3 citations · 3 across the 4 of their papers we have counts for
5 papers
Democratizing LLM Training with the Edge: Challenges, Systems, and the Road Ahead
Ziyue Luo, Jiaxuan Cai, Cedric LeDenmat +27
The growing computational power of modern GPU data centers has let a few industrial giants train and deploy large language models (LLMs) on massive amounts of data. Yet the sheer s…
BEACON: A Versatile Accelerator for Computational Pathology Applications
Sumanth Gudaparthi, Ananth Krishna Prasad, Lin Jia +2
While accelerators for AI have seen great commercial success, it is challenging to replicate that success for other specialized domains due to a number of factors. We make the case…
MedEqualizer: A Framework Investigating Bias in Synthetic Medical Data and Mitigation via Augmentation
Sama Salarian, Yue Zhang, Swati Padhee +1
Synthetic healthcare data generation presents a viable approach to enhance data accessibility and support research by overcoming limitations associated with real-world medical data…
HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs
Yue Zhang, Yuntian He, Saket Gurukar +1
Heterogeneous graphs are ubiquitous in real-world applications because they can represent various relationships between different types of entities. Therefore, learning embeddings…
Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain
Amin Karimi Monsefi, Payam Karisani, Mengxi Zhou +5
Standard modern machine-learning-based imaging methods have faced challenges in medical applications due to the high cost of dataset construction and, thereby, the limited labeled…