Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDPrabhat
Person information
- affiliation: Lawrence Berkeley National Laboratory, NERSC
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
Journal Articles
- 2020
[j13]Jin-Long Wu
, Karthik Kashinath, Adrian Albert, Dragos Chirila, Prabhat, Heng Xiao:
Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems. J. Comput. Phys. 406: 109209 (2020)- 2019
- [j12]Alex Gittens, Kai Rothauge, Shusen Wang, Michael W. Mahoney, Jey Kottalam, Lisa Gerhardt, Prabhat, Michael F. Ringenburg, Kristyn J. Maschhoff:
Alchemist: An Apache Spark ⇔ MPI interface. Concurr. Comput. Pract. Exp. 31(16) (2019) - [j11]Jeffrey Regier, Keno Fischer, Kiran Pamnany, Andreas Noack, Jarrett Revels, Maximilian Lam, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat:
Cataloging the visible universe through Bayesian inference in Julia at petascale. J. Parallel Distributed Comput. 127: 89-104 (2019) - [j10]Markus Reichstein, Gustau Camps-Valls, Bjorn Stevens, Martin Jung, Joachim Denzler, Nuno Carvalhais, Prabhat:
Deep learning and process understanding for data-driven Earth system science. Nat. 566(7743): 195-204 (2019) - [j9]Babak Behzad, Surendra Byna, Prabhat, Marc Snir:
Optimizing I/O Performance of HPC Applications with Autotuning. ACM Trans. Parallel Comput. 5(4): 15:1-15:27 (2019) - 2018
- [j8]Kristofer E. Bouchard, James B. Aimone, Miyoung Chun, Thomas Dean, Michael Denker, Markus Diesmann, David Donofrio, Loren M. Frank, Narayanan Kasthuri, Christof Koch, Oliver Rübel, Horst D. Simon, Friedrich T. Sommer, Prabhat:
International Neuroscience Initiatives through the Lens of High-Performance Computing. Computer 51(4): 50-59 (2018) - 2016
- [j7]Oliver Rübel, Max Dougherty, Prabhat, Peter Denes, David Conant, Edward F. Chang, Kristofer E. Bouchard:
Methods for Specifying Scientific Data Standards and Modeling Relationships with Applications to Neuroscience. Frontiers Neuroinformatics 10: 48 (2016) - 2013
- [j6]Dean N. Williams, Peer-Timo Bremer, Charles M. Doutriaux, John Patchett, Sean Williams, Galen M. Shipman, Ross Miller, David Pugmire, Brian E. Smith, Chad A. Steed, E. Wes Bethel, Hank Childs, Harinarayan Krishnan, Prabhat, Michael F. Wehner, Cláudio T. Silva, Emanuele Santos, David Koop, Tommy Ellqvist, Jorge Poco, Berk Geveci, Aashish Chaudhary, Andy Bauer, Alexander Pletzer, Dave Kindig, Gerald Potter, Thomas P. Maxwell:
Ultrascale Visualization of Climate Data. Computer 46(9): 68-76 (2013) - [j5]Wei-Chen Chen, George Ostrouchov, David Pugmire, Prabhat, Michael F. Wehner:
A Parallel EM Algorithm for Model-Based Clustering Applied to the Exploration of Large Spatio-Temporal Data. Technometrics 55(4): 513-523 (2013) - 2012
- [j4]Richard L. Martin, Prabhat, David Donofrio, James A. Sethian, Maciej Haranczyk:
Accelerating analysis of void space in porous materials on multicore and GPU platforms. Int. J. High Perform. Comput. Appl. 26(4): 347-357 (2012) - 2010
- [j3]Hank Childs, David Pugmire, Sean Ahern, Brad Whitlock, Mark Howison, Prabhat, Gunther H. Weber, E. Wes Bethel:
