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20232025
most citedComprehensive Exploration of Synthetic Data Generation: A Survey

18 citations · 39 across the 10 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

cs.DC2024★ 1 cited

Octopus: Experiences with a Hybrid Event-Driven Architecture for Distributed Scientific Computing

Haochen Pan, Ryan Chard, Sicheng Zhou +7

Scientific research increasingly relies on distributed computational resources, storage systems, networks, and instruments, ranging from HPC and cloud systems to edge devices. Even…

cs.DC2024

Object Proxy Patterns for Accelerating Distributed Applications

J. Gregory Pauloski, Valerie Hayot-Sasson, Logan Ward +4

Workflow and serverless frameworks have empowered new approaches to distributed application design by abstracting compute resources. However, their typically limited or one-size-fi…

cs.DC2024★ 1 cited

GreenFaaS: Maximizing Energy Efficiency of HPC Workloads with FaaS

Alok Kamatar, Valerie Hayot-Sasson, Yadu Babuji +6

Application energy efficiency can be improved by executing each application component on the compute element that consumes the least energy while also satisfying time constraints.…

cs.DL2024★ 11 cited

Oil & Water? Diffusion of AI Within and Across Scientific Fields

Eamon Duede, William Dolan, André Bauer +2

This study empirically investigates claims of the increasing ubiquity of artificial intelligence (AI) within roughly 80 million research publications across 20 diverse scientific f…

cs.LG2024★ 1 cited

Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision

Nathaniel Hudson, J. Gregory Pauloski, Matt Baughman +13

Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries. As the demand for more capable AI models continues to grow, we…

cs.LG2024★ 18 cited

Comprehensive Exploration of Synthetic Data Generation: A Survey

André Bauer, Simon Trapp, Michael Stenger +5

Recent years have witnessed a surge in the popularity of Machine Learning (ML), applied across diverse domains. However, progress is impeded by the scarcity of training data due to…