Tim Moon

Publication Activity (10 Years)

Years Active: 2016-2022
Publications (10 Years): 11

Top Topics

Generative Model

Maximum Entropy Principle

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)
Sam Ade Jacobs, Tim Moon, Kevin McLoughlin, Derek Jones, David Hysom, Dong H. Ahn, John C. Gyllenhaal, Pythagoras Watson, Felice C. Lightstone, Jonathan E. Allen, Ian Karlin, Brian Van Essen
Enabling rapid COVID-19 small molecule drug design through scalable deep learning of generative models. Int. J. High Perform. Comput. Appl. 35 (5) (2021)
Zachariah Carmichael, Tim Moon, Sam Ade Jacobs
Learning Interpretable Models Through Multi-Objective Neural Architecture Search. CoRR (2021)
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)
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)
Nikoli Dryden, Tim Moon, Sam Ade Jacobs, Brian Van Essen
Communication Quantization for Data-Parallel Training of Deep Neural Networks. MLHPC@SC (2016)
Tim Moon, Jack Poulson
Accelerating eigenvector and pseudospectra computation using blocked multi-shift triangular solves. CoRR (2016)