11 citations · 22 across the 9 of their papers we have counts for
9 papers
Unseen Cost of Space Computing: Quantifying LEO Battery Aging via Physics-Driven Modeling
Li Zeng, Jingyang Zhu, Zixin Wang +2
Low Earth Orbit (LEO) satellite constellations in the 6G era are evolving into intelligent in-orbit computational platforms, forming Space Computing Power Networks (SCPNs) to deliv…
Pretraining Large Language Models with NVFP4
NVIDIA, Felix Abecassis, Anjulie Agrusa +87
Large Language Models (LLMs) today are powerful problem solvers across many domains, and they continue to get stronger as they scale in model size, training set size, and training…
A Survey of Optimization Modeling Meets LLMs: Progress and Future Directions
Ziyang Xiao, Jingrong Xie, Lilin Xu +15
By virtue of its great utility in solving real-world problems, optimization modeling has been widely employed for optimal decision-making across various sectors, but it requires su…
Edge Large AI Models: Revolutionizing 6G Networks
Zixin Wang, Yuanming Shi, Yong Zhou +2
Large artificial intelligence models (LAMs) possess human-like abilities to solve a wide range of real-world problems, exemplifying the potential of experts in various domains and…
Satellite Edge Artificial Intelligence with Large Models: Architectures and Technologies
Yuanming Shi, Jingyang Zhu, Chunxiao Jiang +2
Driven by the growing demand for intelligent remote sensing applications, large artificial intelligence (AI) models pre-trained on large-scale unlabeled datasets and fine-tuned for…
Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networks
Yan Zhu, Jingyang Zhu, Ting Wang +3
Advancements in artificial intelligence (AI) and low-earth orbit (LEO) satellites have promoted the application of large remote sensing foundation models for various downstream tas…
Hierarchical Learning and Computing over Space-Ground Integrated Networks
Jingyang Zhu, Yuanming Shi, Yong Zhou +2
Space-ground integrated networks hold great promise for providing global connectivity, particularly in remote areas where large amounts of valuable data are generated by Internet o…
Satellite Federated Edge Learning: Architecture Design and Convergence Analysis
Yuanming Shi, Li Zeng, Jingyang Zhu +3
The proliferation of low-earth-orbit (LEO) satellite networks leads to the generation of vast volumes of remote sensing data which is traditionally transferred to the ground server…
Over-the-Air Federated Learning and Optimization
Jingyang Zhu, Yuanming Shi, Yong Zhou +3
Federated learning (FL), as an emerging distributed machine learning paradigm, allows a mass of edge devices to collaboratively train a global model while preserving privacy. In th…