Publications

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

Journals and Book Chapters

  1. (new!) 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, Under Revision, July 2020. [arXiv]
  2. (new!) 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]
  3. 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]
  4. 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]
  5. Turaga, P., Anirudh, R., Chellappa, R., Manifold Learning, in Computer Vision. (editor: Katsushi Ikeuchi) Springer, Feb 2020. [Chapter on Springer]
  6. 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].
  7. 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]
  8. 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]
  9. Shroff, N., Anirudh, R., & Chellappa, R., Summarization and search over geometric spaces. In Riemannian Computing in Computer Vision, 2016.[Springer]

Peer-Reviewed Conference Papers

  1. (new!) Narayanaswamy, V., Thiagarajan, J.J., Anirudh, R., & Spanias, A., Unsupervised Audio Source Separation using Generative Priors, Interspeech 2020. [preprint] [code]
  2. Koneripalli, K., Lohit, S., Anirudh, R., & Turaga, P., Rate-Invariant Autoencoding of Time-Series, in ICASSP 2020. [preprint] [published version]
  3. Anirudh, R., & Thiagarajan, J. J., Bootstrapping graph convolutional neural networks for autism spectrum disorder classification, in ICASSP 2019. [arXiv preprint]
  4. Thiagarajan, J. J., Anirudh, R., Sridhar, R., & Bremer, P. T., Unsupervised Dimension Selection using a Blue Noise Spectrum. in ICASSP 2019. [arXiv preprint].
  5. Thiagarajan, J. J., Kim, I., Anirudh, R., & Bremer, P. T., Understand Deep Neural Networks through Input Uncertainties, in ICASSP 2019 (Oral). [arXiv preprint]
  6. Thopalli, K., Anirudh, R., Thiagarajan, J. J., Turaga, P., Multiple Subspace Alignment Improves Domain Adaptation, in ICASSP 2019. [arXiv preprint]
  7. Thiagarajan, J.J., Jain, N., Anirudh, R., et al., Bootstrapping Parameter Space Exploration for Fast Tuning, to appear in ICS 2018.
  8. 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. 
  9. Thiagarajan, J.J., Anirudh, R.,  et al. PADDLE: Performance Analysis using a Data-driven Learning Environment. to appear in IPDPS 2018.
  10. Marathe, A., Anirudh, R., et al., Performance Modeling under Resource Constraints Using Deep Transfer Learning, SC 2017. [pdf]
  11. Anirudh, R., Masroor, A., & Turaga, P., Diversity promoting online sampling for streaming video summarization, ICIP 2016. [pdf]
  12. 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]
  13. Sivakumar, A., Anirudh, R., & Turaga, P., Geometric Compression of Orientation Signals for Fast Gesture Analysis. DCC 2015. [pdf]
  14. Anirudh, R., Turaga, P., Su, J., & Srivastava, A., Elastic functional coding of human actions: From vector-fields to latent variables. CVPR, 2015. [pdf][code]
  15. Anirudh, R., & Turaga, P., Interactively test driving an object detector: Estimating performance on unlabeled data. WACV, 2014. [pdf]
  16. 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]

Workshop Papers & Manuscripts

  1. (new!) R. Anirudh et al., Generative Patch Priors for Practical Compressive Image Recovery, June 2020. [arXiv preprint] (code/models will be available soon)
  2. H. Kim et al., Extreme Few-view CT Reconstruction using Deep Inference, Deep Inverse NeurIPS 2019 Workshop. [paper]
  3. R. Anirudh et al., Improving Limited Angle CT Reconstruction with a Robust GAN Prior, Deep Inverse NeurIPS 2019 Workshop. [paper]
  4. 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]
  5. 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]
  6. U. S. Shathamallu, Modeling Human Brain Connectomes using Structured Neural Networks, Graph Representation Learning Workshop at NeurIPS 2019. [paper]
  7. R. Anirudh et al., An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks, May 2018. [arxiv preprint]
  8. R. Anirudh*, J. J. Thiagarajan* et al., MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis, Feb 2018. [arxiv preprint]
  9. Anirudh, R., Kailkhura, B., Thiagarajan, J. J., Bremer, T., Poisson Disk Sampling on the Grassmannnian: Applications in Subspace Optimization, CVPR Workshops 2017. [pdf]
  10. Autism Spectrum Disorder Classification using Graph Kernels on Multidimensional Time Series. [arXiv preprint].
  11. Anirudh, R., Venkataraman, V., Natesan Ramamurthy, K., & Turaga, P., A Riemannian framework for statistical analysis of topological persistence diagrams, CVPR Workshops 2016 [pdf]. [code]
  12. Som, A., Anirudh, R., Wang, Q., & Turaga, P., Riemannian geometric approaches for measuring movement quality, CVPR Workshops 2016 [pdf].
  13. Wang, Q., Anirudh, R., & Turaga, P., Temporal Reflection Symmetry of Human Actions: A Riemannian Analysis, BMVC Workshops 2015. [pdf].
  14. Anirudh, R., Venkataraman, V., & Turaga, P., A Generalized Lyapunov Feature for Dynamical Systems on Riemannian Manifolds, BMVC Workshops 2015 [pdf].
  15. 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)