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Wahid Bhimji
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2020 – today
- 2024
- [i16]Shashank Subramanian, Ermal Rrapaj, Peter Harrington, Smeet Chheda, Steven Farrell, Brian Austin, Samuel Williams, Nicholas J. Wright, Wahid Bhimji:
Comprehensive Performance Modeling and System Design Insights for Foundation Models. CoRR abs/2410.00273 (2024) - [i15]Wahid Bhimji, Paolo Calafiura, Ragansu Chakkappai, Yuan-Tang Chou, Sascha Diefenbacher, Jordan Dudley, Steven Farrell, Aishik Ghosh, Isabelle Guyon, Chris Harris, Shih-Chieh Hsu, Elham E Khoda, Rémy Lyscar, Alexandre Michon, Benjamin Nachman, Peter Nugent, Mathis Reymond, David Rousseau, Benjamin Sluijter, Benjamin Thorne, Ihsan Ullah, Yulei Zhang:
FAIR Universe HiggsML Uncertainty Challenge Competition. CoRR abs/2410.02867 (2024) - 2023
- [j2]Wahid Bhimji, Dale Carder, Eli Dart
, Javier M. Duarte
Snowmass 2021 Computational Frontier CompF4 Topical Group Report Storage and Processing Resource Access. Comput. Softw. Big Sci. 7(1): 5 (2023) - [c10]Shashank Subramanian, Peter Harrington, Kurt Keutzer, Wahid Bhimji, Dmitriy Morozov, Michael W. Mahoney, Amir Gholami:
Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior. NeurIPS 2023 - [c9]Nestor Demeure
High-level GPU code: a case study examining JAX and OpenMP. SC Workshops 2023: 1105-1113 - [i14]Shashank Subramanian, Peter Harrington, Kurt Keutzer, Wahid Bhimji, Dmitriy Morozov, Michael W. Mahoney, Amir Gholami:
Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior. CoRR abs/2306.00258 (2023) - 2022
- [i13]Ashesh Chattopadhyay
Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence. CoRR abs/2205.04601 (2022) - 2021
- [c8]Khaled Z. Ibrahim, Tan Nguyen, Hai Ah Nam, Wahid Bhimji
Architectural Requirements for Deep Learning Workloads in HPC Environments. PMBS 2021: 7-17 - [i12]Eli Dart, William E. Allcock, Wahid Bhimji, Tim Boerner, Ravinderjeet Cheema, Andrew Cherry, Brent Draney, Salman Habib, Damian Hazen, Jason Hill, Matt Kollross, Suzanne Parete-Koon, Daniel Pelfrey, Adrian Pope, Jeff Porter, David Wheeler:
The Petascale DTN Project: High Performance Data Transfer for HPC Facilities. CoRR abs/2105.12880 (2021) - 2020
- [j1]Frédéric Bapst
A Pattern Recognition Algorithm for Quantum Annealers. Comput. Softw. Big Sci. 4(1) (2020)
2010 – 2019
- 2019
- [c7]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 - [c6]Atilim Günes Baydin, Lei Shao, Wahid Bhimji
Etalumis: bringing probabilistic programming to scientific simulators at scale. SC 2019: 29:1-29:24 - [i11]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) - 2018
- [c5]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 - [c4]Steven Andrew Farrell, Aaron Vose, Oliver Evans, Matthew L. Henderson, Shreyas Cholia
Interactive Distributed Deep Learning with Jupyter Notebooks. ISC Workshops 2018: 678-687 - [i10]Kim Albertsson, Piero Altoe, Dustin Anderson, Michael Andrews, Juan Pedro Araque Espinosa, Adam Aurisano
Machine Learning in High Energy Physics Community White Paper. CoRR abs/1807.02876 (2018) - [i9]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) - [i8]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) - 2017
- [c3]Thorsten Kurth, Jian Zhang, Nadathur Satish, Evan Racah, Ioannis Mitliagkas, Md. Mostofa Ali Patwary, Tareq M. Malas, Narayanan Sundaram, Wahid Bhimji, Mikhail Smorkalov
Deep learning at 15PF: supervised and semi-supervised classification for scientific data. SC 2017: 7 - [i7]Mustafa Mustafa, Deborah Bard, Wahid Bhimji, Rami Al-Rfou, Zarija Lukic:
Creating Virtual Universes Using Generative Adversarial Networks. CoRR abs/1706.02390 (2017) - [i6]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) - [i5]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) - [i4]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) - 2016
- [c2]Evan Racah, Seyoon Ko
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks. ICMLA 2016: 892-897 - [c1]Md. Mostofa Ali Patwary, Nadathur Rajagopalan Satish, Narayanan Sundaram, Jialin Liu, Peter J. Sadowski
PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures. IPDPS 2016: 494-503 - [i3]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) - [i2]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) - 2011
- [i1]Samuel C. Skipsey, Wahid Bhimji, Mike Kenyon:
Establishing Applicability of SSDs to LHC Tier-2 Hardware Configuration. CoRR abs/1102.3114 (2011)
Coauthor Index
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