132 citations · 167 across the 10 of their papers we have counts for
10 papers
STT: Soft Template Tuning for Few-Shot Adaptation
Ping Yu, Wei Wang, Chunyuan Li +3
Prompt tuning has been an extremely effective tool to adapt a pre-trained model to downstream tasks. However, standard prompt-based methods mainly consider the case of sufficient d…
Data Efficient 3D Learner via Knowledge Transferred from 2D Model
Ping-Chung Yu, Cheng Sun, Min Sun
Collecting and labeling the registered 3D point cloud is costly. As a result, 3D resources for training are typically limited in quantity compared to the 2D images counterpart. In…
Efficient Language Modeling with Sparse all-MLP
Ping Yu, Mikel Artetxe, Myle Ott +4
All-MLP architectures have attracted increasing interest as an alternative to attention-based models. In NLP, recent work like gMLP shows that all-MLPs can match Transformers in la…
SDA: Improving Text Generation with Self Data Augmentation
Ping Yu, Ruiyi Zhang, Yang Zhao +3
Data augmentation has been widely used to improve deep neural networks in many research fields, such as computer vision. However, less work has been done in the context of text, pa…
ReMP: Rectified Metric Propagation for Few-Shot Learning
Yang Zhao, Chunyuan Li, Ping Yu +1
Few-shot learning features the capability of generalizing from a few examples. In this paper, we first identify that a discriminative feature space, namely a rectified metric space…
Structure-Aware Human-Action Generation
Ping Yu, Yang Zhao, Chunyuan Li +2
Generating long-range skeleton-based human actions has been a challenging problem since small deviations of one frame can cause a malformed action sequence. Most existing methods b…