Sparse Representations for Pattern Classification using Learned Dictionaries. - Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Andreas Spanias (SGAI Conf., 2008)

Fast image registration with non-stationary Gauss-Markov random field templates. - Karthikeyan Natesan Ramamurthy; Jayaraman J. Thiagarajan; Andreas Spanias (ICIP, 2009)

Template Learning using Wavelet Domain Statistical Models. - Karthikeyan Natesan Ramamurthy; Jayaraman J. Thiagarajan; Andreas Spanias (SGAI Conf., 2009)

Dimensionality Reduction for Distance Based Video Clustering. - Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Andreas Spanias (AIAI, 2010)

Learning dictionaries for local sparse coding in image classification. - Jayaraman J. Thiagarajan; Andreas Spanias (ACSCC, 2011)

Work in progress - Interactive signal-processing labs and simulations on iOS devices. - Jinru Liu; Andreas Spanias; Mahesh K. Banavar; Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Shuang Hu; Xue Zhang 002 (FIE, 2011)

Work in progress - Modules and laboratories for a pathways course in signals and systems. - Kostas Tsakalis; Jayaraman J. Thiagarajan; Tolga M. Duman; Martin Reisslein; G. Tong Zhou; Xiaoli Ma; Photini Spanias (FIE, 2011)

Analyte detection using an ion-channel sensor array. - Prasanna Sattigeri; Karthikeyan Natesan Ramamurthy; Jayaraman J. Thiagarajan; Michael Goryll; Andreas Spanias; Trevor Thornton (DSP, 2011)

Improved sparse coding using manifold projections. - Karthikeyan Natesan Ramamurthy; Jayaraman J. Thiagarajan; Andreas Spanias (ICIP, 2011)

Transform domain features for ion-channel signal classification. - Karthikeyan Natesan Ramamurthy; Jayaraman J. Thiagarajan; Prasanna Sattigeri; Michael Goryll; Andreas Spanias; Trevor Thornton; Stephen M. Phillips (Biomed. Signal Process. Control., 2011)

Optimality and stability of the K-hyperline clustering algorithm. - Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Andreas Spanias (Pattern Recognit. Lett., 2011)

Analysis of the MPEG-1 Layer III (MP3) Algorithm Using MATLAB - Jayaraman J. Thiagarajan; Andreas Spanias (Morgan & Claypool Publishers, 2011)

Learning dictionaries with graph embedding constraints. - Karthikeyan Natesan Ramamurthy; Jayaraman J. Thiagarajan; Prasanna Sattigeri; Andreas Spanias (ACSCC, 2012)

Automated tumor segmentation using kernel sparse representations. - Jayaraman J. Thiagarajan; Deepta Rajan; Karthikeyan Natesan Ramamurthy; David H. Frakes; Andreas Spanias (BIBE, 2012)

Implementation of a fast image coding and retrieval system using a GPU. - Prasanna Sattigeri; Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Andreas Spanias (ESPA, 2012)

Work in progress: Performing signal analysis laboratories using Android devices. - Suhas Ranganath; Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Shuang Hu; Mahesh K. Banavar; Andreas Spanias (FIE, 2012)

Interactive DSP laboratories on mobile phones and tablets. - Jinru Liu; Shuang Hu; Jayaraman J. Thiagarajan; Xue Zhang 002; Suhas Ranganath; Mahesh K. Banavar; Andreas Spanias (ICASSP, 2012)

Supervised local sparse coding of sub-image features for image retrieval. - Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Prasanna Sattigeri; Andreas Spanias (ICIP, 2012)

Learning Multilevel Dictionaries for Compressed Sensing Using Discriminative Clustering. - Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Andreas Spanias; Panos Nasiopoulos (IIH-MSP, 2012)

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)

Boosted dictionaries for image restoration based on sparse representations. - Karthikeyan Natesan Ramamurthy; Jayaraman J. Thiagarajan; Andreas Spanias; Prasanna Sattigeri (ICASSP, 2013)

Ensemble Sparse Models for Image Analysis - Karthikeyan Natesan Ramamurthy; Jayaraman J. Thiagarajan; Prasanna Sattigeri; Andreas Spanias (CoRR, 2013)

Learning Stable Multilevel Dictionaries for Sparse Representation of Images - Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Andreas Spanias (CoRR, 2013)

Mixing matrix estimation using discriminative clustering for blind source separation. - Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Andreas Spanias (Digit. Signal Process., 2013)

