deep Models. - Jayaraman J. Thiagarajan; Bindya Venkatesh; Rushil Anirudh; Peer-Timo Bremer; Jim Gaffney; Gemma Anderson; Brian K. Spears (CoRR, 2020)

Meaningful uncertainties from deep neural network surrogates of large-scale numerical simulations. - Gemma J. Anderson; Jim A. Gaffney; Brian K. Spears; Peer-Timo Bremer; Rushil Anirudh; Jayaraman J. Thiagarajan (CoRR, 2020)

MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking. - Rushil Anirudh; Jayaraman J. Thiagarajan; Bhavya Kailkhura; Peer-Timo Bremer (Int. J. Comput. Vis., 2020)

Uncovering interpretable relationships in high-dimensional scientific data through function preserving projections. - Shusen Liu 001; Rushil Anirudh; Jayaraman J. Thiagarajan; Peer-Timo Bremer (Mach. Learn. Sci. Technol., 2020)

Improved surrogates in inertial confinement fusion with manifold and cycle consistencies. - Rushil Anirudh; Jayaraman J. Thiagarajan; Peer-Timo Bremer; Brian K. Spears (Proc. Natl. Acad. Sci. USA, 2020)

Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications. - Shusen Liu 001; 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; et al. (IEEE Trans. Vis. Comput. Graph., 2020)

Attribute-Guided Adversarial Training for Robustness to Natural Perturbations. - Tejas Gokhale; Rushil Anirudh; Bhavya Kailkhura; Jayaraman J. Thiagarajan; Chitta Baral; Yezhou Yang (AAAI, 2021)

Accurate and Robust Feature Importance Estimation under Distribution Shifts. - Jayaraman J. Thiagarajan; Vivek Sivaraman Narayanaswamy; Rushil Anirudh; Peer-Timo Bremer; Andreas Spanias (AAAI, 2021)

Dynamic CT Reconstruction from Limited Views with Implicit Neural Representations and Parametric Motion Fields. - Albert W. Reed; Hyojin Kim 001; Rushil Anirudh; K. Aditya Mohan; Kyle Champley; Jingu Kang; Suren Jayasuriya (ICCV, 2021)

Data-Driven Estimation of Temporal-Sampling Errors in Unsteady Flows. - Harsh Bhatia; Steve Petruzza; Rushil Anirudh; Attila Gyulassy; Robert M. Kirby; Valerio Pascucci; Peer-Timo Bremer (ISVC (1), 2021)

Generative Patch Priors for Practical Compressive Image Recovery. - Rushil Anirudh; Suhas Lohit; Pavan K. Turaga (WACV, 2021)

Recovering Trajectories of Unmarked Joints in 3D Human Actions Using Latent Space Optimization. - Suhas Lohit; Rushil Anirudh; Pavan K. Turaga (WACV, 2021)

Transfer learning suppresses simulation bias in predictive models built from sparse, multi-modal data. - Bogdan Kustowski; Jim A. Gaffney; Brian K. Spears; Gemma J. Anderson; Rushil Anirudh; Peer-Timo Bremer; Jayaraman J. Thiagarajan (CoRR, 2021)

Δ-UQ: Accurate Uncertainty Quantification via Anchor Marginalization. - Rushil Anirudh; Jayaraman J. Thiagarajan (CoRR, 2021)

Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion. - Ankita Shukla; Rushil Anirudh; Eugene Kur; Jayaraman J. Thiagarajan; Peer-Timo Bremer; Brian K. Spears; Tammy Ma; Pavan K. Turaga (CoRR, 2021)

MARGIN: Uncovering Deep Neural Networks Using Graph Signal Analysis. - Rushil Anirudh; Jayaraman J. Thiagarajan; Rahul Sridhar; Peer-Timo Bremer (Frontiers Big Data, 2021)

MR-GAN: Manifold Regularized Generative Adversarial Networks for Scientific Data. - Qunwei Li; Bhavya Kailkhura; Rushil Anirudh; Jize Zhang; Yi Zhou 017; Yingbin Liang; Thomas Yong-Jin Han; Pramod K. Varshney (SIAM J. Math. Data Sci., 2021)

Out of Distribution Detection via Neural Network Anchoring. - Rushil Anirudh; Jayaraman J. Thiagarajan (ACML, 2022)

Intracardiac Electrical Imaging Using the 12-Lead ECG: A Machine Learning Approach Using Synthetic Data. - Mikel Landajuela; Rushil Anirudh; Joe Loscazo; Robert Blake (CinC, 2022)

Sparsity Improves Unsupervised Attribute Discovery in Stylegan. - Shusen Liu 001; Rushil Anirudh; Jayaraman J. Thiagarajan; Peer-Timo Bremer (ICASSP, 2022)

Predicting the Generalization Gap in Deep Models using Anchoring. - Vivek Sivaraman Narayanaswamy; Rushil Anirudh; Irene Kim; Yamen Mubarka; Andreas Spanias; Jayaraman J. Thiagarajan (ICASSP, 2022)

Accurate Calibration of Agent-based Epidemiological Models with Neural Network Surrogates. - Rushil Anirudh; Jayaraman J. Thiagarajan; Peer-Timo Bremer; Timothy C. Germann; Sara Y. Del Valle; Frederick H. Streitz (Healthcare AI and COVID-19 Workshop, 2022)

Machine Learning-Powered Mitigation Policy Optimization in Epidemiological Models. - Jayaraman J. Thiagarajan; Rushil Anirudh; Peer-Timo Bremer; Timothy C. Germann; Sara Y. Del Valle; Frederick H. Streitz (Healthcare AI and COVID-19 Workshop, 2022)

