Tom Benson

Publication Activity (10 Years)

Years Active: 2019-2022
Publications (10 Years): 9

Top Topics

Generative Model

Convolutional Neural Networks

Training Samples

Eigenvalues And Eigenvectors

Top Venues

Publications

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)
You Xuan Thung, Tom Benson, Nikita Klimenko
Detecting languages in streetscapes using deep convolutional neural networks. Big Data (2022)
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)
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 (2019)
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)
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 (2019)
Nikoli Dryden, Naoya Maruyama, Tim Moon, Tom Benson, Marc Snir, Brian Van Essen
Channel and filter parallelism for large-scale CNN training. SC (2019)
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)
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)