Sparse Methods in Image Understanding and Computer Vision. - Jayaraman J. Thiagarajan (2013)

Consensus inference with multilayer graphs for multi-modal data. - Karthikeyan Natesan Ramamurthy; Jayaraman J. Thiagarajan; Rahul Sridhar; Premnishanth Kothandaraman; Ramanathan Nachiappan (ACSSC, 2014)

A scalable feature learning and tag prediction framework for natural environment sounds. - Prasanna Sattigeri; Jayaraman J. Thiagarajan; Mohit Shah; Karthikeyan Natesan Ramamurthy; Andreas Spanias (ACSSC, 2014)

Multiple kernel interpolation for inverting non-linear dimensionality reduction and dimension estimation. - Jayaraman J. Thiagarajan; Peer-Timo Bremer; Karthikeyan Natesan Ramamurthy (ICASSP, 2014)

Image segmentation using consensus from hierarchical segmentation ensembles. - Hyojin Kim 001; Jayaraman J. Thiagarajan; Peer-Timo Bremer (ICIP, 2014)

Automatic image annotation using inverse maps from semantic embeddings. - Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Prasanna Sattigeri; Peer-Timo Bremer; Andreas Spanias (ICIP, 2014)

Multivariate volume visualization through dynamic projections. - Shusen Liu 001; Bei Wang 001; Jayaraman J. Thiagarajan; Peer-Timo Bremer; Valerio Pascucci (LDAV, 2014)

Computing persistent homology under random projection. - Karthikeyan Natesan Ramamurthy; Kush R. Varshney; Jayaraman J. Thiagarajan (SSP, 2014)

Recovering non-negative and combined sparse representations. - Karthikeyan Natesan Ramamurthy; Jayaraman J. Thiagarajan; Andreas Spanias (Digit. Signal Process., 2014)

Kernel Sparse Models for Automated Tumor Segmentation. - Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Deepta Rajan; Andreas Spanias; Anup Puri; David H. Frakes (Int. J. Artif. Intell. Tools, 2014)

Multiple Kernel Sparse Representations for Supervised and Unsupervised Learning. - Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Andreas Spanias (IEEE Trans. Image Process., 2014)

Image Understanding Using Sparse Representations - Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Pavan K. Turaga; Andreas Spanias (Morgan & Claypool Publishers, 2014)

Subspace learning using consensus on the grassmannian manifold. - Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy (ICASSP, 2015)

A Randomized Ensemble Approach to Industrial CT Segmentation. - Hyojin Kim 001; Jayaraman J. Thiagarajan; Peer-Timo Bremer (ICCV, 2015)

Identifying the Culprits Behind Network Congestion. - Abhinav Bhatele; Andrew R. Titus; Jayaraman J. Thiagarajan; Nikhil Jain; Todd Gamblin; Peer-Timo Bremer; Martin Schulz 001; Laxmikant V. Kalé (IPDPS, 2015)

Visual Exploration of High-Dimensional Data through Subspace Analysis and Dynamic Projections. - Shusen Liu 001; Bei Wang 001; Jayaraman J. Thiagarajan; Peer-Timo Bremer; Valerio Pascucci (Comput. Graph. Forum, 2015)

Undergraduate Signal Processing Laboratories for the Android Operating System. - Suhas Ranganath; Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Shuang Hu; Mahesh K. Banavar; Andreas Spanias (CoRR, 2015)

Learning Stable Multilevel Dictionaries for Sparse Representations. - Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Andreas Spanias (IEEE Trans. Neural Networks Learn. Syst., 2015)

Theoretical guarantees for poisson disk sampling using pair correlation function. - Bhavya Kailkhura; Jayaraman J. Thiagarajan; Peer-Timo Bremer; Pramod K. Varshney (ICASSP, 2016)

Beyond L2-loss functions for learning sparse models. - Karthikeyan Natesan Ramamurthy; Aleksandr Y. Aravkin; Jayaraman J. Thiagarajan (ICASSP, 2016)

Consensus inference on mobile phone sensors for activity recognition. - Huan Song; Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Andreas Spanias; Pavan K. Turaga (ICASSP, 2016)

Robust Local Scaling Using Conditional Quantiles of Graph Similarities. - Jayaraman J. Thiagarajan; Prasanna Sattigeri; Karthikeyan Natesan Ramamurthy; Bhavya Kailkhura (ICDM Workshops, 2016)

Auto-context modeling using multiple Kernel learning. - Huan Song; Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Andreas Spanias (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)