Single Model Uncertainty Estimation via Stochastic Data Centering. - Jayaraman J. Thiagarajan; Rushil Anirudh; Vivek Sivaraman Narayanaswamy; Timo Bremer (NeurIPS, 2022)

Revisiting Deep Subspace Alignment for Unsupervised Domain Adaptation. - Kowshik Thopalli; Jayaraman J. Thiagarajan; Rushil Anirudh; Pavan K. Turaga (CoRR, 2022)

Revisiting Inlier and Outlier Specification for Improved Out-of-Distribution Detection. - Vivek Sivaraman Narayanaswamy; Yamen Mubarka; Rushil Anirudh; Deepta Rajan; Andreas Spanias; Jayaraman J. Thiagarajan (CoRR, 2022)

On-the-fly Object Detection using StyleGAN with CLIP Guidance. - Yuzhe Lu; Shusen Liu 001; Jayaraman J. Thiagarajan; Wesam Sakla; Rushil Anirudh (CoRR, 2022)

Enabling machine learning-ready HPC ensembles with Merlin. - 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; et al. (Future Gener. Comput. Syst., 2022)

A biology-informed similarity metric for simulated patches of human cell membrane. - Harsh Bhatia; Jayaraman J. Thiagarajan; Rushil Anirudh; T. S. Jayram; Tomas Oppelstrup; Helgi I. Ingólfsson; Felice C. Lightstone; Peer-Timo Bremer (Mach. Learn. Sci. Technol., 2022)

Suppressing simulation bias in multi-modal data using transfer learning. - 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 (Mach. Learn. Sci. Technol., 2022)

Cross-GAN Auditing: Unsupervised Identification of Attribute Level Similarities and Differences Between Pretrained Generative Models. - Matthew L. Olson; Shusen Liu 001; Rushil Anirudh; Jayaraman J. Thiagarajan; Peer-Timo Bremer; Weng-Keen Wong (CVPR, 2023)

Robust Time Series Recovery and Classification Using Test-Time Noise Simulator Networks. - Eun Som Jeon; Suhas Lohit; Rushil Anirudh; Pavan K. Turaga (ICASSP, 2023)

DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction. - Jiaming Liu 001; Rushil Anirudh; Jayaraman J. Thiagarajan; Stewart He; K. Aditya Mohan; Ulugbek S. Kamilov; Hyojin Kim 001 (ICCV, 2023)

Exploring Inlier and Outlier Specification for Improved Medical OOD Detection. - Vivek Sivaraman Narayanaswamy; Yamen Mubarka; Rushil Anirudh; Deepta Rajan; Jayaraman J. Thiagarajan (ICCV (Workshops), 2023)

Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors. - Vivek Sivaraman Narayanaswamy; Yamen Mubarka; Rushil Anirudh; Deepta Rajan; Andreas Spanias; Jayaraman J. Thiagarajan (MIDL, 2023)

Improving Diversity with Adversarially Learned Transformations for Domain Generalization. - Tejas Gokhale; Rushil Anirudh; Jayaraman J. Thiagarajan; Bhavya Kailkhura; Chitta Baral; Yezhou Yang (WACV, 2023)

Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification. - Rakshith Subramanyam; Mark Heimann; T. S. Jayram; Rushil Anirudh; Jayaraman J. Thiagarajan (WACV, 2023)

The Surprising Effectiveness of Deep Orthogonal Procrustes Alignment in Unsupervised Domain Adaptation. - Kowshik Thopalli; Rushil Anirudh; Pavan K. Turaga; Jayaraman J. Thiagarajan (IEEE Access, 2023)

CREPE: Learnable Prompting With CLIP Improves Visual Relationship Prediction. - Rakshith Subramanyam; T. S. Jayram; Rushil Anirudh; Jayaraman J. Thiagarajan (CoRR, 2023)

PAGER: A Framework for Failure Analysis of Deep Regression Models. - Jayaraman J. Thiagarajan; Vivek Sivaraman Narayanaswamy; Puja Trivedi; Rushil Anirudh (CoRR, 2023)

Transformer-Powered Surrogates Close the ICF Simulation-Experiment Gap with Extremely Limited Data. - Matthew L. Olson; Shusen Liu 001; Jayaraman J. Thiagarajan; Bogdan Kustowski; Weng-Keen Wong; Rushil Anirudh (CoRR, 2023)

'Eyes of a Hawk and Ears of a Fox': Part Prototype Network for Generalized Zero-Shot Learning. - Joshua Feinglass; Jayaraman J. Thiagarajan; Rushil Anirudh; T. S. Jayram; Yezhou Yang (CVPR Workshops, 2024)

The Double-Edged Sword Of Ai Safety: Balancing Anomaly Detection and OOD Generalization Via Model Anchoring. - Vivek Sivaraman Narayanaswamy; Rushil Anirudh; Jayaraman J. Thiagarajan (ICASSP, 2024)

Exploring the Utility of Clip Priors for Visual Relationship Prediction. - Rakshith Subramanyam; T. S. Jayram; Rushil Anirudh; Jayaraman J. Thiagarajan (ICASSP, 2024)

Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks. - Puja Trivedi; Mark Heimann; Rushil Anirudh; Danai Koutra; Jayaraman J. Thiagarajan (ICLR, 2024)

PAGER: Accurate Failure Characterization in Deep Regression Models. - Jayaraman J. Thiagarajan; Vivek Sivaraman Narayanaswamy; Puja Trivedi; Rushil Anirudh (ICML, 2024)

On the Use of Anchoring for Training Vision Models. - Vivek Sivaraman Narayanaswamy; Kowshik Thopalli; Rushil Anirudh; Yamen Mubarka; Wesam Sakla; Jayaraman J. Thiagarajan (NeurIPS, 2024)