Publications

Selected Publications are listed below, for a comprehensive and up to date list please visit my Google Scholar page.

Preprints and Working Papers

  1. R. Anirudh et al., Accurate Calibration of Agent-based Epidemiological Models with Neural Network Surrogates, 2020. [arXiv preprint]
  2. J. J. Thiagarajan et al., Machine Learning-Powered Mitigation Policy Optimization in Epidemiological Models, 2020. [arXiv preprint]

2021

  1. (new!) Anirudh, R., and Thiagarajan, J. J., Machine Learning Methods for Autism Spectrum Disorder Classification\. To appear as a book chapter in “Neural Engineering Techniques for Autism Spectrum Disorder” (Elsevier), 2021.
  2. (new!) Anirudh, R.*, Thiagarajan, J. J.*, Sridhar, R., Bremer, P. T., MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis, to appear in Frontiers in Big Data-Machine Learning and Artificial Intelligence, 2021. [arxiv preprint]
  3. Gokhale, T., Anirudh, R., Kailkhura, B., Thiagarajan, J. J., Baral, C., Yang, Y., Attribute-guided Adversarial Training for Robustness to Natural Perturbations, AAAI 2021. [arXiv preprint]
  4. Thiagarajan, J. J., Narayanaswamy, V., Anirudh, R., Bremer, P. T., Spanias, A., Accurate and Robust Feature Importance Estimation under Distribution Shifts, AAAI 2021. [arXiv preprint]
  5. Lohit, S., Anirudh, R., Turaga, P., Recovering Trajectories of Unmarked Joints in 3D Human Actions Using Latent Space Optimization, WACV 2021. [pdf]
  6. Anirudh, R., Lohit, S., Turaga, P., Generative Patch Priors for Practical Compressive Image Recovery, WACV 2021. [pdf] [github] [press release] Best Paper Honorable Mention Award!

2020

  1. Thiagarajan, J. J., Venkatesh, B., Anirudh, R., Bremer, P. T., Gaffney, J., Anderson, G., Spears, B. K., Designing Accurate Emulators for Scientific Processes using Calibration-Driven Deep Models, accepted for publication in Nature Communications, November 2020. [paper] Featured in editor’s picks (link)
  2. Narayanaswamy, V., Thiagarajan, J.J., Anirudh, R., & Spanias, A., Unsupervised Audio Source Separation using Generative Priors, Interspeech 2020. [preprint] [code]
  3. Liu, S., Anirudh, R., Thiagarajan, J. J., & Bremer, P. T., Uncovering Interpretable Relationships in High-Dimensional Scientific Data Through Function Preserving Projections, to appear in  Machine Learning: Science and Technology (2020). [paper] [github]
  4. Anirudh, R., Thiagarajan, J. J., Bremer, P. T., Spears, B. K., Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies, Proc. of National Academy of Sciences (PNAS), published online April 2020. [arXiv] [published version] [github] [Slides]
  5. Anirudh, R., Thiagarajan, J. J., Kailkhura, B., Bremer, P. T., MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking, International Journal of Computer Vision (IJCV) Special Issue on GANs, accepted Feb 2020. [published version][arXiv][earlier versions]
  6. Turaga, P., Anirudh, R., Chellappa, R., Manifold Learning, in Computer Vision. (editor: Katsushi Ikeuchi) Springer, Feb 2020. [Chapter on Springer]
  7. Koneripalli, K., Lohit, S., Anirudh, R., & Turaga, P., Rate-Invariant Autoencoding of Time-Series,in ICASSP 2020. [preprint] [published version]

2019

  1. Anirudh, R., & Thiagarajan, J. J., Bootstrapping graph convolutional neural networks for autism spectrum disorder classification, in ICASSP 2019. [arXiv preprint]
  2. Thiagarajan, J. J., Anirudh, R., Sridhar, R., & Bremer, P. T., Unsupervised Dimension Selection using a Blue Noise Spectrum. in ICASSP 2019. [arXiv preprint].
  3. Thiagarajan, J. J., Kim, I., Anirudh, R., & Bremer, P. T., Understand Deep Neural Networks through Input Uncertainties, in ICASSP 2019 (Oral). [arXiv preprint]
  4. Thopalli, K., Anirudh, R., Thiagarajan, J. J., Turaga, P., Multiple Subspace Alignment Improves Domain Adaptation, in ICASSP 2019. [arXiv preprint]
  5. H. Kim et al., Extreme Few-view CT Reconstruction using Deep Inference,Deep Inverse NeurIPS 2019 Workshop. [paper]
  6. R. Anirudh et al., Improving Limited Angle CT Reconstruction with a Robust GAN Prior, Deep Inverse NeurIPS 2019 Workshop. [paper]
  7. R. Anirudh et al., Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion, Machine Learning for Physical Sciences Workshop at NeurIPS 2019. [paper]
  8. V. S. Narayanaswamy et al., Designing Deep Inverse Models for History Matching in Reservoir Simulations, Machine Learning for Physical Sciences Workshop at NeurIPS 2019 [paper]
  9. U. S. Shathamallu, Modeling Human Brain Connectomes using Structured Neural Networks, Graph Representation Learning Workshop at NeurIPS 2019. [paper]