Measuring glomerular number from kidney MRI images. - Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Berkay Kanberoglu; David H. Frakes; Kevin Bennett; Andreas Spanias (Image Processing, 2016)

Influential Node Detection in Implicit Social Networks using Multi-task Gaussian Copula Models. - Qunwei Li; Bhavya Kailkhura; Jayaraman J. Thiagarajan; Zhenliang Zhang; Pramod K. Varshney (NIPS Time Series Workshop, 2016)

Sparsifying Word Representations for Deep Unordered Sentence Modeling. - Prasanna Sattigeri; Jayaraman J. Thiagarajan (Rep4NLP@ACL, 2016)

A machine learning framework for performance coverage analysis of proxy applications. - Tanzima Z. Islam; Jayaraman J. Thiagarajan; Abhinav Bhatele; Martin Schulz 001; Todd Gamblin (SC, 2016)

Data-Driven Performance Modeling of Linear Solvers for Sparse Matrices. - Jae-Seung Yeom; Jayaraman J. Thiagarajan; Abhinav Bhatele; Greg Bronevetsky; Tzanio V. Kolev (PMBS@SC, 2016)

TreeView: Peeking into Deep Neural Networks Via Feature-Space Partitioning. - Jayaraman J. Thiagarajan; Bhavya Kailkhura; Prasanna Sattigeri; Karthikeyan Natesan Ramamurthy (CoRR, 2016)

Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach. - Prashant Khanduri; Bhavya Kailkhura; Jayaraman J. Thiagarajan; Pramod K. Varshney (IEEE Signal Process. Lett., 2016)

Stair blue noise sampling. - Bhavya Kailkhura; Jayaraman J. Thiagarajan; Peer-Timo Bremer; Pramod K. Varshney (ACM Trans. Graph., 2016)

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

A deep learning approach to multiple kernel fusion. - Huan Song; Jayaraman J. Thiagarajan; Prasanna Sattigeri; Karthikeyan Natesan Ramamurthy; Andreas Spanias (ICASSP, 2017)

Learning Robust Representations for Computer Vision. - Peng Zheng 002; Aleksandr Y. Aravkin; Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy (ICCV 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)

Efficient Data-Driven Geologic Feature Detection from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm. - Youzuo Lin; Shusen Wang; Jayaraman J. Thiagarajan; George Guthrie; David Coblentz (CoRR, 2017)

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

Attend and Diagnose: Clinical Time Series Analysis Using Attention Models. - Huan Song; Deepta Rajan; Jayaraman J. Thiagarajan; Andreas Spanias (AAAI, 2018)

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)

A Generative Modeling Approach to Limited Channel ECG Classification. - Deepta Rajan; Jayaraman J. Thiagarajan (EMBC, 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)

Triplet Network with Attention for Speaker Diarization. - Huan Song; Megan M. Willi; Jayaraman J. Thiagarajan; Visar Berisha; Andreas Spanias (INTERSPEECH, 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)

Mitigating inter-job interference using adaptive flow-aware routing. - Staci A. Smith; Clara E. Cromey; David K. Lowenthal; Jens Domke; Nikhil Jain; Jayaraman J. Thiagarajan; Abhinav Bhatele (SC, 2018)

Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections. - Jayaraman J. Thiagarajan; Shusen Liu 001; Karthikeyan Natesan Ramamurthy; Peer-Timo Bremer (Comput. Graph. Forum, 2018)

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

Controlled Random Search Improves Sample Mining and Hyper-Parameter Optimization. - Gowtham Muniraju; Bhavya Kailkhura; Jayaraman J. Thiagarajan; Peer-Timo Bremer (CoRR, 2018)

Can Deep Clinical Models Handle Real-World Domain Shifts? - Jayaraman J. Thiagarajan; Deepta Rajan; Prasanna Sattigeri (CoRR, 2018)

Attention Models with Random Features for Multi-layered Graph Embeddings. - Uday Shankar Shanthamallu; Jayaraman J. Thiagarajan; Huan Song; Andreas Spanias (CoRR, 2018)

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

Improving Robustness of Attention Models on Graphs. - Uday Shankar Shanthamallu; Jayaraman J. Thiagarajan; Andreas Spanias (CoRR, 2018)

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

Improved Community Detection using Deep Embeddings from Multilayer Graphs. - Huan Song; Jayaraman J. Thiagarajan (CoRR, 2018)

A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms. - Bhavya Kailkhura; Jayaraman J. Thiagarajan; Charvi Rastogi; Pramod K. Varshney; Peer-Timo Bremer (J. Mach. Learn. Res., 2018)