Extreme Scaling of Production Visualization Software on Diverse Architectures. IEEE Computer Graphics and Applications 30(3): 22-31 (2010) - 2008
- [j2]Prabhat, Andrew S. Forsberg, Michael Katzourin, Kristi Wharton, Mel Slater:
A Comparative Study of Desktop, Fishtank, and Cave Systems for the Exploration of Volume Rendered Confocal Data Sets. IEEE Trans. Vis. Comput. Graph. 14(3): 551-563 (2008) - 2006
- [j1]Andrew S. Forsberg, Prabhat, Graff Haley, Andrew Bragdon, Joseph Levy, Caleb I. Fassett, David Shean, James W. Head III, Sarah Milkovich, Mark A. Duchaineau:
Adviser: Immersive Field Work for Planetary Geoscientists. IEEE Computer Graphics and Applications 26(4): 46-54 (2006)
Conference and Workshop Papers
- 2020
- [c55]Grzegorz Muszynski, Prabhat, Jan Balewski, Karthik Kashinath, Michael F. Wehner, Vitaliy Kurlin:
Atmospheric Blocking Pattern Recognition in Global Climate Model Simulation Data. ICPR 2020: 677-684 - [c54]Mahesh Balasubramanian, Trevor D. Ruiz, Brandon Cook, Prabhat, Sharmodeep Bhattacharyya, Aviral Shrivastava, Kristofer E. Bouchard:
Scaling of Union of Intersections for Inference of Granger Causal Networks from Observational Data. IPDPS 2020: 264-273 - [c53]Chiyu Max Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi A. Tchelepi, Philip Marcus, Prabhat, Anima Anandkumar:
MeshfreeFlowNet: a physics-constrained deep continuous space-time super-resolution framework. SC 2020: 9 - 2019
- [c52]Sookyung Kim, Sunghyun Park, Sunghyo Chung, Joonseok Lee, Yunsung Lee, Hyojin Kim, Prabhat, Jaegul Choo:
Learning to Focus and Track Extreme Climate Events. BMVC 2019: 11 - [c51]Chiyu Max Jiang, Jingwei Huang, Karthik Kashinath, Prabhat, Philip Marcus, Matthias Nießner:
Spherical CNNs on Unstructured Grids. ICLR (Poster) 2019 - [c50]Atilim Gunes Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood:
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. NeurIPS 2019: 5460-5473 - [c49]Liu Yang, Prabhat, George E. Karniadakis, Sean Treichler, Thorsten Kurth, Keno Fischer, David A. Barajas-Solano, Joshua Romero, Valentin Churavy, Alexandre M. Tartakovsky, Michael Houston:
Highly-Ccalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs. DLS@SC 2019: 1-11 - [c48]Atilim Günes Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Lawrence Meadows, Jialin Liu, Andreas Munk, Saeid Naderiparizi, Bradley Gram-Hansen, Gilles Louppe, Mingfei Ma, Xiaohui Zhao, Philip H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood:
Etalumis: bringing probabilistic programming to scientific simulators at scale. SC 2019: 29:1-29:24 - [c47]Adam Rupe, Prabhat, James P. Crutchfield, Nalini Kumar, Vladislav Epifanov, Karthik Kashinath, Oleksandr Pavlyk, Frank Schlimbach, Mostofa Patwary, Sergey Maidanov, Victor W. Lee:
DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems. MLHPC@SC 2019: 75-87 - [c46]Chao Jiang, Dave Ojika, Thorsten Kurth, Prabhat, Sofia Vallecorsa, Bhavesh Patel, Herman Lam:
Acceleration of Scientific Deep Learning Models on Heterogeneous Computing Platform with Intel® FPGAs. ISC Workshops 2019: 587-600 - [c45]Sookyung Kim, Hyojin Kim, Joonseok Lee, Sangwoong Yoon, Samira Ebrahimi Kahou, Karthik Kashinath, Prabhat:
