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Reference | Publication | Mathematics and Computer Science Division

Machine Learning Publications

Publications

  • P. Balaprakash, R. Egele, M. Salim, S. Wild, V. Vishwanath, F. ZXia, T. Beettin, R. Stevens, Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research, arXiv:1909.00311.  The Bookmark is https://arxiv.org/abs/1909.00311.
  • P Balaprakash, M Salim, T Uram, V Vishwanath, S Wild
    2018 IEEE 25th International Conference on High Performance Computing (HiPC) Dec. 2018, DOI: 10.1109/HiPC.2018.00014.  DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks
  • S. Aithal and P. Balaprakash. MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles. In 34st ISC High Performance Conference(to appear), Frankfurt, Germany, 2019.
  • S Madireddy, DW Chung, T Loeffler, SKRS Sankaranarayanan, D. N. Seidman, P. Balaprakash, O. Heinonen
    arXiv preprint arXiv:1904.05433, 2019.  Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection
  • S. Aithal and P. Balaprakash. MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles. In 34st ISC High Performance Conference(to appear), Frankfurt, Germany, 2019.
  • ukreja, Navjot, Alena Shilova, Olivier Beaumont, Jan Hückelheim, Nicola Ferrier, Paul Hovland, and Gerard Gorman. "Training on the Edge: The why and the how." In 1st Workshop on Parallel AI and Systems for the Edge (PAISE). 2019.
  • Jun Han, Jun Tao, Hanqi Guo, Danny Z Chen, and Chaoli Wang. "Flow Field Reduction via Reconstructing Vector Data from 3D Streamlines Using Deep Learning." IEEE Computer Graphics and Applications (to appear), 2019. 
  • Nathan Baker, Frank Alexander, Timo Bremer, Aric Hagberg, Yannis Kevrekidis, Habib Najm, Manish Parashar, Abani Patra, James Sethian, Stefan Wild, Karen Willcox Technical report, U.S. Department of Energy,  DOI:  10.2172/1478744 2019. Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence
  • S. Sreepathi, J. Kumar, R. T. Mills, Forrest M. Hoffman, V. Sripathi, W. W. Hargrove, 2017. "Parallel Multivariate Spatio-Temporal Clustering of Large Ecological Datasets on Hybrid Supercomputers" https://www.climatemodeling.org/~rmills/pubs/Sreepathi_IEEECluster_2017…. Proceedings of IEEE Cluster 2017. doi: 10.1109/CLUSTER.2017.88.
  • JM Wozniak, R Jain, P Balaprakash, J Ozik, NT Collier, J Bauer, F Xia, Thomas Brettin, Rick Stevens, Jamaludin Mohd-Yusof, Cristina Garcia Cardona, Brian Van Essen, Matthew Baughman, BMC Bioinformatics 19 (18), 491, 2018.  CANDLE/Supervisor: A workflow framework for machine learning applied to cancer research
  • R. T. Mills, V. Sripathi, J. Kumar, S. Sreepathi, F. M. Hargrove, W. W. Hargrove, 2018. "Parallel k-means Clustering of Geospatial Data Sets Using Manycore CPU Architectures" https://www.climatemodeling.org/~rmills/pubs/Mills_DMESS2018_20181117.p…. Eighth Workshop on Data Mining in Earth System Sciences (DMESS 2018), Proceedings of the IEEE International Conference on Data Mining (ICDM 2018) Workshops.
  • A. Sanaullah, M. Herbordt, C. Yang, Y. Alexeev, K. Yoshii, "Real-Time Data Analysis for Medical Diagnosis using FPGA Accelerated Neural Networks", BMC special issue
  • A. Sanaullah, C. Yang, M. Herbordt, Y. Alexeev, K. Yoshii, "Application Aware Tuning of Reconfigurable Multi-Layer Perceptiron Architectures", 2018 IEEE High Performance Extreme Computing Conference (HPEC)
  • Y. Luo, X. Wang, S. Ogrenci-Memik, G. Memik, K. Yoshii, P. Beckman, "Minimizing Thermal Variation in Heterogeneous HPC Systems with FPGA Nodes", 2018 IEEE International Conference on Computer Design (ICCD), Florida, Oct 10
  • Omer Subasi, L. Bautista-Gomez,P. Balaprakash, O. Sabri Unsal, S. Krishnamoorthy, F. Cappello, A. Cristal, S. Di, J. Labarta, "Exploring the Capabilities of Support Vector Machines in Detecting Silent Data Corruptions", Sustainable Computing, Informatics and Systems, (SCIS) Elsevier, forthcoming, Volume 19, Pages 277-290, September 2018.
  • C. Wang, N. Dryden, F. Cappello, M. Snir, "Neural network based silent error detector", Best Paper, IEEE Cluster 2018
