597 citations · 2.3k across the 62 of their papers we have counts for
9 papers · 1 filter
GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training
Tianle Cai, Shengjie Luo, Keyulu Xu +3
Normalization is known to help the optimization of deep neural networks. Curiously, different architectures require specialized normalization methods. In this paper, we study what…
Variance-reduced Language Pretraining via a Mask Proposal Network
Liang Chen
Self-supervised learning, a.k.a., pretraining, is important in natural language processing. Most of the pretraining methods first randomly mask some positions in a sentence and the…
Taking Notes on the Fly Helps BERT Pre-training
Qiyu Wu, Chen Xing, Yatao Li +3
How to make unsupervised language pre-training more efficient and less resource-intensive is an important research direction in NLP. In this paper, we focus on improving the effici…
Transferred Discrepancy: Quantifying the Difference Between Representations
Yunzhen Feng, Runtian Zhai, Di He +2
Understanding what information neural networks capture is an essential problem in deep learning, and studying whether different models capture similar features is an initial step t…
MC-BERT: Efficient Language Pre-Training via a Meta Controller
Zhenhui Xu, Linyuan Gong, Guolin Ke +5
Pre-trained contextual representations (e.g., BERT) have become the foundation to achieve state-of-the-art results on many NLP tasks. However, large-scale pre-training is computati…
Rethinking Positional Encoding in Language Pre-training
Guolin Ke, Di He, Tie-Yan Liu
In this work, we investigate the positional encoding methods used in language pre-training (e.g., BERT) and identify several problems in the existing formulations. First, we show t…