2 citations · 4 across the 11 of their papers we have counts for
5 papers · 1 filter
Understanding the Landscape of Ampere GPU Memory Errors
Zhu Zhu, Yu Sun, Dhatri Parakal +9
Graphics Processing Units (GPUs) have become a de facto solution for accelerating high-performance computing (HPC) applications. Understanding their memory error behavior is an ess…
FediLoRA: Practical Federated Fine-Tuning of Foundation Models Under Missing-Modality Constraints
Lishan Yang, Wei Emma Zhang, Nam Kha Nguygen +4
Federated Learning with LoRA fine-tuning offers an efficient and privacy-aware solution for institutions to collaboratively leverage their large datasets to train VLLMs. However, p…
FedDPG: An Adaptive Yet Efficient Prompt-tuning Approach in Federated Learning Settings
Ali Shakeri, Wei Emma Zhang, Amin Beheshti +3
Pre-trained Language Models (PLMs) have demonstrated impressive performance in various NLP tasks. However, traditional fine-tuning methods for leveraging PLMs for downstream tasks…
MMiC: Mitigating Modality Incompleteness in Clustered Federated Learning
Lishan Yang, Wei Emma Zhang, Quan Z. Sheng +3
In the era of big data, data mining has become indispensable for uncovering hidden patterns and insights from vast and complex datasets. The integration of multimodal data sources…
Safety Interventions against Adversarial Patches in an Open-Source Driver Assistance System
Cheng Chen, Grant Xiao, Daehyun Lee +4
Drivers are becoming increasingly reliant on advanced driver assistance systems (ADAS) as autonomous driving technology becomes more popular and developed with advanced safety feat…