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Thiagarajan J.J., Venkatesh B., and Rajan D. (2020). “Learn-by-Calibrating: Using Calibration as a Training Objective.” IEEE ICASSP.
Thiagarajan J.J., Venkatesh B., Sattigeri P., and Bremer P-T. (2020). “Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors.” 34th AAAI Conference on Artificial Intelligence.
Tipnis U., Abbas K., Tran E., et al. (2020). “Functional Connectome Fingerprint Gradients in Young Adults.” Preprint.
Zhang J., Kailkhura B., and Han T.Y-J. (2020). “Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning.” ICML.
Alom, Z., Taha, T., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, S., Hasan, M., Van Essen, B., Awwal, A., and Asari, V. (2019). “A State-of-the-Art Survey on Deep Learning Theory and Architectures.” Electronics.
Anirudh, R., Kim, H., Thiagarajan, J.J., Mohan, A.K., and Champley, K. (2020). “Improving Limited Angle CT Reconstruction with a Robust GAN Prior.” Neurips 2019: Solving Inverse Problems with Deep Learning Workshop.
Anirudh, R. and Thiagarajan, J.J. (2019). “Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification.” Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing.
Anirudh, R., Thiagarajan, J.J., Liu, S., Bremer, P-T., and Spears, B. (2020). “Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion.” Neurips 2019: Machine Learning and the Physical Sciences Workshop.
Chu, A., Nguyen, D., Talathi, S.S., (…), Stolaroff, J.K., and Giera, B. (2019). “Automated Detection and Sorting of Microencapsulation: via Machine Learning.” Lab on a Chip.
Cong, G., Domeniconi, G., Shapiro, J., Zhou, F., and Chen, B. (2019). “Accelerating Deep Neural Network Training for Action Recognition on a Cluster of GPUs.” Proceedings of the 30th International Symposium on Computer Architecture and High Performance Computing.
Cong, G., Domeniconi, G., Yang, C.-C., Shapiro, J., and Chen, B. (2019). “Video Action Recognition with an Additional End-To-End Trained Temporal Stream.” 2019 IEEE Winter Conference on Applications of Computer Vision.
Deelman, E., Mandal, A., Jiang, M., and Sakellariou, R. (2019). “The Role of Machine Learning in Scientific Workflows.” International Journal of High Performance Computing Applications.
Druzgalski, C., Lapointe, S., Whitesides, R., and McNenly, M. (2020). “Predicting Octane Number from Microscale Flame Dynamics.” Combustion and Flame.
Dryden, N., Maruyama, N., Benson, T., Moon, T., Snir, M., and Van Essen, B. (2019). “Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism.” International Parallel and Distributed Processing Symposium.
Dryden, N., Maruyama, N., Moon, T., (…), Snir, M., and Van Essen, B. (2019). “Aluminum: An Asynchronous, GPU-Aware Communication Library Optimized for Large-Scale Training of Deep Neural Networks on HPC Systems.” Proceedings of Machine Learning in HPC Environments and the International Conference for High Performance Computing, Networking, Storage and Analysis.
Endrei, M., Jin, C., Dinh, M.N., (…), DeRose, L., and de Supinski, B.R. (2019). “Statistical and Machine Learning Models for Optimizing Energy in Parallel Applications.” International Journal of High Performance Computing Applications.
Fan, Y.J. (2019). “Autoencoder Node Saliency: Selecting Relevant Latent Representations.” Pattern Recognition.
Humbird, K.D., Peterson, J.L., and Mcclarren, R.G. (2019). “Deep Neural Network Initialization with Decision Trees.” IEEE Transactions on Neural Networks and Learning Systems.
Kafle, S., Gupta, V., Kailkhura, B., Wimalajeewa, T., and Varshney, P.K. (2019). “Joint Sparsity Pattern Recovery with 1-b Compressive Sensing in Distributed Sensor Networks.” IEEE Transactions on Signal and Information Processing over Networks.
Kailkhura, B., Gallagher, B., Kim, S., Hiszpanski, A., and Han, T.Y-J. (2019). “Reliable and Explainable Machine-Learning Methods for Accelerated Material Discovery.” npj Computational Materials.
Kamath, C. and Fan, Y.J. (2020). “Compressing Unstructured Mesh Data using Spline Fits, Compressed Sensing, and Regression Methods.” 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings 8646678, pp. 316-320.
Kim, S., Kim, H., Yoon, S., Lee, J., Kahou, S., Kashinath, K., and Prabhat, M. (2019). “Deep-Hurricane-Tracker: Tracking and Forecasting Extreme Climate Events using ConvLSTM.” 2019 IEEE Winter Conference on Applications of Computer Vision.
Leach, W., Henrikson, J., Hatarik, R., (…), Palmer, N., and Rever, M. (2019). “Using Convolutional Neural Networks to Classify Static X-ray Imager Diagnostic Data at the National Ignition Facility.” Proceedings of the International Society for Optical Engineering.
Maiti, A. (2019). “Second-Order Statistical Bootstrap for the Uncertainty Quantification of Time-temperature-superposition Analysis.” Rheologica Acta.
