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Tom Benson
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2020 – today
- 2022
- [c7]You Xuan Thung, Tom Benson, Nikita Klimenko:
Detecting languages in streetscapes using deep convolutional neural networks. IEEE Big Data 2022: 1711-1718 - [c6]Shehtab Zaman
, Tim Moon, Tom Benson, Sam Adé Jacobs, Kenneth Chiu, Brian Van Essen:
Parallelizing Graph Neural Networks via Matrix Compaction for Edge-Conditioned Networks. CCGRID 2022: 386-395 - 2021
- [c5]Arpan Jain, Tim Moon, Tom Benson, Hari Subramoni, Sam Adé Jacobs, Dhabaleswar K. Panda, Brian Van Essen:
SUPER: SUb-Graph Parallelism for TransformERs. IPDPS 2021: 629-638
2010 – 2019
- 2019
- [c4]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, Shusen Liu, Peer-Timo Bremer:
Parallelizing Training of Deep Generative Models on Massive Scientific Datasets. CLUSTER 2019: 1-10 - [c3]Nikoli Dryden, Naoya Maruyama, Tom Benson, Tim Moon, Marc Snir, Brian Van Essen:
Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism. IPDPS 2019: 210-220 - [c2]Nikoli Dryden, Naoya Maruyama, Tim Moon, Tom Benson, Marc Snir, Brian Van Essen:
Channel and filter parallelism for large-scale CNN training. SC 2019: 10:1-10:20 - [c1]Simone Mora, Amin Anjomshoaa
, Tom Benson, Fábio Duarte, Carlo Ratti:
Towards Large-scale Drive-by Sensing with Multi-purpose City Scanner Nodes. WF-IoT 2019: 743-748 - [i2]Nikoli Dryden, Naoya Maruyama, Tom Benson, Tim Moon, Marc Snir, Brian Van Essen:
Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism. CoRR abs/1903.06681 (2019) - [i1]Sam Ade Jacobs, Brian Van Essen, David Hysom, Jae-Seung Yeom, Tim Moon, Rushil Anirudh, Jayaraman J. Thiagarajan, Shusen Liu, Peer-Timo Bremer, Jim Gaffney, Tom Benson, Peter B. Robinson, J. Luc Peterson, Brian K. Spears:
Parallelizing Training of Deep Generative Models on Massive Scientific Datasets. CoRR abs/1910.02270 (2019)
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