Deep-Hurricane-Tracker: Tracking and Forecasting Extreme Climate Events. WACV 2019: 1761-1769 - 2018
- [c44]Haoyuan Xing, Sofoklis Floratos, Spyros Blanas, Suren Byna, Prabhat, Kesheng Wu, Paul Brown:
ArrayBridge: Interweaving Declarative Array Processing in SciDB with Imperative HDF5-Based Programs. ICDE 2018: 977-988 - [c43]Nicholas Choma, Federico Monti, Lisa Gerhardt, Tomasz Palczewski, Zahra Ronaghi, Prabhat, Wahid Bhimji, Michael M. Bronstein, Spencer R. Klein, Joan Bruna:
Graph Neural Networks for IceCube Signal Classification. ICMLA 2018: 386-391 - [c42]Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat:
Cataloging the Visible Universe Through Bayesian Inference at Petascale. IPDPS 2018: 44-53 - [c41]Alex Gittens, Kai Rothauge, Shusen Wang, Michael W. Mahoney, Lisa Gerhardt, Prabhat, Jey Kottalam, Michael F. Ringenburg, Kristyn J. Maschhoff:
Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist. KDD 2018: 293-301 - [c40]Jialin Liu, Quincey Koziol, Gregory F. Butler, Neil Fortner, Mohamad Chaarawi, Houjun Tang, Suren Byna, Glenn K. Lockwood, Ravi Cheema, Kristy A. Kallback-Rose, Damian Hazen, Prabhat:
Evaluation of HPC Application I/O on Object Storage Systems. PDSW-DISCS@SC 2018: 24-34 - [c39]Thorsten Kurth, Sean Treichler, Joshua Romero, Mayur Mudigonda, Nathan Luehr, Everett H. Phillips, Ankur Mahesh, Michael A. Matheson, Jack Deslippe, Massimiliano Fatica, Prabhat, Michael Houston:
Exascale deep learning for climate analytics. SC 2018: 51:1-51:12 - [c38]Amrita Mathuriya, Deborah Bard, Peter Mendygral, Lawrence Meadows, James Arnemann, Lei Shao, Siyu He, Tuomas Kärnä, Diana Moise, Simon J. Pennycook, Kristyn J. Maschhoff, Jason Sewall, Nalini Kumar, Shirley Ho, Michael F. Ringenburg, Prabhat, Victor W. Lee:
CosmoFlow: using deep learning to learn the universe at scale. SC 2018: 65:1-65:11 - [c37]Steven Andrew Farrell, Aaron Vose, Oliver Evans, Matthew L. Henderson, Shreyas Cholia, Fernando Pérez, Wahid Bhimji, Shane Canon, Rollin C. Thomas, Prabhat:
Interactive Distributed Deep Learning with Jupyter Notebooks. ISC Workshops 2018: 678-687 - 2017
- [c36]Kristofer E. Bouchard, Alejandro F. Bujan, Farbod Roosta-Khorasani, Shashanka Ubaru, Prabhat, Antoine Snijders, Jian-Hua Mao, Edward F. Chang, Michael W. Mahoney, Sharmodeep Bhattacharyya:
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction. NIPS 2017: 1078-1086 - [c35]Evan Racah, Christopher Beckham, Tegan Maharaj, Samira Ebrahimi Kahou, Prabhat, Chris Pal:
ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events. NIPS 2017: 3402-3413 - [c34]Thorsten Kurth, Jian Zhang, Nadathur Satish, Evan Racah, Ioannis Mitliagkas, Md. Mostofa Ali Patwary, Tareq M. Malas, Narayanan Sundaram, Wahid Bhimji, Mikhail Smorkalov, Jack Deslippe, Mikhail Shiryaev, Srinivas Sridharan, Prabhat, Pradeep Dubey:
Deep learning at 15PF: supervised and semi-supervised classification for scientific data. SC 2017: 7 - [c33]Brian Friesen, Md. Mostofa Ali Patwary, Brian Austin, Nadathur Satish, Zachary Slepian, Narayanan Sundaram, Deborah Bard, Daniel J. Eisenstein, Jack Deslippe, Pradeep Dubey, Prabhat:
