14 citations · 18 across the 5 of their papers we have counts for
5 papers
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation
Shu-Hao Zhang, Le-Tong Huang, Xiang-Sheng Deng +5
Quantization has emerged as a mainstream approach for deploying Large Language Models (LLMs) on resource-constrained devices, yet compressing precision below 4-bit typically causes…
TernaryCLIP: Efficiently Compressing Vision-Language Models with Ternary Weights and Distilled Knowledge
Shu-Hao Zhang, Wei-Cheng Tang, Chen Wu +5
Recent years have witnessed an increasing interest in image-text contrastive modeling, exemplified by models such as Contrastive Language-Image Pretraining (CLIP). In this paper, w…
Efficient Ternary Weight Embedding Model: Bridging Scalability and Performance
Jiayi Chen, Chen Wu, Shaoqun Zhang +3
Embedding models have become essential tools in both natural language processing and computer vision, enabling efficient semantic search, recommendation, clustering, and more. Howe…
AcademicGPT: Empowering Academic Research
Shufa Wei, Xiaolong Xu, Xianbiao Qi +21
Large Language Models (LLMs) have demonstrated exceptional capabilities across various natural language processing tasks. Yet, many of these advanced LLMs are tailored for broad, g…
KG-MTT-BERT: Knowledge Graph Enhanced BERT for Multi-Type Medical Text Classification
Yong He, Cheng Wang, Shun Zhang +3
Medical text learning has recently emerged as a promising area to improve healthcare due to the wide adoption of electronic health record (EHR) systems. The complexity of the medic…