  • Madireddy, Sandeep, Prasanna Balaprakash, Philip Carns, Robert Latham, Robert Ross, Shane Snyder, and Stefan M. Wild. “Machine Learning Based Parallel I/O Predictive Modeling: A Case Study on Lustre File Systems”. In: High Performance Computing. Cham: Springer International Publishing, 184–204, 2018.
  • Madireddy, Sandeep, Prasanna Balaprakash, Philip Carns, Robert Latham, Robert Ross, Shane Snyder, andStefan M. Wild. “Modeling I/O Performance Variability Using Conditional Variational Autoencoders”. In:2018 IEEE International Conference on Cluster Computing (CLUSTER), 2018.
  • R. T. Mills, V. Sripathi, J. Kumar, S. Sreepathi, F. M. Hargrove, W. W. Hargrove, 2018. "Parallel k-means Clustering of Geospatial Data Sets Using Manycore CPU Architectures". Eighth Workshop on Data Mining in Earth System Sciences (DMESS 2018), Proceedings of the IEEE International Conference on Data Mining (ICDM 2018) Workshops.
  • Peterka, T., Nashed, Y., Grindeanu, I., Mahadevan, V., Yeh, R., Tricoche, X.: Foundations of Multivariate Functional Approximation for Scientific Data. Proceedings of IEEE Symposium on Large Data Analysis and Visualization (LDAV) 2018, Berlin, Germany, 2018.
  • Cherukara, Mathew J., Youssef SG Nashed, and Ross J. Harder. "Real-time coherent diffraction inversion using deep generative networks." Scientific reports8, no. 1 (2018): 16520.
  • Yoo, Seunghwan, Pablo Ruiz, Xiang Huang, Kuan He, Xiaolei Wang, Itay Gdor, Alan Selewa et al. "Bayesian Approach for Automatic Joint Parameter Estimation in 3D Image Reconstruction from Multi-Focus Microscope." In 201825th IEEE International Conference on Image Processing (ICIP), pp. 3583-3587. IEEE, 2018.
  • Seunghwan Yoo, Pablo Ruiz, Xiang Huang, Kuan He, Nicola J. Ferrier, Mark Hereld, Alan Selewa, Matthew Daddysman, Norbert Scherer, Oliver Cossairt, Aggelos K. Katsaggelos, “3D Image reconstruction from multi-focus microscope: axial super-resolution and multiple-frame processing,” In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1453-1457. IEEE, 2018.
  • Agarwal, Nitin, Ferrier, Nicola and Mark Hereld. "Towards Automated Transcription of Label Text from Pinned Insect Collections." 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 189-198, March 2018.
  • Zhang, Yan, Dilshan Godaliyadda, Nicola Ferrier, Emine Gulsoy, Charles Bouman and Charudatta Phatak. "SLADS-Net: Supervised Learning Approach for Dynamic Sampling using Deep Neural Networks." 2018 Imaging Science and Technology International Symposium on Electronic Imaging, January 28, 2018–February 1, 2018.
  • Zhang, Yan, G.M. Godaliyadda, Nicola Ferrier, Emine Gulsoy, Charles Bouman and Charudatta Phatak. "Reduced Electron Exposure for Energy-Dispersive Spectroscopy using Dynamic Sampling." Ultramicroscopy, Vol 184, Part B, Jan 2018, Pages 90–97. DOI: https://doi.org/10.1016/j.ultramic.2017.10.015
  • K. Kulshreshtha, S.H.K. Narayanan, J. Bessac & K. MacIntyre (2018 )Efficient computation of derivatives for solving optimization problems in R and Python using SWIG-generated interfaces to ADOL-C, Optimization Methods and Software,33:4-6,1173-1191,DOI: 10.1080/10556788.2018.1425861
  • P. Balaprakash, M. Salim, T. Uram, V. Vishwanath, and S. M. Wild. DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks. In 25th IEEE International Conference on High Performance Computing, Data, and Analytics. IEEE, 2018. DOI: 10.1109/HiPC.2018.00014
  • Z. Liu, R. Kettimuthu, P. Balaprakash, and I. Foster. Building a wide-area data transfer performance predictor: An empirical study. In the 1st International Conference on Machine Learning for Networking, MLN 2018. Springer, 2018. 
  • P. Balaprakash, J. Dongarra, T. Gamblin, M. Hall, J. K. Hollingsworth, B. Norris, and R. Vuduc. Autotuning in high-performance computing applications. Proceedings of the IEEE, pages 1–16, 2018. http://dx.doi.org/10.1109/JPROC.2018.2841200
  • A. Sanaullah, M. Herbordt, C. Yang, Y. Alexeev, K. Yoshi, "Boosting Curative Surgery Success Rates with FPGAs", Computational Approaches for Cancer (CAFCW17) at SC17
