118 citations · 199 across the 20 of their papers we have counts for
5 papers · 1 filter
Less is More: Task-aware Layer-wise Distillation for Language Model Compression
Chen Liang, Simiao Zuo, Qingru Zhang +3
Layer-wise distillation is a powerful tool to compress large models (i.e. teacher models) into small ones (i.e., student models). The student distills knowledge from the teacher by…
PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance
Qingru Zhang, Simiao Zuo, Chen Liang +4
Large Transformer-based models have exhibited superior performance in various natural language processing and computer vision tasks. However, these models contain enormous amounts…
MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation
Simiao Zuo, Qingru Zhang, Chen Liang +3
Pre-trained language models have demonstrated superior performance in various natural language processing tasks. However, these models usually contain hundreds of millions of param…
CAMERO: Consistency Regularized Ensemble of Perturbed Language Models with Weight Sharing
Chen Liang, Pengcheng He, Yelong Shen +2
Model ensemble is a popular approach to produce a low-variance and well-generalized model. However, it induces large memory and inference costs, which are often not affordable for…
No Parameters Left Behind: Sensitivity Guided Adaptive Learning Rate for Training Large Transformer Models
Chen Liang, Haoming Jiang, Simiao Zuo +5
Recent research has shown the existence of significant redundancy in large Transformer models. One can prune the redundant parameters without significantly sacrificing the generali…