3 citations · 6 across the 6 of their papers we have counts for
Showing 2024Show all
2 papers · 1 filter
gr-qc2024★ 1 cited
Machine learning-driven conservative-to-primitive conversion in hybrid piecewise polytropic and tabulated equations of state
Semih Kacmaz, Roland Haas, E. A. Huerta
We present a novel machine learning (ML) method to accelerate conservative-to-primitive inversion, focusing on hybrid piecewise polytropic and tabulated equations of state. Traditi…
cs.DC2024
Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources -- A Case Study on Federated Fine-tuning of LLaMA 2
Zilinghan Li, Shilan He, Pranshu Chaturvedi +4
Federated learning enables multiple data owners to collaboratively train robust machine learning models without transferring large or sensitive local datasets by only sharing the p…