Narayanaswamy, V., Thiagarajan, J.J., Anirudh, R., Forouzanfar, F., Bremer, P-T., and Wu, X-H. (2020). “Designing Deep Inverse Models for History Matching in Reservoir Simulations.” Neurips 2019: Machine Learning and the Physical Sciences Workshop.
Narayanaswamy, V.S., Thiagarajan, J.J., Song, H., and Spanias, A. (2019). “Designing an Effective Metric Learning Pipeline for Speaker Diarization.” Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing.
Nathan, E., Sanders, G., and Henson, V.E. (2019). “Personalized Ranking in Dynamic Graphs using Nonbacktracking Walks.” Lecture Notes in Computer Science, including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics.
Patki, T., Thiagarajan, J.J., Ayala, A., and Islam, T. (2020). “Performance Optimality or Reproducibility: That Is the Question.” Supercomputing 2019.
Petersen, B.K., Yang, J., Grathwohl, W.S., (…), An, G., and Faissol, D.M. (2019). “Deep Reinforcement Learning and Simulation as a Path toward Precision Medicine.” Journal of Computational Biology.
Reza, T., Ripeanu, M. Tripoul, N., Sanders, G., and Pearce, R. (2019). “PruneJuice: Pruning Trillion-edge Graphs to a Precise Pattern-matching Solution.” Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis.
Roberts, R.S., Goforth, J.W., Weinert, G.F., (…), Stinson, B.J., and Duncan, A.M. (2019). “Automated Annotation of Satellite Imagery using Model-based Projections.” Proceedings of the Applied Imagery Pattern Recognition Workshop.
Shanthamallu, U., Li, Q., Thiagarajan, J.J., Anirudh, R., Kaplan, A., and Bremer, P-T. (2020). “Modeling Human Brain Connectomes using Structured Neural Networks.” Neurips 2019: Graph Representation Learning Workshop.
Shanthamallu, U., Thiagarajan, J.J., Song, H., and Spanias, A. (2020). “GrAMME: Semi-Supervised Learning using Multi-Layered Graph Attention Models.” IEEE Transactions on Neural Networks and Learning Systems.
Shukla, R., Lipasti, M., Van Essen, B., Moody, A., and Maruyama, N. (2019). “Remodel: Rethinking Deep CNN Models to Detect and Count on a Neurosynaptic System.” Frontiers in Neuroscience.
Song, H., and Thiagarajan, J.J. (2020). “Improved Deep Embeddings for Inferencing with Multi-Layered Graphs.” Deep Graph Learning: Methodologies and Applications, IEEE Big Data 2019.
Thiagarajan, J.J., Anirudh, R., Sridhar, R., and Bremer, P-T. (2019). “Unsupervised Dimension Selection using a Blue Noise Graph Spectrum.” Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing.
Thiagarajan, J.J., Kashyap, S., and Karagyris, A. (2020). “Distill-to-Label: Weakly Supervised Instance Labeling using Knowledge Distillation.” IEEE International Conference on Machine Learning and Applications 2019.
Thiagarajan, J.J., Kim, I., Anirudh, R., and Bremer, P-T. (2019). “Understanding Deep Neural Networks through Input Uncertainties.” Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing.
Thiagarajan, J.J., Rajan, D., and Sattigeri, P. (2019). “Understanding Behavior of Clinical Models under Domain Shifts.” 2019 KDD Workshop on Applied Data Science for Healthcare.
Thopalli, K., Anirudh, R., Thiagarajan, J.J., and Turaga, P. (2019). “Multiple Subspace Alignment Improves Domain Adaptation.” Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing.
Tran, K., Panahi, A., Adiga, A., Sakla, W., and Krim, H. (2019). “Nonlinear Multi-Scale Super-resolution using Deep Learning.” Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing.
Tripoul, N., Halawa, H., Reza, T., (…), Pearce, R., and Ripeanu, M. (2019). “There Are Trillions of Little Forks in the Road. Choose Wisely! Estimating the Cost and Likelihood of Success of Constrained Walks to Optimize a Graph Pruning Pipeline.” Proceedings of IA3 2018: 8th Workshop on Irregular Applications: Architectures and Algorithms, and the International Conference for High Performance Computing, Networking, Storage and Analysis.
Veldt, N., Klymko, C., and Gleich, D.F. (2019). “Flow-Based Local Graph Clustering with Better Seed Set Inclusion.” SIAM International Conference on Data Mining.
White, D.A., Arrighi, W.J., Kudo, J., and Watts, S.E. (2019). “Multiscale Topology Optimization using Neural Network Surrogate Models.” Computer Methods in Applied Mechanics and Engineering.
Yuan, B., Giera, B., Guss, G., Matthews, M., and McMains, S. (2019). “Semi-Supervised Convolutional Neural Networks for in-situ Video Monitoring of Selective Laser Melting.” IEEE Winter Conference on Applications of Computer Vision.
Anirudh R., Kim H., Thiagarajan J.J., et al. (2018). “Lose the Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion.” Conference on Computer Vision and Pattern Recognition.