Galactos: computing the anisotropic 3-point correlation function for 2 billion galaxies. SC 2017: 20 - [c32]Christopher S. Daley, Prabhat, Sudip S. Dosanjh, Nicholas J. Wright:
Performance analysis of emerging data analytics and HPC workloads. PDSW-DISCS@SC 2017: 43-48 - 2016
- [c31]Alex Gittens, Aditya Devarakonda, Evan Racah, Michael F. Ringenburg, Lisa Gerhardt, Jey Kottalam, Jialin Liu, Kristyn J. Maschhoff, Shane Canon, Jatin Chhugani, Pramod Sharma, Jiyan Yang, James Demmel, Jim Harrell, Venkat Krishnamurthy, Michael W. Mahoney, Prabhat:
Matrix factorizations at scale: A comparison of scientific data analytics in spark and C+MPI using three case studies. IEEE BigData 2016: 204-213 - [c30]Bin Dong, Suren Byna, Kesheng Wu, Prabhat, Hans Johansen, Jeffrey N. Johnson, Noel Keen:
Data Elevator: Low-Contention Data Movement in Hierarchical Storage System. HiPC 2016: 152-161 - [c29]Evan Racah, Seyoon Ko, Peter J. Sadowski, Wahid Bhimji, Craig Tull, Sang-Yun Oh, Pierre Baldi, Prabhat:
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks. ICMLA 2016: 892-897 - [c28]Md. Mostofa Ali Patwary, Nadathur Rajagopalan Satish, Narayanan Sundaram, Jialin Liu, Peter J. Sadowski, Evan Racah, Surendra Byna, Craig Tull, Wahid Bhimji, Prabhat, Pradeep Dubey:
PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures. IPDPS 2016: 494-503 - [c27]Alex Gittens, Jey Kottalam, Jiyan Yang, Michael F. Ringenburg, Jatin Chhugani, Evan Racah, Mohitdeep Singh, Yushu Yao, Curt Fischer, Oliver Rübel, Benjamin P. Bowen, Norman G. Lewis, Michael W. Mahoney, Venkat Krishnamurthy, Prabhat:
A Multi-Platform Evaluation of the Randomized CX Low-Rank Matrix Factorization in Spark. IPDPS Workshops 2016: 1403-1412 - 2015
- [c26]Prabhat, Surendra Byna, Venkatram Vishwanath, Eli Dart, Michael F. Wehner, William D. Collins:
TECA: Petascale Pattern Recognition for Climate Science. CAIP (2) 2015: 426-436 - [c25]Babak Behzad, Surendra Byna, Stefan M. Wild, Prabhat, Marc Snir:
Dynamic Model-Driven Parallel I/O Performance Tuning. CLUSTER 2015: 184-193 - [c24]Huong Luu, Marianne Winslett, William Gropp, Robert B. Ross, Philip H. Carns, Kevin Harms, Prabhat, Surendra Byna, Yushu Yao:
A Multiplatform Study of I/O Behavior on Petascale Supercomputers. HPDC 2015: 33-44 - [c23]Jeffrey Regier, Andrew C. Miller, Jon McAuliffe, Ryan P. Adams, Matthew D. Hoffman, Dustin Lang, David Schlegel, Prabhat:
Celeste: Variational inference for a generative model of astronomical images. ICML 2015: 2095-2103 - [c22]Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Md. Mostofa Ali Patwary, Prabhat, Ryan P. Adams:
Scalable Bayesian Optimization Using Deep Neural Networks. ICML 2015: 2171-2180 - [c21]Andrew C. Miller, Albert Wu, Jeffrey Regier, Jon McAuliffe, Dustin Lang, Prabhat, David Schlegel, Ryan P. Adams:
A Gaussian Process Model of Quasar Spectral Energy Distributions. NIPS 2015: 2494-2502 - [c20]Shane Snyder, Philip H. Carns, Robert Latham, Misbah Mubarak, Robert B. Ross, Christopher D. Carothers, Babak Behzad, Huong Vu Thanh Luu, Surendra Byna, Prabhat:
