3 citations · 5 across the 10 of their papers we have counts for
4 papers · 1 filter
Easz: An Agile Transformer-based Image Compression Framework for Resource-constrained IoTs
Yu Mao, Jingzong Li, Jun Wang +4
Neural image compression, necessary in various machine-to-machine communication scenarios, suffers from its heavy encode-decode structures and inflexibility in switching between di…
Lossless Compression of Large Language Model-Generated Text via Next-Token Prediction
Yu Mao, Holger Pirk, Chun Jason Xue
As large language models (LLMs) continue to be deployed and utilized across domains, the volume of LLM-generated data is growing rapidly. This trend highlights the increasing impor…
WISE: A Framework for Gigapixel Whole-Slide-Image Lossless Compression
Yu Mao, Jun Wang, Nan Guan +1
Whole-Slide Images (WSIs) have revolutionized medical analysis by presenting high-resolution images of the whole tissue slide. Despite avoiding the physical storage of the slides,…
When Compression Meets Model Compression: Memory-Efficient Double Compression for Large Language Models
Weilan Wang, Yu Mao, Dongdong Tang +3
Large language models (LLMs) exhibit excellent performance in various tasks. However, the memory requirements of LLMs present a great challenge when deploying on memory-limited dev…