28 citations · 68 across the 5 of their papers we have counts for
5 papers
Federated Few-Shot Learning for Mobile NLP
Dongqi Cai, Shangguang Wang, Yaozong Wu +2
Natural language processing (NLP) sees rich mobile applications. To support various language understanding tasks, a foundation NLP model is often fine-tuned in a federated, privacy…
Towards Practical Few-shot Federated NLP
Dongqi Cai, Yaozong Wu, Haitao Yuan +3
Transformer-based pre-trained models have emerged as the predominant solution for natural language processing (NLP). Fine-tuning such pre-trained models for downstream tasks often…
Efficient NLP Model Finetuning via Multistage Data Filtering
Xu Ouyang, Shahina Mohd Azam Ansari, Felix Xiaozhu Lin +1
As model finetuning is central to the modern NLP, we set to maximize its efficiency. Motivated by redundancy in training examples and the sheer sizes of pretrained models, we explo…
STI: Turbocharge NLP Inference at the Edge via Elastic Pipelining
Liwei Guo, Wonkyo Choe, Felix Xiaozhu Lin
Natural Language Processing (NLP) inference is seeing increasing adoption by mobile applications, where on-device inference is desirable for crucially preserving user data privacy…
Draining our Glass: An Energy and Heat Characterization of Google Glass
Robert LiKamWa, Zhen Wang, Aaron Carroll +2
The Google Glass is a mobile device designed to be worn as eyeglasses. This form factor enables new usage possibilities, such as hands-free video chats and instant web search. Howe…