189 citations · 808 across the 51 of their papers we have counts for
19 papers · 1 filter
Model Uncertainty-Aware Knowledge Amalgamation for Pre-Trained Language Models
Lei Li, Yankai Lin, Xuancheng Ren +4
As many fine-tuned pre-trained language models~(PLMs) with promising performance are generously released, investigating better ways to reuse these models is vital as it can greatly…
Auto-Encoding Knowledge Graph for Unsupervised Medical Report Generation
Fenglin Liu, Chenyu You, Xian Wu +3
Medical report generation, which aims to automatically generate a long and coherent report of a given medical image, has been receiving growing research interests. Existing approac…
RAP: Robustness-Aware Perturbations for Defending against Backdoor Attacks on NLP Models
Wenkai Yang, Yankai Lin, Peng Li +2
Backdoor attacks, which maliciously control a well-trained model's outputs of the instances with specific triggers, are recently shown to be serious threats to the safety of reusin…
Topology-Imbalance Learning for Semi-Supervised Node Classification
Deli Chen, Yankai Lin, Guangxiang Zhao +4
The class imbalance problem, as an important issue in learning node representations, has drawn increasing attention from the community. Although the imbalance considered by existin…
Well-classified Examples are Underestimated in Classification with Deep Neural Networks
Guangxiang Zhao, Wenkai Yang, Xuancheng Ren +3
The conventional wisdom behind learning deep classification models is to focus on bad-classified examples and ignore well-classified examples that are far from the decision boundar…
Dynamic Knowledge Distillation for Pre-trained Language Models
Lei Li, Yankai Lin, Shuhuai Ren +3
Knowledge distillation~(KD) has been proved effective for compressing large-scale pre-trained language models. However, existing methods conduct KD statically, e.g., the student mo…