16 citations · 24 across the 3 of their papers we have counts for
4 papers
Reinforced Multi-Teacher Selection for Knowledge Distillation
Fei Yuan, Linjun Shou, Jian Pei +4
In natural language processing (NLP) tasks, slow inference speed and huge footprints in GPU usage remain the bottleneck of applying pre-trained deep models in production. As a popu…
Model Compression with Two-stage Multi-teacher Knowledge Distillation for Web Question Answering System
Ze Yang, Linjun Shou, Ming Gong +2
Deep pre-training and fine-tuning models (such as BERT and OpenAI GPT) have demonstrated excellent results in question answering areas. However, due to the sheer amount of model pa…
Model Compression with Multi-Task Knowledge Distillation for Web-scale Question Answering System
Ze Yang, Linjun Shou, Ming Gong +2
Deep pre-training and fine-tuning models (like BERT, OpenAI GPT) have demonstrated excellent results in question answering areas. However, due to the sheer amount of model paramete…
NeuronBlocks: Building Your NLP DNN Models Like Playing Lego
Ming Gong, Linjun Shou, Wutao Lin +5
Deep Neural Networks (DNN) have been widely employed in industry to address various Natural Language Processing (NLP) tasks. However, many engineers find it a big overhead when the…