Techniques for modeling large-scale HPC I/O workloads. PMBS@SC 2015: 5:1-5:11 - [c19]Md. Mostofa Ali Patwary, Surendra Byna, Nadathur Rajagopalan Satish, Narayanan Sundaram, Zarija Lukic, Vadim Roytershteyn, Michael J. Anderson, Yushu Yao, Prabhat, Pradeep Dubey:
BD-CATS: big data clustering at trillion particle scale. SC 2015: 6:1-6:12 - [c18]Babak Behzad, Surendra Byna, Prabhat, Marc Snir:
Pattern-driven parallel I/O tuning. PDSW@SC 2015: 43-48 - 2014
- [c17]Babak Behzad, Surendra Byna, Stefan M. Wild, Prabhat, Marc Snir:
Improving parallel I/O autotuning with performance modeling. HPDC 2014: 253-256 - 2013
- [c16]Babak Behzad, Joey Huchette, Huong Vu Thanh Luu, Ruth A. Aydt, Surendra Byna, Yushu Yao, Quincey Koziol, Prabhat:
A framework for auto-tuning HDF5 applications. HPDC 2013: 127-128 - [c15]Babak Behzad, Huong Vu Thanh Luu, Joseph Huchette, Surendra Byna, Prabhat, Ruth A. Aydt, Quincey Koziol, Marc Snir:
Taming parallel I/O complexity with auto-tuning. SC 2013: 68:1-68:12 - [c14]E. Wes Bethel, Prabhat, Surendra Byna, Oliver Rübel, K. John Wu, Michael F. Wehner:
Why high performance visual data analytics is both relevant and difficult. Visualization and Data Analysis 2013: 86540B - 2012
- [c13]Mehmet Balman, Eric Pouyoul, Yushu Yao, E. Wes Bethel, Burlen Loring, Prabhat, John Shalf, Alex Sim, Brian L. Tierney:
Experiences with 100Gbps network applications. DICT@HPDC 2012: 33-42 - [c12]Surendra Byna, Jerry Chi-Yuan Chou, Oliver Rübel, Prabhat, Homa Karimabadi, William S. Daughton, Vadim Roytershteyn, E. Wes Bethel, Mark Howison, Ke-Jou Hsu, Kuan-Wu Lin, Arie Shoshani, Andrew Uselton, Kesheng Wu:
Parallel I/O, analysis, and visualization of a trillion particle simulation. SC 2012: 59 - [c11]Babak Behzad, Joey Huchette, Huong Luu, Ruth A. Aydt, Quincey Koziol, Prabhat, Surendra Byna, Mohamad Chaarawi, Yushu Yao:
Abstract: Auto-Tuning of Parallel IO Parameters for HDF5 Applications. SC Companion 2012: 1430 - [c10]Allen R. Sanderson, Brad Whitlock, Oliver Rübel, Hank Childs, Gunther H. Weber, Prabhat, Kenseng Wu:
A System for Query Based Analysis and Visualization. EuroVA@EuroVis 2012 - [c9]Prabhat, Oliver Rübel, Surendra Byna, Kesheng Wu, Fuyu Li, Michael F. Wehner, Wes Bethel:
TECA: A Parallel Toolkit for Extreme Climate Analysis. ICCS 2012: 866-876 - 2011
- [c8]Jerry Chi-Yuan Chou, Kesheng Wu, Prabhat:
FastQuery: A Parallel Indexing System for Scientific Data. CLUSTER 2011: 455-464 - [c7]Wei Zhuo, Prabhat, Christopher J. Paciorek, Cari Kaufman, Wes Bethel:
Parallel Kriging Analysis for Large Spatial Datasets. ICDM Workshops 2011: 38-44 - [c6]Jerry Chi-Yuan Chou, Mark Howison, Brian Austin, Kesheng Wu, Ji Qiang, E. Wes Bethel, Arie Shoshani, Oliver Rübel, Prabhat, Robert D. Ryne:
Parallel index and query for large scale data analysis. SC 2011: 30:1-30:11 - [c5]Jerry Chi-Yuan Chou, Kesheng Wu, Prabhat:
FastQuery: A General Indexing and Querying System for Scientific Data. SSDBM 2011: 573-574 - 2010
- [c4]Oliver Rübel, Sean Ahern, E. Wes Bethel, Mark D. Biggin, Hank Childs, Estelle Cormier-Michel, Angela H. DePace, Michael B. Eisen, Charless C. Fowlkes, Cameron G. R. Geddes, Hans Hagen, Bernd Hamann, Min-Yu Huang, Soile V. E. Keränen, David W. Knowles, Cris L. Luengo Hendriks, Jitendra Malik, Jeremy S. Meredith, Peter Messmer, Prabhat:
