2 citations · 3 across the 4 of their papers we have counts for
4 papers
On the accuracy and efficiency of group-wise clipping in differentially private optimization
Zhiqi Bu, Ruixuan Liu, Yu-Xiang Wang +2
Recent advances have substantially improved the accuracy, memory cost, and training speed of differentially private (DP) deep learning, especially on large vision and language mode…
Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer
Qingru Zhang, Dhananjay Ram, Cole Hawkins +2
Pretrained transformer models have demonstrated remarkable performance across various natural language processing tasks. These models leverage the attention mechanism to capture lo…
Coupling public and private gradient provably helps optimization
Ruixuan Liu, Zhiqi Bu, Yu-xiang Wang +2
The success of large neural networks is crucially determined by the availability of data. It has been observed that training only on a small amount of public data, or privately on…
Better Context Makes Better Code Language Models: A Case Study on Function Call Argument Completion
Hengzhi Pei, Jinman Zhao, Leonard Lausen +2
Pretrained code language models have enabled great progress towards program synthesis. However, common approaches only consider in-file local context and thus miss information and…