11 citations · 27 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 6 cited
Deep Convolutional Neural Networks with Zero-Padding: Feature Extraction and Learning
Zhi Han, Baichen Liu, Shao-Bo Lin +1
This paper studies the performance of deep convolutional neural networks (DCNNs) with zero-padding in feature extraction and learning. After verifying the roles of zero-padding in…
cs.CL2022★ 11 cited
Structured Prompting: Scaling In-Context Learning to 1,000 Examples
Yaru Hao, Yutao Sun, Li Dong +3
Large language models have exhibited intriguing in-context learning capability, achieving promising zero- and few-shot performance without updating the parameters. However, convent…
cs.CL2022★ 10 cited
Prototypical Calibration for Few-shot Learning of Language Models
Zhixiong Han, Yaru Hao, Li Dong +2
In-context learning of GPT-like models has been recognized as fragile across different hand-crafted templates, and demonstration permutations. In this work, we propose prototypical…