Coupling visualization and data analysis for knowledge discovery from multi-dimensional scientific data. ICCS 2010: 1757-1764 - 2008
- [c3]Daniela Mayumi Ushizima, Oliver Rübel, Prabhat, Gunther H. Weber, E. Wes Bethel, Cecilia R. Aragon, Cameron G. R. Geddes, Estelle Cormier-Michel, Bernd Hamann, Peter Messmer, Hans Hagen:
Automated Analysis for Detecting Beams in Laser Wakefield Simulations. ICMLA 2008: 382-387 - [c2]Oliver Rübel, Prabhat, Kesheng Wu, Hank Childs, Jeremy S. Meredith, Cameron G. R. Geddes, Estelle Cormier-Michel, Sean Ahern, Gunther H. Weber, Peter Messmer, Hans Hagen, Bernd Hamann, E. Wes Bethel:
High performance multivariate visual data exploration for extremely large data. SC 2008: 51 - 2005
- [c1]Prabhat, Samuel G. Fulcomer:
Experiences in driving a cave with IBM scalable graphics engine-3 (SGE-3) prototypes. VRST 2005: 231-234
Informal and Other Publications
- 2021
- [i31]Luca Pion-Tonachini, Kristofer E. Bouchard, Héctor García Martín, Sean Peisert, W. Bradley Holtz, Anil Aswani, Dipankar Dwivedi, Haruko M. Wainwright, Ghanshyam Pilania, Benjamin Nachman, Babetta L. Marrone, Nicola Falco, Prabhat, Daniel B. Arnold, Alejandro Wolf-Yadlin, Sarah Powers, Sharlee Climer, Quinn Jackson, Ty Carlson, Michael Sohn, Petrus H. Zwart, Neeraj Kumar, Amy Justice, Claire J. Tomlin, Daniel A. Jacobson, Gos Micklem, Georgios V. Gkoutos, Peter J. Bickel, Jean-Baptiste Cazier, Juliane Müller, Bobbie-Jo Webb-Robertson, Rick Stevens, Mark Anderson, Kenneth Kreutz-Delgado, Michael W. Mahoney, James B. Brown:
Learning from learning machines: a new generation of AI technology to meet the needs of science. CoRR abs/2111.13786 (2021) - 2020
- [i30]Chiyu Max Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi A. Tchelepi, Philip Marcus, Prabhat, Anima Anandkumar:
MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework. CoRR abs/2005.01463 (2020) - [i29]Nicholas Choma, Daniel Murnane, Xiangyang Ju, Paolo Calafiura, Sean Conlon, Steven Farrell, Prabhat, Giuseppe Cerati, Lindsey Gray, Thomas Klijnsma, Jim Kowalkowski, Panagiotis Spentzouris, Jean-Roch Vlimant, Maria Spiropulu, Adam Aurisano, Jeremy Hewes, Aristeidis Tsaris, Kazuhiro Terao, Tracy L. Usher:
Track Seeding and Labelling with Embedded-space Graph Neural Networks. CoRR abs/2007.00149 (2020) - 2019
- [i28]Chiyu Max Jiang, Jingwei Huang, Karthik Kashinath, Prabhat, Philip Marcus, Matthias Nießner:
Spherical CNNs on Unstructured Grids. CoRR abs/1901.02039 (2019) - [i27]Atilim Günes Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Lawrence Meadows, Jialin Liu, Andreas Munk, Saeid Naderiparizi, Bradley Gram-Hansen, Gilles Louppe, Mingfei Ma, Xiaohui Zhao, Philip H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood:
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale. CoRR abs/1907.03382 (2019) - [i26]Adam Rupe, Karthik Kashinath, Nalini Kumar, Victor W. Lee, Prabhat, James P. Crutchfield:
Towards Unsupervised Segmentation of Extreme Weather Events. CoRR abs/1909.07520 (2019) - [i25]Adam Rupe, Nalini Kumar, Vladislav Epifanov, Karthik Kashinath, Oleksandr Pavlyk, Frank Schlimbach, Mostofa Patwary, Sergey Maidanov, Victor W. Lee, Prabhat, James P. Crutchfield:
DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems. CoRR abs/1909.11822 (2019) - [i24]Liu Yang, Sean Treichler, Thorsten Kurth, Keno Fischer, David A. Barajas-Solano, Joshua Romero, Valentin Churavy, Alexandre M. Tartakovsky, Michael Houston, Prabhat, George E. Karniadakis:
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs. CoRR abs/1910.13444 (2019) - 2018
- [i23]Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat:
Cataloging the Visible Universe through Bayesian Inference at Petascale. CoRR abs/1801.10277 (2018) - [i22]Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, Prabhat:
Approximate Inference for Constructing Astronomical Catalogs from Images. CoRR abs/1803.00113 (2018) - [i21]Alex Gittens, Kai Rothauge, Shusen Wang, Michael W. Mahoney, Lisa Gerhardt, Prabhat, Jey Kottalam, Michael F. Ringenburg, Kristyn J. Maschhoff:
Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist. CoRR abs/1805.11800 (2018) - [i20]Alex Gittens, Kai Rothauge, Shusen Wang, Michael W. Mahoney, Jey Kottalam, Lisa Gerhardt, Prabhat, Michael F. Ringenburg, Kristyn J. Maschhoff:
Alchemist: An Apache Spark MPI Interface. CoRR abs/1806.01270 (2018) - [i19]Atilim Gunes Baydin, Lukas Heinrich, Wahid Bhimji, Bradley Gram-Hansen, Gilles Louppe, Lei Shao, Prabhat, Kyle Cranmer, Frank D. Wood:
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. CoRR abs/1807.07706 (2018) - [i18]Amrita Mathuriya, Deborah Bard, Peter Mendygral, Lawrence Meadows, James Arnemann, Lei Shao, Siyu He, Tuomas Karna, Diana Moise, Simon J. Pennycook, Kristyn J. Maschhoff, Jason Sewall, Nalini Kumar, Shirley Ho, Michael F. Ringenburg, Prabhat, Victor W. Lee:
CosmoFlow: Using Deep Learning to Learn the Universe at Scale. CoRR abs/1808.04728 (2018) - [i17]Mahesh Balasubramanian, Trevor D. Ruiz, Brandon Cook, Sharmodeep Bhattacharyya, Prabhat, Aviral Shrivastava, Kristofer E. Bouchard:
Optimizing the Union of Intersections LASSO (UoILASSO) and Vector Autoregressive (UoIVAR) Algorithms for Improved Statistical Estimation at Scale. CoRR abs/1808.06992 (2018) - [i16]Nicholas Choma, Federico Monti, Lisa Gerhardt, Tomasz Palczewski, Zahra Ronaghi, Prabhat, Wahid Bhimji, Michael M. Bronstein, Spencer R. Klein, Joan Bruna:
Graph Neural Networks for IceCube Signal Classification. CoRR abs/1809.06166 (2018) - [i15]Thorsten Kurth, Sean Treichler, Joshua Romero, Mayur Mudigonda, Nathan Luehr, Everett H. Phillips, Ankur Mahesh, Michael A. Matheson, Jack Deslippe, Massimiliano Fatica, Prabhat, Michael Houston:
Exascale Deep Learning for Climate Analytics. CoRR abs/1810.01993 (2018) - 2017
- [i14]Haoyuan Xing, Sofoklis Floratos, Spyros Blanas, Suren Byna, Prabhat, Kesheng Wu, Paul Brown:
ArrayBridge: Interweaving declarative array processing with high-performance computing. CoRR abs/1702.08327 (2017) - [i13]Thorsten Kurth, Jian Zhang, Nadathur Satish, Ioannis Mitliagkas, Evan Racah, Md. Mostofa Ali Patwary, Tareq M. Malas, Narayanan Sundaram, Wahid Bhimji, Mikhail Smorkalov, Jack Deslippe, Mikhail Shiryaev, Srinivas Sridharan, Prabhat, Pradeep Dubey:
Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data. CoRR abs/1708.05256 (2017) - [i12]Brian Friesen, Md. Mostofa Ali Patwary, Brian Austin, Nadathur Satish, Zachary Slepian, Narayanan Sundaram, Deborah Bard, Daniel J. Eisenstein, Jack Deslippe, Pradeep Dubey, Prabhat:
Galactos: Computing the Anisotropic 3-Point Correlation Function for 2 Billion Galaxies. CoRR abs/1709.00086 (2017) - [i11]Eli Dart, Michael F. Wehner, Prabhat:
An Assessment of Data Transfer Performance for Large-Scale Climate Data Analysis and Recommendations for the Data Infrastructure for CMIP6. CoRR abs/1709.09575 (2017) - [i10]Wahid Bhimji, Steven Andrew Farrell, Thorsten Kurth, Michela Paganini, Prabhat, Evan Racah:
Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC. CoRR abs/1711.03573 (2017) - [i9]Mario Lezcano Casado, Atilim Gunes Baydin, David Martínez-Rubio, Tuan Anh Le, Frank D. Wood, Lukas Heinrich, Gilles Louppe, Kyle Cranmer, Karen Ng, Wahid Bhimji, Prabhat:
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators. CoRR abs/1712.07901 (2017) - [i8]Amrita Mathuriya, Thorsten Kurth, Vivek Rane, Mustafa Mustafa, Lei Shao, Debbie Bard, Prabhat, Victor W. Lee:
Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer. CoRR abs/1712.09388 (2017) - 2016
- [i7]Evan Racah, Seyoon Ko, Peter J. Sadowski, Wahid Bhimji, Craig Tull, Sang-Yun Oh, Pierre Baldi, Prabhat:
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks. CoRR abs/1601.07621 (2016) - [i6]Yunjie Liu, Evan Racah, Prabhat, Joaquin Correa, Amir Khosrowshahi, David Lavers, Kenneth Kunkel, Michael F. Wehner, William D. Collins:
Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets. CoRR abs/1605.01156 (2016) - [i5]Alex Gittens, Aditya Devarakonda, Evan Racah, Michael F. Ringenburg, Lisa Gerhardt, Jey Kottalam, Jialin Liu, Kristyn J. Maschhoff, Shane Canon, Jatin Chhugani, Pramod Sharma, Jiyan Yang, James Demmel, Jim Harrell, Venkat Krishnamurthy, Michael W. Mahoney, Prabhat:
Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies. CoRR abs/1607.01335 (2016) - [i4]Md. Mostofa Ali Patwary, Nadathur Rajagopalan Satish, Narayanan Sundaram, Jialin Liu, Peter J. Sadowski, Evan Racah, Surendra Byna, Craig Tull, Wahid Bhimji, Prabhat, Pradeep Dubey:
PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures. CoRR abs/1607.08220 (2016) - [i3]Jeffrey Regier, Kiran Pamnany, Ryan Giordano, Rollin C. Thomas, David Schlegel, Jon McAuliffe, Prabhat:
Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference. CoRR abs/1611.03404 (2016) - [i2]Evan Racah, Christopher Beckham, Tegan Maharaj, Prabhat, Christopher J. Pal:
Semi-Supervised Detection of Extreme Weather Events in Large Climate Datasets. CoRR abs/1612.02095 (2016) - 2013
- [i1]Christopher J. Paciorek, Benjamin Lipshitz, Wei Zhuo, Prabhat, Cari Kaufman, Rollin C. Thomas:
Parallelizing Gaussian Process Calculations in R. CoRR abs/1305.4886 (2013)
Coauthor Index
Surendra Byna
aka: Suren Byna
aka: Suren Byna
a service of 
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-04-25 05:33 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