Optimizing Kernel Machines Using Deep Learning. - Huan Song; Jayaraman J. Thiagarajan; Prasanna Sattigeri; Andreas Spanias (IEEE Trans. Neural Networks Learn. Syst., 2018)

Visual Exploration of Semantic Relationships in Neural Word Embeddings. - Shusen Liu 001; Peer-Timo Bremer; Jayaraman J. Thiagarajan; Vivek Srikumar; Bei Wang 001; Yarden Livnat; Valerio Pascucci (IEEE Trans. Vis. Comput. Graph., 2018)

Improved Deep Embeddings for Inferencing with Multi-Layered Graphs. - Huan Song; Jayaraman J. Thiagarajan (IEEE BigData, 2019)

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)

Designing an Effective Metric Learning Pipeline for Speaker Diarization. - Vivek Sivaraman Narayanaswamy; Jayaraman J. Thiagarajan; Huan Song; Andreas Spanias (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)

Distill-to-Label: Weakly Supervised Instance Labeling Using Knowledge Distillation. - Jayaraman J. Thiagarajan; Satyananda Kashyap; Alexandros Karargyris (ICMLA, 2019)

Performance optimality or reproducibility: that is the question. - Tapasya Patki; Jayaraman J. Thiagarajan; Alexis Ayala; Tanzima Z. Islam (SC, 2019)

Audio Source Separation via Multi-Scale Learning with Dilated Dense U-Nets. - Vivek Sivaraman Narayanaswamy; Sameeksha Katoch; Jayaraman J. Thiagarajan; Huan Song; Andreas Spanias (CoRR, 2019)

A Look at the Effect of Sample Design on Generalization through the Lens of Spectral Analysis. - Bhavya Kailkhura; Jayaraman J. Thiagarajan; Qunwei Li; Peer-Timo Bremer (CoRR, 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)

Heteroscedastic Calibration of Uncertainty Estimators in Deep Learning. - Bindya Venkatesh; Jayaraman J. Thiagarajan (CoRR, 2019)

Invenio: Discovering Hidden Relationships Between Tasks/Domains Using Structured Meta Learning. - Sameeksha Katoch; Kowshik Thopalli; Jayaraman J. Thiagarajan; Pavan K. Turaga; Andreas Spanias (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)

Machine Learning Meets Visualization to Make Artificial Intelligence Interpretable (Dagstuhl Seminar 19452). - Enrico Bertini; Peer-Timo Bremer; Daniela Oelke; Jayaraman J. Thiagarajan (Dagstuhl Reports, 2019)

Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors. - Jayaraman J. Thiagarajan; Bindya Venkatesh; Prasanna Sattigeri; Peer-Timo Bremer (AAAI, 2020)

Treeview and Disentangled Representations for Explaining Deep Neural Networks Decisions. - Prasanna Sattigeri; Karthikeyan Natesan Ramamurthy; Jayaraman J. Thiagarajan; Bhavya Kailkhura (ACSSC, 2020)

A Regularized Attention Mechanism for Graph Attention Networks. - Uday Shankar Shanthamallu; Jayaraman J. Thiagarajan; Andreas Spanias (ICASSP, 2020)

Learn-By-Calibrating: Using Calibration As A Training Objective. - Jayaraman J. Thiagarajan; Bindya Venkatesh; Deepta Rajan (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)

Improving Reliability of Clinical Models Using Prediction Calibration. - Jayaraman J. Thiagarajan; Bindya Venkatesh; Deepta Rajan; Prasanna Sattigeri (UNSURE/GRAIL@MICCAI, 2020)

A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning. - Bhavya Kailkhura; Jayaraman J. Thiagarajan; Qunwei Li; Jize Zhang; Yi Zhou; Timo Bremer (NeurIPS, 2020)

Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration. - Bindya Venkatesh; Jayaraman J. Thiagarajan; Kowshik Thopalli; Prasanna Sattigeri (CoRR, 2020)

Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models. - Jayaraman J. Thiagarajan; Prasanna Sattigeri; Deepta Rajan; Bindya Venkatesh (CoRR, 2020)

Self-Training with Improved Regularization for Few-Shot Chest X-Ray Classification. - Deepta Rajan; Jayaraman J. Thiagarajan; Alexandros Karargyris; Satyananda Kashyap (CoRR, 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)

Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification. - Bindya Venkatesh; Jayaraman J. Thiagarajan (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)

