30 citations · 38 across the 3 of their papers we have counts for
5 papers
ScaLA: Accelerating Adaptation of Pre-Trained Transformer-Based Language Models via Efficient Large-Batch Adversarial Noise
Minjia Zhang, Niranjan Uma Naresh, Yuxiong He
In recent years, large pre-trained Transformer-based language models have led to dramatic improvements in many natural language understanding tasks. To train these models with incr…
NxMTransformer: Semi-Structured Sparsification for Natural Language Understanding via ADMM
Connor Holmes, Minjia Zhang, Yuxiong He +1
Natural Language Processing (NLP) has recently achieved success by using huge pre-trained Transformer networks. However, these models often contain hundreds of millions or even bil…
Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping
Minjia Zhang, Yuxiong He
Recently, Transformer-based language models have demonstrated remarkable performance across many NLP domains. However, the unsupervised pre-training step of these models suffers fr…
Zoom: SSD-based Vector Search for Optimizing Accuracy, Latency and Memory
Minjia Zhang, Yuxiong He
With the advancement of machine learning and deep learning, vector search becomes instrumental to many information retrieval systems, to search and find best matches to user querie…
Navigating with Graph Representations for Fast and Scalable Decoding of Neural Language Models
Minjia Zhang, Xiaodong Liu, Wenhan Wang +2
Neural language models (NLMs) have recently gained a renewed interest by achieving state-of-the-art performance across many natural language processing (NLP) tasks. However, NLMs a…