  • A. Sanaullah, C. Yang, Y. Alexeev, K. Yoshii, M.C. Herbordt. "TRIP: An Ultra-Low Latency TeraOps/s  Reconfigureable Inference Processor for Multi-Layer Perceptrons." Poster presented at 2017 International Conference for High Performance Computing, Networking, Storage and Analysis, Denver, CO., US, November 12, 2017-November 17, 2017
  • Sunwoo Lee, Ankit Agrawal, Prasanna Balaprakash, Alok Choudhary, Wei-keng Liao, Communication-efficient parallelization strategy for deep convolutional neural network training, Workshop: 4th Workshop on Machine Learning in HPC Environments)- SC18
  • Xin Liang, Sheng Di, Sihuan Li, Dingwen Tao, Zizhong Chen, Franck Cappello, Exploring Best Lossy Compression Strategy by Combining SZ with Spatiotemporal Decimation, Workshop: Machine Learning in HPC Environments - SC18
  • Xin-Chuan Wu, Sheng Di, Franck Cappello, Hal Finkel, Yuri Alexeev, Frederic T. Chong, Amplitude-Aware Lossy Compression for Quantum Circuit Simulation, Workshop: 4th International Workshop on Data Reduction for Big Scientific Data, DRBSD-4 - SC18
  • Preeti Malakar, Prasanna Balaprakash, Venkatram Vishwanath, Vitali Morozov, Kalyan Kumaran, Benchmarking Machine Learning Methods for Performance Modeling of Scientific Application – (Workshop: 9th IEEE International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems. PMBS’18)
  • Z Liu, P Balaprakash, R Kettimuthu, I Foster, Wide Area Data Transfer,in 26th ACM International Symposium on High-Performance Parallel and Distributed Computing, Washington, DC, July 2017
  • S Sahoo, TA Russo, J Elliott, I Foster, Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US, Water Resources Research, 2017
  • K Zhang, A Guliani, S Ogrenci-Memik, G Memik, K Yoshii, R Sankaran, P. Beckman, Machine Learning-Based Temperature Prediction for Runtime Thermal Management across System Components, IEEE Transactions on Parallel and Distributed Systems, 2017
  • P-L Guhur, H Zhang, T Peterka, E Constantinescu, F Cappello, Lightweight and Accurate Silent Data Corruption Detection in Ordinary Differential Equation Solvers, in EuroPar 2016
  • P Balaprakash, A Tiwari, SM Wild, L Carrington, PD Hovland, AutoMOMML: Automatic Multi-objective Modeling with Machine Learning, International Conference on High Performance Computing, 2016,  pp. 219-239
  • F Isaila, P Balaprakash, SM Wild, D Kimpe, R Latham, R Ross, P Hovland, Collective I/O tuning using analytical and machine learning models, in IEEE International Conference on Cluster Computing (CLUSTER), 2015, pp. 128-137
  • P Balaprakash, Y Alexeev, SA Mickelson, S Leyffer, R Jacob, A Craig, Machine-learning-based load balancing for Community Ice CodE component in CESM, in International Conference on High Performance Computing for Computational Science, 2014, pp. 79-91
  • P Balaprakash, Y Alexeev, SA Mickelson, S Leyffer, RL Jacob, AP Craig, Machine learning based load-balancing for the cesm climate modeling package, in Proc. VECPAR 2014
  • O Subasi, S Di, P Balaprakash, O Unsai, J Labaarta, A Cristal, S Krishnamoorthy, F. Cappello,  MACORD: Online Adaptive Machine Learning Framework for Silent Error Detection, in CLUSTER 2017
  • A. Guliani, K. Zhang, S. Ogrenci-Mernik, G. Mernik, K. Yoshii, R. Sakaran, and P. Beckman, Machine learning-based temperature prediction for runtime thermal management across system components, IEEE Transactions on Parallel and Distributed Systems, July 2017, DOI Bookmark: http://doi.ieeecomputersociety.org/

Presentations 

  • Justin M. Wozniak, Rajeev Jain, Prasanna Balaprakash, Jonathan Ozik, Nicholson Collier, John Bauer, Fangfang Xia, Thomas Brettin, Rick Stevens, Jamaludin Mohd-Yusof, Cristina, Garcia Cardona, Brian Van Essen, and Matthew Baughman, “CANDLE/Supervisor: A workflow framework for machine learning applied to cancer research,” SC17 Workshop: Computational Approaches for Cancer

Posters

Preprints

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