2018

  1. Thiagarajan, J.J., Jain, N., Anirudh, R., et al., Bootstrapping Parameter Space Exploration for Fast Tuning, to appear in ICS 2018.
  2. Anirudh, R., Kim, H., Thiagarajan, J. J., Mohan, K.A., Champley, K., Bremer, T., Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion,  [pdf] [supplementary material]. Spotlight talk (given to ~7% of 3300 submissions) in CVPR, 2018. 
  3. Thiagarajan, J.J., Anirudh, R.,  et al. PADDLE: Performance Analysis using a Data-driven Learning Environment. to appear in IPDPS 2018.

2017

  1. Anirudh, R., Turaga, P., & Srivastava, A., Optimization Problems Associated with Manifold-Valued Curves with Applications in Computer Vision. In Convex Optimization Methods in Imaging Science, 2017. [Springer].
  2. Marathe, A., Anirudh, R., et al., Performance Modeling under Resource Constraints Using Deep Transfer Learning,SC 2017. [pdf]
  3. Anirudh, R., Kailkhura, B., Thiagarajan, J. J., Bremer, T., Poisson Disk Sampling on the Grassmannnian: Applications in Subspace Optimization, CVPR Workshops 2017. [pdf]

<= 2016

  1. Anirudh, R., Turaga, P., Su, J., & Srivastava, A., Elastic Functional Coding of Riemannian Trajectories. Accepted T-PAMI, 2016. — 2016 Impact Factor: 8.329 [arXiv] [IEEE Xplore] [github]
  2. Anirudh, R., & Turaga, P., Geometry-based symbolic approximation for fast sequence matching on manifolds. Accepted at IJCV, 2015 — 2015 Impact Factor: 4.275 [arXiv] [Springer]
  3. Shroff, N., Anirudh, R., & Chellappa, R., Summarization and search over geometric spaces. In Riemannian Computing in Computer Vision, 2016.[Springer]
  4. Anirudh, R., Masroor, A., & Turaga, P., Diversity promoting online sampling for streaming video summarization, ICIP 2016. [pdf]
  5. Anirudh, R., Thiagarajan, J. J., Bremer, T., & Kim, H., Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data. SPIE Medical Imaging, 2016. [pdf]
  6. Sivakumar, A., Anirudh, R., & Turaga, P., Geometric Compression of Orientation Signals for Fast Gesture Analysis. DCC 2015. [pdf]
  7. Anirudh, R., Turaga, P., Su, J., & Srivastava, A., Elastic functional coding of human actions: From vector-fields to latent variables. CVPR, 2015. [pdf][code]
  8. Anirudh, R., & Turaga, P., Interactively test driving an object detector: Estimating performance on unlabeled data. WACV, 2014.[pdf]
  9. Anirudh, R., Ramamurthy, K., Thiagarajan, J. J., Turaga, P., & Spanias, A.,  A heterogeneous dictionary model for representation and recognition of human actions., ICASSP, 2013. [pdf]
  10. Anirudh, R., Venkataraman, V., Natesan Ramamurthy, K., & Turaga, P., A Riemannian framework for statistical analysis of topological persistence diagrams, CVPR Workshops 2016 [pdf]. [code]
  11. Som, A., Anirudh, R., Wang, Q., & Turaga, P., Riemannian geometric approaches for measuring movement quality, CVPR Workshops 2016 [pdf].
  12. Wang, Q., Anirudh, R., & Turaga, P., Temporal Reflection Symmetry of Human Actions: A Riemannian Analysis, BMVC Workshops 2015. [pdf].
  13. Anirudh, R., Venkataraman, V., & Turaga, P., A Generalized Lyapunov Feature for Dynamical Systems on Riemannian Manifolds, BMVC Workshops 2015 [pdf].
  14. Krzyzaniak, M., Anirudh, R., Venkataraman, V., Turaga, P., & Wei, S. X. Towards realtime measurement of connectedness in human movement. In Proceedings of the 2nd International Workshop on Movement and Computing (MOCO), 2015. [pdf]

Other Unpublished Work

  1. Shape Pattern Recognition using the Euclidean Distance Method, Undergraduate Thesis Project, April 2010. (pdf)
  2. Frequency-Domain Adaptive Noise Cancellation.  (DSP Term Project), Nov 2010. (pdf)
  3. An OpenCV Implementation of Supervised Texture Segmentation Using Gabor Filters. (Digital Image Processing Term Project) April 2011. (pdf)
  4. Validation of the cortical homunculus using functional-MRI. (Biomedical Image Processing Term Project), Jan 2012. (pdf)
  5. ceci n’ est pas une code, R. Anirudh, M. Krzyzaniak, A. Faith, S. Lohit, Sep 2015.
    Inspired by the “The Treachery of Images”.
    Displayed on the walls of Stauffer-B at Arizona State University. (original pdf) (on display)