most citedCERT: Contrastive Self-supervised Learning for Language Understanding

10 citations · 11 across the 4 of their papers we have counts for

collaborators

6 papers

cs.LG2021

A Unified Game-Theoretic Interpretation of Adversarial Robustness

Jie Ren, Die Zhang, Yisen Wang +8

This paper provides a unified view to explain different adversarial attacks and defense methods, \emph{i.e.} the view of multi-order interactions between input variables of DNNs. B…

cs.CL20211 cited

Self-supervised Regularization for Text Classification

Meng Zhou, Zechen Li, Pengtao Xie

Text classification is a widely studied problem and has broad applications. In many real-world problems, the number of texts for training classification models is limited, which re…

cs.LG2021

A Unified Game-Theoretic Interpretation of Adversarial Robustness

Jie Ren, Die Zhang, Yisen Wang +8

This paper provides a unified view to explain different adversarial attacks and defense methods, i.e. the view of multi-order interactions between input variables of DNNs. Based on…

cs.CL202010 cited

CERT: Contrastive Self-supervised Learning for Language Understanding

Hongchao Fang, Sicheng Wang, Meng Zhou +2

Pretrained language models such as BERT, GPT have shown great effectiveness in language understanding. The auxiliary predictive tasks in existing pretraining approaches are mostly…

cs.LG2020

MedDialog: Two Large-scale Medical Dialogue Datasets

Xuehai He, Shu Chen, Zeqian Ju +10

Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs. To fac…

cs.LG2020

End-To-End Graph-based Deep Semi-Supervised Learning

Zihao Wang, Enmei Tu, Zhou Meng

The quality of a graph is determined jointly by three key factors of the graph: nodes, edges and similarity measure (or edge weights), and is very crucial to the success of graph-b…