GrAMME: Semisupervised Learning Using Multilayered Graph Attention Models. - Uday Shankar Shanthamallu; Jayaraman J. Thiagarajan; Huan Song; Andreas Spanias (IEEE Trans. Neural Networks Learn. Syst., 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)

Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks. - Uday Shankar Shanthamallu; Jayaraman J. Thiagarajan; Andreas Spanias (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)

College Life is Hard! - Shedding Light on Stress Prediction for Autistic College Students using Data-Driven Analysis. - Tanzima Z. Islam; Philip Wu Liang; Forest Sweeney; Cody Pranger; Jayaraman J. Thiagarajan; Moushumi Sharmin; Shameem Ahmed (COMPSAC, 2021)

Using Deep Image Priors to Generate Counterfactual Explanations. - Vivek Sivaraman Narayanaswamy; Jayaraman J. Thiagarajan; Andreas Spanias (ICASSP, 2021)

Deep Learning with hyper-parameter tuning for COVID-19 Cough Detection. - Sunil Rao; Vivek Sivaraman Narayanaswamy; Michael Esposito; Jayaraman J. Thiagarajan; Andreas Spanias (IISA, 2021)

On the Design of Deep Priors for Unsupervised Audio Restoration. - Vivek Sivaraman Narayanaswamy; Jayaraman J. Thiagarajan; Andreas Spanias (Interspeech, 2021)

Comparative Code Structure Analysis using Deep Learning for Performance Prediction. - Tarek Ramadan; Tanzima Z. Islam; Chase Phelps; Nathan Pinnow; Jayaraman J. Thiagarajan (ISPASS, 2021)

Self-training with improved regularization for sample-efficient chest x-ray classification. - Deepta Rajan; Jayaraman J. Thiagarajan; Alexandros Karargyris; Satyananda Kashyap (Computer-Aided Diagnosis, 2021)

Designing Counterfactual Generators using Deep Model Inversion. - Jayaraman J. Thiagarajan; Vivek Sivaraman Narayanaswamy; Deepta Rajan; Jason Liang; Akshay Chaudhari; Andreas Spanias (NeurIPS, 2021)

Loss Estimators Improve Model Generalization. - Vivek Sivaraman Narayanaswamy; Jayaraman J. Thiagarajan; Deepta Rajan; Andreas Spanias (CoRR, 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)

MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation. - 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; et al. (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)

Improving Multi-Domain Generalization through Domain Re-labeling. - Kowshik Thopalli; Sameeksha Katoch; Andreas Spanias; Pavan K. Turaga; Jayaraman J. Thiagarajan (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)

Preventing Failures by Dataset Shift Detection in Safety-Critical Graph Applications. - Hoseung Song; Jayaraman J. Thiagarajan; Bhavya Kailkhura (Frontiers Artif. Intell., 2021)

COVID-19 detection using cough sound analysis and deep learning algorithms. - Sunil Rao; Vivek Sivaraman Narayanaswamy; Michael Esposito; Jayaraman J. Thiagarajan; Andreas Spanias (Intell. Decis. Technol., 2021)

Coverage-Based Designs Improve Sample Mining and Hyperparameter Optimization. - Gowtham Muniraju; Bhavya Kailkhura; Jayaraman J. Thiagarajan; Peer-Timo Bremer; Cihan Tepedelenlioglu; Andreas Spanias (IEEE Trans. Neural Networks Learn. Syst., 2021)

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

Domain Alignment Meets Fully Test-Time Adaptation. - Kowshik Thopalli; Pavan K. Turaga; Jayaraman J. Thiagarajan (ACML, 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)

Improved StyleGAN-v2 based Inversion for Out-of-Distribution Images. - Rakshith Subramanyam; Vivek Sivaraman Narayanaswamy; Mark Naufel; Andreas Spanias; Jayaraman J. Thiagarajan (ICML, 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)

Analyzing Data-Centric Properties for Graph Contrastive Learning. - Puja Trivedi; Ekdeep Singh Lubana; Mark Heimann; Danai Koutra; Jayaraman J. Thiagarajan (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)

Exploring the Design of Adaptation Protocols for Improved Generalization and Machine Learning Safety. - Puja Trivedi; Danai Koutra; Jayaraman J. Thiagarajan (CoRR, 2022)

Analyzing Data-Centric Properties for Contrastive Learning on Graphs. - Puja Trivedi; Ekdeep Singh Lubana; Mark Heimann; Danai Koutra; Jayaraman J. Thiagarajan (CoRR, 2022)