Kamath C. and Fan Y.J. (2018). “Compressing Unstructured Mesh Data using Spline Fits, Compressed Sensing, and Regression Methods.” IEEE Global Conference on Signal and Information Processing.
Kamath C. and Fan Y.J. (2018). "Regression with small data sets: A case study using code surrogates in additive manufacturing." Knowledge and Information Systems: An International Journal.
Lin Y., Wang S., Thiagarajan J.J., et al. (2018). "Efficient Data-Driven Geologic Feature Characterization from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm." Geophysical Journal International.
Liu S., Bremer P-T., Thiagarajan J.J., et al. (2018). "Visual Exploration of Semantic Relationships in Neural Word Embeddings." IEEE Transactions on Visualization and Computer Graphics.
Mundhenk T.N., Ho D., and Chen B.Y. (2018). "Improvements to context based self-supervised learning." Conference on Computer Vision and Pattern Recognition.
Rajan D. and Thiagarajan J.J. (2018). “A Generative Modeling Approach to Limited Channel ECG Classification.” Conference proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
Song H., Rajan D., Thiagarajan J.J., and Spanias A. (2018). "Attend and Diagnose: Clinical Time Series Analysis using Attention Models." AAAI Conference.
Song H., Thiagarajan J.J., Sattigeri P., and Spanias A. (2018). "Optimizing Kernel Machines using Deep Learning." IEEE Transactions on Neural Networks and Learning Systems.
Song H., Willi M., Thiagarajan J.J., et al. (2018). “Triplet Network with Attention for Speaker Diarization.” Proceedings of the Annual Conference of the International Speech Communication Association.
Thiagarajan J.J., Anirudh R., Kailkhura B., et al. (2018). "PADDLE: Performance Analysis using a Data-driven Learning Environment." IEEE International Parallel and Distributed Processing Symposium.
Thiagarajan J.J., Jain N., Anirudh R., et al. (2018). “Bootstrapping Parameter Space Exploration for Fast Tuning.” Association for Computing Machinery.
Thiagarajan J.J., Liu S., Ramamurthy K., and Bremer P-T. (2018). "Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections." Conference on Visualization.
Zheng P., Aravkin A.Y., Ramamurthy K., and Thiagarajan J.J. (2018). "Visual Exploration of Semantic Relationships in Neural Word Embeddings." IEEE International Conference on Computer Vision Workshops.
Anirudh R., Kailkhura B., Thiagarajan J.J., and Bremer P-T. (2017). "Poisson Disk Sampling on the Grassmannian: Applications in Subspace Optimization." Conference on Computer Vision and Pattern Recognition.
Kim S., Ames, S., Lee J., et al. (2017). "Massive Scale Deep Learning for Detecting Extreme Climate Events." International Workshop on Climate Informatics.
Kim S., Ames, S., Lee J., et al. (2017). "Resolution Reconstruction of Climate Data with Pixel Recursive Model." IEEE International Conference on Data Mining.
Lennox K.P., Rosenfield P., Blair B., et al. (2017). "Assessing and Minimizing Contamination in Time of Flight Based Validation Data." Nuclear Instruments and Methods in Physics Research.
Li Q., Kailkhura B., Thiagarajan J.J., and Varshney P.K. (2017). "Influential Node Detection in Implicit Social Networks using Multi-task Gaussian Copula Models." Conference on Neural Information Processing Systems.
Lin Y., Wang S., Thiagarajan J.J., et al. (2017). "Towards Real-Time Geologic Feature Detection from Seismic Measurements using a Randomized Machine-Learning Algorithm." SEG Annual Conference.
Marathe A., Anirudh R., Jain N., et al. (2017). "Performance Modeling Under Resource Constraints using Deep Transfer Learning." Supercomputing Conference.
Mudigonda M., Kim S., Mahesh A., et al. (2017). "Segmenting and Tracking Extreme Climate Events using Neural Networks." Conference on Neural Information Processing Systems.
Mundhenk N.T., Kegelmeyer L.M., and Trummer S.K. (2017). "Deep learning for evaluating difficult-to-detect incomplete repairs of high fluence laser optics at the National Ignition Facility." Thirteenth International Conference on Quality Control by Artificial Vision.
Pallotta G., Konjevod G., Cadena J., and Nguyen P. (2017). "Context-aided Analysis of Community Evolution in Networks." Statistical Analysis and Data Mining: The ASA Data Science Journal.
Sakla W., Konjevod G., and Mundhenk N.T. (2017). "Deep Multi-modal Vehicle Detection in Aerial ISR Imagery." IEEE Winter Conference on Applications of Computer Vision.
Song H., Thiagarajan J.J., Sattigeri P., and Spanias A. (2017). "A Deep Learning Approach to Multiple Kernel Learning." IEEE International Conference on Acoustics, Speech and Signal Processing.
Zheng P., Aravkin A.Y., Ramamurthy K., and Thiagarajan J.J. (2017). "Learning Robust Representations for Computer Vision." IEEE International Conference on Computer Vision Workshops.