Single-Shot Domain Adaptation via Target-Aware Generative Augmentation. - Rakshith Subramanyam; Kowshik Thopalli; Spring Berman; Pavan K. Turaga; 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)

Improving Single-Stage Object Detectors for Nighttime Pedestrian Detection. - Kowshik Thopalli; Jayaraman J. Thiagarajan (Int. J. Pattern Recognit. Artif. Intell., 2022)

Instruction Tools for Signal Processing and Machine Learning for Ion-Channel Sensors. - 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; et al. (Int. J. Virtual Pers. Learn. Environ., 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)

Single-Shot Domain Adaptation via Target-Aware Generative Augmentations. - Rakshith Subramanyam; Kowshik Thopalli; Spring Berman; Pavan K. Turaga; Jayaraman J. Thiagarajan (ICASSP, 2023)

A Closer Look At Scoring Functions And Generalization Prediction. - Puja Trivedi; Danai Koutra; Jayaraman J. Thiagarajan (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)

InterAug: A Tuning-Free Augmentation Policy for Data-Efficient and Robust Object Detection. - Kowshik Thopalli; Devi S; Jayaraman J. Thiagarajan (ICCV (Workshops), 2023)

A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias. - Puja Trivedi; Danai Koutra; Jayaraman J. Thiagarajan (ICLR, 2023)

Target-Aware Generative Augmentations for Single-Shot Adaptation. - Kowshik Thopalli; Rakshith Subramanyam; Pavan K. Turaga; Jayaraman J. Thiagarajan (ICML, 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)

Improving Object Detectors by Exploiting Bounding Boxes for Augmentation Design. - S. Devi; Kowshik Thopalli; R. Dayana; P. Malarvezhi; Jayaraman J. Thiagarajan (IEEE Access, 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)

Federated benchmarking of medical artificial intelligence with MedPerf. - 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; et al. (Nat. Mac. Intell., 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)

DECIDER: Leveraging Foundation Model Priors for Improved Model Failure Detection and Explanation. - Rakshith Subramanyam; Kowshik Thopalli; Vivek Sivaraman Narayanaswamy; Jayaraman J. Thiagarajan (ECCV (79), 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)

On Estimating Link Prediction Uncertainty Using Stochastic Centering. - Puja Trivedi; Danai Koutra; 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)

Speeding Up Image Classifiers with Little Companions. - Yang Liu; Kowshik Thopalli; Jayaraman J. Thiagarajan (CoRR, 2024)

Enhancing Accuracy and Parameter-Efficiency of Neural Representations for Network Parameterization. - Hongjun Choi; Jayaraman J. Thiagarajan; Ruben Glatt; Shusen Liu 001 (CoRR, 2024)

The Surprising Utility of Group Partitioning in Improving Conformal Prediction of Visual Classifiers under Distributional Shifts. - Kowshik Thopalli; Vivek Sivaraman Narayanaswamy; Jayaraman J. Thiagarajan (CVPR Workshops, 2025)

On The Role of Prompt Construction In Enhancing Efficacy and Efficiency of LLM-Based Tabular Data Generation. - Banooqa H. Banday; Kowshik Thopalli; Tanzima Z. Islam; Jayaraman J. Thiagarajan (ICASSP, 2025)

Leveraging Registers in Vision Transformers for Robust Adaptation. - Srikar Yellapragada; Kowshik Thopalli; Vivek Sivaraman Narayanaswamy; Wesam Sakla; Yang Liu; Yamen Mubarka; Dimitris Samaras; Jayaraman J. Thiagarajan (ICASSP, 2025)

Conformal Edge-Weight Prediction in Latent Space. - Akash Choudhuri; Yongjian Zhong; Mehrdad Moharrami; Christine Klymko; Mark Heimann; Jayaraman J. Thiagarajan; Bijaya Adhikari (SDM, 2025)

Multi-fidelity Reinforcement Learning Control for Complex Dynamical Systems. - Luning Sun; Xin-Yang Liu; Siyan Zhao; Aditya Grover; Jian-Xun Wang 001; Jayaraman J. Thiagarajan (CoRR, 2025)

Structure-Aware Representation Learning for Effective Performance Prediction. - Tarek Ramadan; Nathan Pinnow; Chase Phelps; Jayaraman J. Thiagarajan; Tanzima Z. Islam (Concurr. Comput. Pract. Exp., 2025)